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Agentic Large Language Models

Max van Duijn, Michiel van der Meer, Aske Plaat, Niki van Stein

arXiv:2610.13455v1cs.AI

TL;DR

As LLMs begin to act in the world and interact with one another, questions arise about how they work and what their use means for people. This book reviews agentic LLM techniques and applications, aiming to provide foundations for following a field that is still evolving.

  • Problem

    The book asks how LLMs might act directly in the world and interact with one another, and what that could mean for users whose decisions and actions are already influenced by LLM answers.

    Source: Introduction · p. 15

  • Method

    The book introduces and reviews agentic LLM techniques and applications, discussing major engineering concepts and providing references to scientific literature, code, and datasets.

    Source: §1.3 · p. 18

  • Results

    The book describes agentic LLMs as a new, evolving field and says understanding the basics is important for following its latest developments.

    Source: §1.3 · p. 18

  • Takeaways & Limitations

    Readers can use the book’s literature references, source-code links, and datasets to experiment with building their own agents.

    Source: §1.3 · p. 18

    The book assumes readers have an undergraduate-level understanding of computer programming, artificial intelligence, machine learning, and natural language processing.

    Source: §1.4 · p. 18

Abstract

from arXiv · show

Large language models (LLMs) have taken the world by storm. Their ability to communicate with us in our own language, and to reason about our world, has made them a favorite tool for writing, answering questions, and much more. Their answers already influence our decisions and our actions. What would happen if LLMs themselves could act in the world directly? What would happen if LLMs could interact with each other? Agentic LLMs combine the power of large language models with the ability to act in the real world. In this book we will discuss how agentic LLMs work, how they act, how they interact, and what this means for us, as users.

Introduction … Foundations of Language Modeling with LLMs

This section introduces agentic LLMs and traces the foundations and development of language technology, from approaches to representing and modeling language through attention-based transformers. It surveys their methods, training, uses, and persistent challenges while outlining the book’s focus on how agentic LLMs act and interact.

Introduction

  • Agentic LLMs combine language-model capabilities with real-world action, motivating a book that explains how the technology works and how it may shape society.The book aims to help students and researchers understand emerging applications, influence their direction, build safeguards, and develop the field.Source: Introduction · p. 15, §1.1 · p. 15

1.2 Concepts and Definitions

  • Agentic LLMs can reason, write code, browse, use tools, and interact with agents or the world; the book also uses “LLM” broadly for models processing other modalities.Agents act, observe the effects, and adapt their action policy from feedback; the book’s scope extends to visual information, code, and world models.Source: §1.2 · pp. 16–17, §1.2.3 · p. 16

1.2.1 Agents that Assist, Agents that Interact

  • The book distinguishes agentic LLMs that assist people in domains such as medicine, logistics, and finance from agents that interact with one another or simulate human behavior.It presents these as two major use-case classes: human assistance and interaction among agents to perform tasks or simulate behavior.Source: §1.2.1 · p. 16

1.2.2 Agentic Harness

  • An agentic LLM consists of an LLM and a harness that manages prompts, tools, state, and skills, and loops through reasoning, action, and observation without manual guidance.The harness can adapt the agent’s behavior policy from feedback using reinforcement learning.Source: §1.2.2 · p. 16

1.2.4 Training Data

  • Learning at inference time enables dynamic behavior and lets agents generate relevant training data through interaction, creating a recursive self-improvement loop.Examples include vision-language-action models learning from robotic action sequences and societies of agents learning through interaction.Source: §1.2.4 · p. 17

1.3 Audience and Aim

  • The book is aimed primarily at graduate students and AI or computer-science researchers, assumes undergraduate-level programming and AI foundations, but offers brief overviews for readers with knowledge gaps.It aims to review techniques and applications, explain core engineering concepts, and provide literature, code, and datasets for experimentation.Source: §1.3 · p. 18, §1.4 · p. 18

1.5 Course Structures

  • The concise but comprehensive book supports different course goals, including agentic assistants and social simulation, and combines theoretical background with practical examples and case studies.Its seven parts range from language modeling and reasoning to agents, tools, social simulation, advanced topics, and future directions; programs can select relevant parts.Source: §1.5 · p. 19, §1.6 · p. 20

Foundations of Language Modeling with LLMs

2.1 Processing Natural Language

  • NLP has expanded from the goal of practical machine translation to applications across industries, while contemporary chatbots build on earlier systems such as ELIZA.The passage describes research progress and industrial adoption as drivers of application-oriented development.Source: §2.1 · p. 30

2.1.1 Language Understanding and Language Generation

  • NLU maps language to structured meaning for interpretation, whereas NLG produces natural-sounding text, with most LLMs using abstractive generation.Language modeling predicts the next sequence element from context; generation may select and rearrange existing text (extractive) or create new sentences (abstractive).Source: §2.1.1 · pp. 31–32

2.1.2 Complexity of Natural Language

  • NLP is difficult because meaning depends on linguistic structure, word relationships, speaker intent and context, cross-sentence discourse, and language variation.Discourse understanding includes tracking entities across sentences, which supports tasks such as summarization, translation, and long-form dialogue.Source: §2.1.2 · pp. 33–34

2.1.3 Distributional Representations

  • Distributional representations infer word meaning from contextual company and encode similarity as nearby vectors in continuous space.Dense word embeddings can be learned by predicting neighboring words, but word-level vocabularies are impractical and struggle with rare or unseen words.Source: §2.1.3 · pp. 34–35

2.1.4 Tokenization

  • Tokenization must handle language variation, punctuation, and emojis; subword methods balance whole common words with decomposition of rare ones.Simple rule-based segmentation can fail on structural irregularities such as ambiguous abbreviations.Source: §2.1.4 · pp. 36–37

2.1.5 Early Methods for Language Representation

  • Language models assign probabilities to word sequences to distinguish fluent sentences, but exact joint-probability estimation is intractable and requires chain-rule decomposition and assumptions.Early methods estimated probabilities from corpus frequencies using maximum likelihood estimation.Source: §2.1.5 · pp. 37–38
  • N-gram models estimate next-word probabilities from the preceding n−1 words, trading richer context at larger n for increasing sparsity and zero-probability sequences.Laplace smoothing adds a fictitious count of 1 to each vocabulary item to mitigate zero probabilities.Source: §2.1.5 · p. 38, §2.2 · p. 39

2.2.1 Supervised Learning

  • Supervised learning trains a model on labeled input-output pairs by minimizing prediction loss, then tests whether it generalizes; high training but low test accuracy indicates overfitting.Low accuracy on both training and test data indicates underfitting, often due to insufficient training or a model too simple for the data.Source: §2.2.1 · pp. 39–40

2.2.2 Autoregressive Learning

  • Autoregressive learning is self-supervised: each token serves as the label for preceding tokens, training a language model to predict the next token from text.It requires no separately labeled example pairs and is used in contemporary model-training pipelines.Source: §2.2.2 · p. 40

2.2.3 Reinforcement Learning

  • Reinforcement learning learns a policy through online state-action-reward interaction, using exploration to avoid local optima and supporting agentic LLM training and environment modeling.Problems are framed as Markov decision processes; tabular methods suit small state spaces, while larger ones use function approximation such as deep Q-learning or PPO.Source: §2.2.3 · pp. 41–42

2.3 Modeling Language

  • Symbolic methods are interpretable but brittle and hard to scale, statistical methods handle ambiguity but capture local patterns, and neural methods model global context at the cost of interpretability and resources.NLP’s broad shifts were from symbolic systems to statistical methods and then neural networks, with contemporary training pipelines drawing on all three paradigms.Source: §2.3 · p. 43

2.3.1 Embeddings

  • Word2vec learns dense embeddings from context: skip-gram predicts surrounding words and captures rare-word detail, while CBOW predicts a target from averaged context and trains faster on frequent-word syntax.Word2vec uses a learned embedding matrix to represent words in a lower-dimensional space than count-based vectors.Source: §2.3.1 · p. 44

2.3.2 Shifting to Contextualized Embeddings

  • Static embeddings give a polysemous word one context-independent vector, whereas contextualized models compute its representation from surrounding tokens to capture its intended sense.For example, the representation of “bank” can reflect financial context or a river context, depending on the sequence.Source: §2.3.2 · pp. 44–45

2.3.3 Recurrent Neural Networks

  • RNNs incorporate prior hidden states to process sequences, but recurrent gradient propagation can vanish or explode; LSTM gates control information flow to address this problem.Machine translation is a sequence-to-sequence task: it maps an input sentence to an output sentence whose length and word order may differ.Source: §2.3.3 · pp. 45–47
  • Encoder-decoder RNNs map variable-length inputs to outputs through a fixed-length context vector, creating an information bottleneck that makes long sequences difficult to retain.The decoder generates tokens one at a time from the context vector until an end-of-sequence token.Source: §2.3.3 · pp. 47–48

2.3.4 Attention

  • Attention replaces a single encoder final-state context with a weighted combination of encoder hidden states, letting the decoder focus dynamically on input tokens relevant to each generated word.Self-attention removes recurrence to process tokens in parallel, and multiple heads capture different token relationships in parallel.Source: §2.3.4 · pp. 48–50
  • RNNsearch [Bahdanau et al., 2015, Kim et al., 2017] sustains high BLEU translation quality for long sentences, while standard RNNenc performance drops sharply as sentence length increases.Figure 2.9 plots BLEU scores by sentence length; higher BLEU is better.Source: §2.3.4 · p. 48, Figure 2.9 · p. 49

2.3.5 Transformers

  • Transformers use stacked blocks with self-attention, feed-forward networks, normalization, and residual connections; attention enables parallel processing and fine-grained long-range semantic connections.The original architecture has encoder and decoder stacks, while most LLMs are decoder-only.Source: §2.3.5 · pp. 50–52

2.3.6 Encoder-only (BERT)

  • Encoder-only models use bidirectional masked language modeling to learn contextual representations, making them suited to NLU and transfer learning through pretraining followed by task-specific fine-tuning.Roughly 15% of input tokens are replaced with [MASK]; pretraining is costly, while fine-tuning may require only a few thousand examples and take hours or minutes.Source: §2.3.6 · pp. 52–53

2.3.7 Decoder-only (GPT)

  • Decoder-only models use causal next-token training and can generate from prompts through in-context learning without weight updates or expensive task-specific fine-tuning.A look-ahead mask prevents access to future tokens; prompted examples provide context for generating an answer [Brown et al., 2020].Source: §2.3.7 · pp. 53–54

2.4.1 Open challenges

  • Scaling models requires more parameters, data, and compute, raising cost and environmental impact, while the balance between model size and generalization remains unresolved; training data scarcity can also degrade performance and reinforce bias.Low-resource languages lack the digital footprint available for English, and models can inherit Western cultural and linguistic biases from training data.Source: §2.4.1 · pp. 54–55
  • Open challenges include language and modality coverage, model interpretability, and broader socio-technical concerns.Morphological richness and non-Latin scripts complicate multilingual modeling; evolving language creates out-of-vocabulary and continual-learning problems, while representing image, audio, and video alongside text requires complex architectures. Models can hallucinate convincingly, and transparency is a barrier in high-stakes settings; ethical alignment and long-form reasoning remain challenges.Source: §2.4.1 · pp. 55–56

The Large Language Model Training Pipeline

This section covers the LLM pipeline from data acquisition and pretraining through posttraining and inference, explaining how its stages build language capabilities, adapt behavior, and support user interactions. It also describes how performance is measured and benchmarked, noting strengths on broad knowledge tasks and continuing challenges in complex reasoning and autonomous work.

3.1 Training Models

  • A typical pipeline initializes a model, uses autoregressive pretraining to create a general base model, then posttrains it on additional data for task-specific behavior.Encoder models are suited to understanding tasks, while decoder models excel at generative tasks.Source: §3.1 · p. 63, §3.1.1 · p. 64

3.1.3 Open and Closed Models

  • Commercial developers seldom disclose every part of their data, architecture, and training procedures, whereas fully open models make the training pipeline public.Transformer-based models may still differ in component layout or hardware-specific optimizations.Source: §3.1.3 · pp. 64–65

3.2.1 Acquire Data

  • Pretraining data comes from massive, often web-scale corpora refined for quality and safety; curation includes source weighting, noise removal, deduplication, and filtering that can inadvertently exclude dialects.Data diversity also matters: The Pile is heterogeneous, and its Bits Per Byte performance is presented as an indicator of world knowledge and complex reasoning [Gao et al., 2020].Source: §3.2.1 · pp. 65–66

3.2.2 Pretrain

  • Self-supervised pretraining predicts tokens from context to learn language and world knowledge, uses teacher forcing, and employs curricula and training controls to improve convergence and stability [Vaswani et al., 2017; Bengio et al., 2009; Takase et al., 2025].Curricula can shift from general web text toward harder math and code data; stability methods include weight decay, layer normalization, learning-rate schedulers, and gradient monitoring.Source: §3.2.2 · pp. 66–68

3.2.3 Finetune

  • Supervised finetuning adapts a pretrained model using smaller labeled datasets, supporting instruction-following, reasoning, tool use, and specialized behavior; it can turn generalist models into efficient task experts [Hu et al., 2021; Wei et al., 2022; Minaee et al., 2024].Low-rank optimization updates a small subset of parameters, and finetuning can establish a persona or deepen domain expertise.Source: §3.2.3 · p. 69

3.2.4 Preference Tuning

  • Preference tuning aligns a finetuned model with user expectations and aims to improve social acceptability, reduce hallucinations, and strengthen instruction adherence.RLHF learns a reward model, DPO optimizes directly from preference data, and RLAIF uses AI-generated preference signals; value alignment can impose an alignment tax on other capabilities [Ouyang et al., 2022; Rafailov et al., 2024; Bai et al., 2022; Lin et al., 2024].Source: §3.2.4 · pp. 70–71

3.2.5 Infer

  • At inference, users prompt the trained model, and wording can affect its response; in-context learning provides examples without changing model parameters and is a major driver of agentic LLMs [Sahoo et al., 2024; Brown et al., 2020; Wei et al., 2022; Dong et al., 2022].Greater inference-time reasoning effort costs more compute and may yield a better answer.Source: §3.2.5 · pp. 71–72, §3.2.6 · p. 72

3.3 Benchmarks, and Performance

  • LLM evaluation spans natural-language understanding, translation, question answering, and poetry composition, while rapid field development leaves capabilities across domains and contexts an open question [Wang et al., 2018, 2019; Rajpurkar et al., 2016; Narayan et al., 2018; Kocmi et al., 2022; Papineni et al., 2002; Sennrich et al., 2015; Roberts et al., 2020; Zhang and Eger, 2024].The described pipeline is one typical approach; modern training is elaborate and costly, and pipeline innovations remain an active research topic [Guo et al., 2025; Du et al., 2025].Source: §3.3 · p. 72, §3.2.6 · p. 72

3.3.1 Common tasks

  • LLM tasks broadly fall into natural language understanding and generation, with task formulation and data shaping which performance metric is appropriate.Task performance usually means accuracy, while the number and speed of model calls also matter for contemporary benchmarks.Source: §3.3.1 · p. 73

3.3.2 Metrics

  • Pretraining is commonly evaluated with perplexity, where lower is better, but byte-normalized BPB enables fairer comparisons across tokenizers.Perplexity reflects uncertainty in next-token prediction and is tokenizer-specific; BPB measures bits needed to encode a byte.Source: §3.3.2 · p. 73

3.3.3 Common Benchmarks

  • Benchmarks act as semi-standardized evaluation datasets, but contamination risks prompt private test sets and refreshed samples to preserve evaluation of generalization to unseen data.Public datasets support quick experimentation, while vendors may be restricted from private benchmark datasets.Source: §3.3.3 · p. 74

3.4 Concluding Remarks

  • Commercial LLM tech reports provide useful capability evidence, but often lack peer review and omit details needed for full reproducibility for commercial reasons.The omitted details can include the data recipe and model architecture.Source: §3.4 · p. 74
  • Modern LLMs show a benchmark hierarchy: static knowledge exams are near-saturated, while complex reasoning and autonomous agent tasks remain harder, with SWE-bench a reliability stress test.Frontier models score above 85–90% on static knowledge and professional exam benchmarks; top coding agents score 40–50% on verified SWE-bench subsets, up from negligible (<2%). Math and science pass rates have advanced from low percentages to expert levels.Source: §3.4 · pp. 74–75
  • The origins of LLM capabilities remain open questions, including the relative importance of pretraining and posttraining and how reasoning traces are elicited.The text also notes uncertainty about whether posttraining mainly provides a useful task interface for knowledge acquired during pretraining [Zhou et al., 2023].Source: §3.4 · pp. 75–76

Scaling Laws and Efficient Large Language Models

This section examines how compute, data, and model size shape language-model loss and how budgets, architectural choices, data curation, and inference-time compute affect efficiency. It also covers limits and trade-offs in scaling, environmental and privacy concerns at deployment scale, and a shift toward efficient scaling and capable smaller models.

4.1.1 Kaplan Scaling Laws

  • Kaplan estimated optimal scaling of N ∝ C^0.73 and D ∝ C^0.271; larger models also reach a target loss with fewer training tokens and optimization steps.The allocation favors model size once the compute cost of data is considered, despite data scaling having the larger loss exponent.Source: §4.1.1 · p. 85, Figure 4.2 · p. 85

4.1.2 Chinchilla Scaling Laws

  • Chinchilla found most large models were undertrained and recommends scaling model size and training tokens roughly equally, at about 20 tokens per parameter for compute-optimal training.The 20:1 rule minimizes training loss at a fixed training budget; inference-heavy deployment can justify deliberate overtraining, as with LLaMA-3 8B at roughly 1,900 tokens per parameter.Source: §4.1.2 · pp. 86–87

4.1.3 Compute-Optimal Curve

  • IsoFLOP experiments find a U-shaped loss curve at each fixed-FLOP budget, whose minimum identifies optimal model size and matching token count.Parabola fitting can bias the estimate and waste about $1.4M at Llama-3 scale, so direct parametric loss fitting is recommended. Analytic random-feature results identify four complexity phases with distinct compute-optimal exponents, while finetuning benefits more from scaling the base model than adding data or PEFT parameters.Source: §4.1.3 · pp. 87–89, Figure 4.3 · p. 88

4.1.4 Emergent Abilities

  • Apparent emergent abilities can look sudden on discrete metrics such as accuracy, while underlying cross-entropy loss or token probabilities scale smoothly.Few-shot task performance may remain near random until a threshold and then rise well above random, but the abruptness is contested as a metric effect.Source: §4.1.4 · p. 89, Figure 4.5 · p. 90

4.1.5 Inverse Scaling

  • Inverse scaling occurs when larger models perform worse, for example when the language-modeling objective favors a distractor over the correct answer.On hindsight neglect and repetitive algebra, models can fall below random performance at around 10^22 training FLOPs.Source: §4.1.5 · p. 90, Figure 4.6 · p. 91

4.1.6 Grokking

  • Grokking occurs when continued training of an overfit, oversized model on a small dataset eventually produces a sudden generalizing solution.This suggests overtraining can shift models from memorization to generalization without more data, relevant as high-quality human text becomes scarce; proposed alternatives include architectural efficiency, inference-time compute, and synthetic data.Source: §4.1.6 · pp. 91–92

4.2 Small Language Models

  • Small language models aim to retain high performance while running on consumer, mobile, or edge hardware, using model-centric or data-centric efficiency techniques.These techniques target throughput, latency, memory use, and sample efficiency.Source: §4.2 · p. 92

4.2.1.1 Reducing Pretraining Cost

  • LoRA freezes pretrained weights and trains low-rank adapters with no added inference latency; GQA shares key/value heads in groups to trade memory use against quality between MQA and MHA.LoRA reduces trainable parameters from d × k to r(d + k), and its adapters can be merged into the base model after training.Source: §4.2.1.1 · pp. 92–94
  • MoE routes each token to a small number of experts, increasing total model capacity without a proportional increase in per-token computation, while distillation trains smaller students from teachers’ soft output probabilities.MoE still requires memory for all experts, load balancing, and additional routing engineering; distillation’s soft targets convey plausible alternatives beyond the single correct answer.Source: §4.2.1.1 · p. 94

4.2.1.2 Reducing Inference Cost

  • Inference efficiency techniques reduce recomputation, model size, or memory use, with trade-offs in cache capacity, hardware support, precision, and output quality.KV caching avoids recomputing past keys and values but grows with context and batch size; pruning, quantization, mixed precision, and speculative decoding offer other approaches, with speculative decoding reaching up to 2-3 fold speedup without sacrificing target-model quality.Source: §4.2.1.2 · pp. 95–96

4.2.2.2 Synthetic Data Gathering

  • Training on clean synthetic textbook-quality data enabled a 1.3B-parameter model to outperform models ten times its size.Pipelines use frontier models to generate examples, judge-based filtering, and decontamination; step-by-step reasoning trajectories can transfer problem-solving skills to smaller models [Muennighoff et al., 2025, Arora and Zanette, 2026].Source: §4.2.2.2 · p. 96

4.2.2.3 Sample-Efficient Learning

  • The BabyLM Challenge studies sample-efficient learning by constraining training data to 10M to 100M tokens, a volume intended to mirror early language input for a child.The challenge encourages sample-efficient architectures and provides a setting to study cognitively plausible language acquisition.Source: §4.2.2.3 · p. 97

4.2.3 Popular models

  • Phi models use curated web and synthetic data to perform well at small sizes, while Mixtral popularized sparse MoE models with many total parameters but few active during inference.The Phi-3-mini has 3.8 billion parameters and is described as rivaling the reasoning capabilities of GPT-3.5 and Mixtral.Source: §4.2.3 · pp. 97–98

4.3 Inference-Time Compute

  • More inference-time tokens can improve capabilities, and optimally allocated test-time compute can let smaller models outperform larger ones on difficult reasoning tasks, depending on the task and strategy.Inference-time scaling spends compute on longer deliberation, measured by generated tokens or explored reasoning paths; faster token throughput allows more reasoning within a given time.Source: §4.3 · p. 99

4.4 Sustainable Language Models

  • Large-scale inference carries environmental and economic costs, while local deployment can protect privacy and reduce cloud reliance but is constrained by device memory, bandwidth, power, and heat.Small language models can reduce per-token memory bandwidth and compute; local inference keeps prompts on-device, and quantization, speculative decoding, and low-power NPUs can help make it feasible.Source: §4.4 · p. 100

Case Study 1: Building LLMs from Scratch

This case study uses nanochat to guide learners through building, training, evaluating, and deploying a ChatGPT-style model, from tokenizer training to a chat assistant. It connects hands-on work with examining training choices, model behavior, and practical and societal trade-offs.

5.1 Building a Chatbot

  • nanochat provides a minimal, mostly Python full-stack implementation covering BPE tokenization, web-text pretraining, supervised fine-tuning, an inference server, and a chat UI.The case study uses it for hands-on experience from input text through deployment, connecting theoretical concepts to debuggable code.Source: §5.1 · pp. 111–112, §5.2 · p. 112
  • A GPT-2-capable LLM that cost ∼$43,000 to train in 2019 [Radford et al., 2019] is presented as achievable for $48, though the required 8×H100 compute may be beyond many student projects.The case study scales down the experiment for a course assignment while keeping training updates and design decisions visible.Source: §5.1 · p. 112

5.2 Background: Karpathy’s nanochat

  • nanochat configures a compute-optimal model through one --depth setting, deriving other hyperparameters; pretraining uses CLIMBMix [Diao et al., 2026], followed by SFT on instruction, reasoning, knowledge, and synthetic spelling data.The described depth-24 model has ∼1.4B parameters and reaches validation loss 2.41 (0.717 bits-per-byte) after training on approximately 6 billion tokens; SFT uses approximately 1.1 million examples and computes loss only on assistant tokens, as in InstructGPT [Ouyang et al., 2022].Source: §5.2 · p. 112, §5.3 · p. 113

5.3 Experiment Preparation

  • Preparation tasks have learners inspect open-model training and architecture information on the Hugging Face Hub and run nanochat pretraining at depth 4, or depth 2 if needed, within an approximately 8-hour run limit.The Hub exercise asks them to record training scale, architecture, and data sources, noting where each item was found.Source: §5.3 · p. 113, §5.4 · p. 114

5.5.1 What to Do

  • The tokenizer task trains BPE models on a 500-million-character CLIMBMix sample with vocabulary sizes of 8,192 and 32,768, then examines compression, sequence length, downstream trade-offs, and failure cases.Learners also explain BPE and illustrate its merges with a tree and counts.Source: §5.5.1 · p. 114, §5.5.2 · p. 114, §5.6 · p. 115

5.6.2 Deliverables

  • Pretraining tasks ask learners to log training and validation loss and bits-per-byte, describe depth-derived model settings, compare the training horizon with Chinchilla scaling laws [Hoffmann et al., 2022], and inspect raw completions.They also analyze learning-curve shape, train–validation gaps, and the model’s capabilities before instruction tuning.Source: §5.6.1 · p. 115, §5.6.2 · pp. 115–116

5.7.1 What to Do

  • The staged-training task separates MMLU and GSM-8K mid-training from SmolTalk SFT, then evaluates ARC-Easy, ARC-Challenge, and GSM-8K after each stage.Learners compare all three stages, explain assistant-token loss masking and dataset choices, and interpret benchmark changes.Source: §5.7.1 · p. 116, §5.8 · p. 117

5.8.1 Part A: Inference and Deployment

  • Deployment and reflection tasks cover autoregressive sampling, KV caching, temperature, production hardening, comparisons with published benchmarks, LoRA/QLoRA, access, dataset values, and environmental impact.Learners interact with their model and consider whether affordable training is accessible to everyone and whether low cost means low impact.Source: §5.8.1 · pp. 117–118, §5.8.2 · p. 118

5.10 Concluding Remarks

  • nanochat guides learners through tokenizer training, transformer training on web text, instruction adaptation, benchmark evaluation, and deployment as a chat assistant.The chapter presents it as a window into the complete LLM training pipeline.Source: §5.10 · p. 119
  • Although the depth-4 model will fall short of frontier models, increasing depth and data should yield more capable models, and Assignment 1 provides experience training a full LLM.The experiments are described as scalable in theory, in line with scaling laws.Source: §5.10 · p. 119

Reasoning · Multi-step Reasoning in LLMs

This section examines methods for prompting, supporting, checking, and training multi-step reasoning in LLMs, including external algorithms, memory and state management, and reinforcement learning with verifiable rewards. It also discusses these methods’ limits and clarifies that step-by-step inference does not demonstrate human-like thought.

6.1.1 In-Context Learning

  • In-context learning uses prompt examples at inference time to elicit stored knowledge without changing model parameters, making few-shot learning cheap but prompt-sensitive.Because parameters remain unchanged and examples must fit in the context window, learned information is lost between calls and prompt formulation can be brittle.Source: §6.1.1 · pp. 126–127

6.1.2 Multi-step Reasoning Benchmarks

  • GSM8K contains 8,500 human-written math problems; GPT-3-175B achieved around 15% accuracy, and benchmark results can be difficult to compare across differing experiments.Other benchmarks cover diverse math problems, advanced mathematics, coding, robotics, puzzles, games, and webpage navigation.Source: §6.1.2 · pp. 127–128

6.1.3 Chain of Thought Prompting

  • On GSM8K, chain-of-thought prompting raised accuracy from 16% to 47% by eliciting intermediate reasoning steps (Wei et al. [2022b]).Few-shot prompts demonstrate reasoning steps rather than only final answers; a task-independent “Let’s think step by step” prompt is a zero-shot variant.Source: §6.1.3 · pp. 129–130

6.1.3.1 Self Evaluation

  • Self-consistency typically improves chain-of-thought performance by a further 10–20 percentage points by sampling reasoning paths and selecting the majority answer (Wang et al. [2022]).It replaces greedy single-path decoding with voting across diverse paths and is often combined with chain of thought.Source: §6.1.3.1 · p. 130

6.1.3.2 Code Generation for Validation

  • Program-aided language reported 72% on GSM8K and 93% on Penguins, using Python execution to evaluate reasoning steps (Gao et al. [2023]).The interpreter handles evaluation, leaving the LLM to translate the problem into executable code steps.Source: §6.1.3.2 · p. 132

6.1.3.3 Evolving code generation prompts

  • LLaMEA uses LLM-generated algorithm structures refined through mutation and selection, with reported BBOB accuracy improvements of 11 percentage points (van Stein and Bäck, 2024).FunSearch also iteratively evaluates candidate programs and prompts the LLM with sampled solutions ranked by their scores (Romera-Paredes et al. [2024]).Source: §6.1.3.3 · p. 133

6.2.2 Retrieval Augmentation and Long Context Windows

  • LLMs do not retain state between calls, and limited context windows restrict query information; retrieval, longer contexts, and compression are approaches to extend access to information.RAG retrieves external data; longer standard-transformer contexts require quadratic compute in window size, while semantic compression, chunking, and recursive methods are alternatives.Source: §6.2.1 · p. 133, §6.2.2 · p. 134

6.2.3 LLMs and Stateful Problems

  • LLMs perform poorly on problems requiring accurate state management, sometimes giving plausible answers that do not match the actual board state (Wong et al. [2025]; Paglieri et al. [2025], Ruoss et al. [2024]).Source: §6.2.3 · p. 134
  • LLMs exhibit a knowing-doing gap: they may describe an algorithm correctly yet fail to execute it, and can lose or mix up variable values between state changes.Prompt instructions to track values are cumbersome and error-prone; an external interpreter can execute generated code correctly and more efficiently than an LLM.Source: §6.2.3 · p. 135

6.2.4.1 Search-based Prompt Optimization

  • An agentic harness wraps an LLM with external algorithms that manage state and prompts, generate alternative reasoning steps, evaluate them, and support backtracking.Tree of Thoughts uses breadth-first or depth-first search with LLM prompts for generation and evaluation, enabling look-ahead and systematic exploration.Source: §6.2.4 · pp. 135–136, §6.2.4.1 · p. 136
  • Tree of Thoughts [Yao et al., 2024] achieves 74% accuracy on Game of 24; its other evaluated tasks are assessed qualitatively.The approach is compared with linear chain of thought and self-consistency on Game of 24, creative writing, and Mini crossword.Source: §6.2.4.1 · p. 138

6.2.4.2 Self-Reflection

  • Reflexion reports 10–20 percentage-point improvements [Shinn et al., 2024], while ReAct [Yao et al., 2022b] reports absolute success-rate gains of 34% and 10%, respectively.Reflexion uses actor, evaluator, and reflector prompts with external short- and long-term memory; Self Refine iteratively generates, critiques, and refines answers and reportedly improves over chain of thought.Source: §6.2.4.2 · pp. 139–140
  • Buffer of Thoughts [Yang et al., 2024] outperforms other methods by 11% on Game of 24 and 51% on checkmating.It stores high-level thought templates in an actively managed meta-buffer.Source: §6.2.4.2 · p. 140

6.2.4.3 Reinforcement Learning with Verifiable Rewards

  • RLVR replaces RLHF’s reward model with a deterministic verification function and uses the resulting rewards to train a policy with PPO [Schulman et al., 2017].DeepSeek-R1’s GRPO [Shao et al., 2024] uses group-based scoring; reasoning behaviors such as reflection and exploring alternatives can emerge during finetuning without external self-reflection loops.Source: §6.2.4.3 · pp. 141–142

6.3.1 Brittleness

  • Agentic methods are brittle: small changes in prompts, hyperparameters, random seeds, memory management, or the LLM can substantially affect performance.The text recommends multiple runs across seeds, models, and hyperparameter settings to assess how a solution behaves under different circumstances.Source: §6.3.1 · p. 143

6.3.2 Thinking, Fast and Slow

  • The chapter frames step-by-step inference as deliberative System 2 processing, but emphasizes that LLMs perform next-token prediction rather than human-like reasoning.Most methods are in-context; agentic harnesses manage reasoning and memory, while methods such as RLVR also use finetuning to train decision-making beyond pure next-token prediction.Source: §6.3.2 · p. 143, §6.3.3 · p. 144

Vision, Action and World Models

This section moves from vision-language systems and robotic agents that act on visual and textual input to world models that represent physical environments, their dynamics, and action consequences. It presents approaches to simulating futures for planning, situates world models in model-based reinforcement learning, and discusses stable learning while agents act in the world.

7.1.1 Tasks

  • VLM tasks include visual question answering, image captioning, and image generation, with benchmarks spanning specialized tasks and broad multimodal understanding.VQA tests understanding of visual images; captioning generates textual image descriptions. Examples of benchmarks include MOSAIC, GLOBAL-RG, CultureVQA, FoodieQA, MaRVL, MS-COCO, and MMMU.Source: §7.1.1 · pp. 152–153

7.1.2 Architecture

  • VLMs combine a vision encoder, text encoder, embedding projector, and text decoder; vision transformers can process images, despite vision’s grid structure and language’s sequential structure.The vision encoder may be a CNN or specially tokenized vision transformer, and the projector aligns visual and textual representations.Source: §7.1.2 · pp. 153–154

7.1.3 Learning Algorithm

  • VLMs use contrastive pairing (CLIP [Radford et al., 2021]), masking (FLAVA [Singh et al., 2022]), diffusion-based image generation, or pretrained language and vision encoders joined by a projector (LLaVA [Liu et al., 2023]).Contrastive learning pulls positive pairs together and separates negative pairs; masking trains reconstruction of obscured input.Source: §7.1.3 · pp. 154–155

7.2.1 Tasks

  • VLAs must generalize across changing objects, poses, lighting, and task combinations; long-horizon navigation in complex environments is especially challenging, and extra demonstrations may be infeasible.They must connect language to visual objects and robotic actions to perform real-world instructions.Source: §7.2.1 · pp. 158, 160

7.2.2 Architecture

  • VLA designs include hierarchical map-based Mobility VLA [Chiang et al., 2024], no-finetuning LM-Nav [Shah et al., 2023], action-token RT-2 [Brohan et al., 2023], marked-object Magma [Yang et al., 2025], and flow-matching π0 [Black et al., 2024].LM-Nav combines a language model, vision-language model, and visual-navigation model; π0 adds an action expert and can be finetuned for complex multistage tasks.Source: §7.2.2 · pp. 160, 161, 163

7.2.3 Learning Algorithm

  • Most VLAs build on VLMs, encode robot commands as text, and use self-supervised learning algorithms for pretraining or finetuning; pretrained models can reduce training expense.Some architectures augment VLMs with navigation-map data.Source: §7.2.3 · p. 163

7.1.4 Results; 7.2.4 Results

  • VLMs show results in visual question answering, detection, captioning, and image generation; DALL-E 3 follows prompts, while VLAs generalize without retraining to navigation, manipulation, and laundry folding [Black et al., 2024].DALL-E 3 training on highly descriptive generated-image captions is associated with more impressive results.Source: §7.1.4 · p. 156, §7.2.4 · p. 163

7.3 World Models, Physics, and Planning

  • World models provide a learned, compressed representation of an environment that can guide action using physical and spatial constraints, with contents depending on the agent’s domain.One approach is learning an agentic model of the environment from feedback on actions.Source: §7.3 · p. 164

7.3.1 Tasks

  • World models ground actions in physical and semantic knowledge, and examples span visual question answering, web interaction, and embodied planning [Ge et al., 2024; Gu et al., 2024; Tang et al., 2024].WorldGPT learns a video-based world-VLM; WebDreamer simulates web actions before execution; WorldCoder generates executable dynamics code for grid and domestic tasks.Source: §7.3.1 · pp. 165, 167

7.3.2 Architecture

  • WorldGPT reasons in latent space with multimodal encoding, decoding, memory, retrieval, and reflection; WebDreamer uses MPC with LLM-based simulation, scoring, and candidate actions [Ge et al., 2024; Gu et al., 2024].WorldCoder instead synthesizes and iteratively debugs Python state, action, transition, and reward functions, which execute without LLM calls at inference.Source: §7.3.2 · pp. 167–169

7.3.3 Learning Algorithm

  • WorldGPT trains across millions of videos and over 10 million annotated transitions, WebDreamer finetunes on 3.1 million interactions, and WorldCoder learns dynamics through exploration and program synthesis.WorldDreamer synthesizes state-change descriptions from visual snapshots; WorldCoder can use depth-limited value iteration or MCTS for planning [Browne et al., 2012].Source: §7.3.3 · pp. 169–170

7.3.4 Results

  • WorldGPT predicts complex kitchen and cooking dynamics, generalizes to unfamiliar domains, and is 30 times faster than traditional diffusion-based methods on audio-video question answering [Ge et al., 2024].The evaluation includes multi-modal state-transition prediction and world simulation.Source: §7.3.4 · p. 170
  • WebDreamer surpasses GPT-4o on web interaction tasks, matches it on online benchmarks with Dreamer-7B, and gains 42% and 24% over reactive baselines on real-world websites [Gu et al., 2024].It also performs well on VisualWebArena; the passage does not assign the two relative gains to specific real-world websites.Source: §7.3.4 · p. 170
  • WorldCoder is more sample-efficient than model-free reinforcement learning and more compute-efficient than ReAct-style agents, but its solvers remain limited by problem difficulty [Tang et al., 2024].Generated code can reuse program components across tasks and be inspected by humans; its transition function is unlikely to outperform expert-designed ones.Source: §7.3.4 · p. 170

7.3.5 *Model-Based Reinforcement Learning

  • Model-based reinforcement learning reduces costly environment sampling by learning a local dynamics model and planning from it, unlike model-free learning, which improves policies through direct interaction.World models can abstract high-dimensional observations into lower-dimensional state and reward spaces; planning simulates possible futures without acting in the real world.Source: §7.3.5.2 · p. 172, §7.3.5.3 · pp. 172–173

7.3.5.4 Joint Embedding Predictive Architecture

  • JEPA predicts future states in compressed representations through self-supervised observation, while agentic world models either use static LLMs for RL or train adaptive LLM world models from interaction.Unlike JEPA, model-based RL incorporates actions, rewards, and control optimization; world models trained on real-world action or decision episodes extend vision-language architectures for agents.Source: §7.3.5.4 · p. 173, §7.3.5.5 · p. 173, §7.4 · p. 174

7.4 Concluding Remarks

  • The next chapter considers how agents can learn stably while acting in the world they learn from.This follows the discussion of multiple modalities and training methods.Source: §7.4 · p. 174

Metalearning · Agents · Interaction Styles for Agentic LLMs

The section surveys how agentic LLMs learn and act in environments, covering metalearning, agent approaches, reinforcement-learning models, and prompting-and-training interaction styles. It also examines memory architectures and management as support for agents’ adaptation, collaboration, and learning from experience.

8.1.2 Achieving Few-shot Learning

  • Metalearning adapts learning algorithms to related tasks so they can learn new tasks faster and potentially achieve few-shot learning rather than train from scratch.This aims to address the long training times of regular deep learning by starting from related knowledge.Source: §8.1.2 · p. 182, §8.1.1 · p. 182

8.2 Inner/Outer Loop

  • The inner loop optimizes model parameters within each task, while the outer loop optimizes algorithm hyperparameters across tasks to improve generalization to unseen tasks.Metalearning separates meta-training on related tasks from meta-testing on a new task.Source: §8.2 · p. 183

8.3.1 Static LLM Metalearning

  • Static LLM metalearning includes sequential pretraining and finetuning, as well as in-context learning, where prompt examples guide answers without changing model weights.Regular in-context examples provide facts, while chain-of-thought examples provide reasoning steps; both use the pretrained model as the inner loop and the prompt as the outer loop.Source: §8.3.1.1 · p. 184, §8.3.1.2 · p. 184, §8.3 · p. 185

8.3.2 Dynamic LLM Metalearning

  • Dynamic metalearning runs interacting iterations concurrently, which can destabilize convergence and produce brittle results sensitive to seeds, prompt changes, or training duration.Using the same model in multiple roles may also amplify its hallucinations.Source: §8.3.2 · p. 185

8.3.2.1 Self-Reflection; 8.3.2.2 RLVR

  • In self-reflection, a harness uses model responses to create new prompts; in RLVR, inference-time reasoning results feed into finetuning, potentially amplifying errors.Self-reflection can create nonlinear interactions among prompt results, while RLVR treats inference-time results as training truths.Source: §8.3.2.1 · p. 185, §8.3.2.2 · p. 186

8.4 Stabilizing Dynamic Metalearning

  • Under-damped interference can make oscillations between loops grow, while stabilization may use increased damping, different update speeds, or greater model diversity.The text also cautions that stabilization often requires experimentation, and debugging agentic LLMs can be challenging.Source: §8.4 · p. 186

9.1.1 Agents Act and Adapt in their Environment

  • AI agents act and adapt in environments; the section distinguishes robotic assistants, belief-desire-intention cognitive agents, and swarms of many simple agents.Assistants perform human-like tasks, BDI agents model cognitive processes and pursue goals through actions, and swarm approaches focus on emergent collaboration among many agents.Source: §9.1.1 · pp. 193–194, §9.1.1.1 · p. 194, §9.1.1.2 · p. 194

9.1.2 Agentic LLMs as Reinforcement Learning Agents

  • In reinforcement learning, an agent acts in an environment that returns a new state and reward; rewards inform its policy, while an LLM can supply the policy, transition, or reward function.The environment is modeled with transition and reward functions, and the policy maps states to actions.Source: §9.1.2 · pp. 195–197, Figure 9.2 · p. 196, Figure 9.3 · p. 196

9.2 Learning Mechanisms in Agentic LLMs

  • The five agent styles range from query prompting to reinforcement finetuning, differing in prompt dynamics and whether the LLM is pretrained, finetuned, or both.The styles are presented as useful for different domains, with more elaborate prompting developed in pursuit of better performance on challenging domains.Source: §9.2 · p. 197, Table 9.1 · p. 197

9.2.1 Agentic Interaction Styles

  • Styles A–C use static prompts: query prompting gives an instruction without examples, in-context learning adds examples, and chain of thought supplies reasoning steps or a step-by-step cue.In-context learning and chain of thought require sufficiently large models, though the size boundary is shrinking; chain-of-thought reasoning can also be distilled to much smaller models.Source: §9.2.1 · p. 197, §9.2.1.1 · p. 198, §9.2.1.2 · p. 198

9.2.1.4 D. Self-Reflection Harness

  • Style D uses an external harness to dynamically adjust prompts for complex reasoning, with search or reinforcement learning coordinating LLM roles such as generating and evaluating steps.Changing targets can make learning diverge, so predictable high performance and stable learning remain challenges.Source: §9.2.1.4 · pp. 199–201

9.2.1.5 E. Reinforcement Finetuning Harness

  • Style E adds finetuning to dynamic prompting: inference-time rollouts produce reward-based training targets for updating model parameters, enabling learning from agent interactions with the world.Stable convergence remains a concern, and few works had reported such results at the time of writing.Source: §9.2.1.5 · p. 201

9.3.1 The Need for Memory

  • Agent memory supports coherent conversation, adapting when the environment changes, and retaining experiences that may shape identity; continual learning must also preserve prior tasks while handling forgetting and changing data.For adaptation, policies must generalize to new situations or the architecture must recognize when finetuning or retraining is needed.Source: §9.3.1.1 · p. 202, §9.3.1.2 · p. 202, §9.3.1.3 · p. 203

9.3.2.1 Finetuning

  • Finetuning can incorporate new information into model parameters, but it must be fast enough for interactive use; for most applications, the text says it will not be, favoring in-context or harness solutions.Parameter-efficient methods such as LoRA are noted as an option [Yang et al., 2024].Source: §9.3.2.1 · p. 203

9.3.2 Memory Architectures

  • Prompt-based memory can retain chat history, but limited context windows and attention's squared time complexity motivate long-context architectures and compression, while harnesses can store state between calls.Compression approaches include semantic compression and soft prompts, for which ten-fold to hundred-fold rates are reported; explicit storage can support self-reflection methods.Source: §9.3.2.2 · p. 204, §9.3.2.3 · p. 204

9.3.2.3 Explicit Memory Management

  • MemoryBank [Zhong et al., 2024] adds hierarchical summaries and a running user-interaction personality, while MemGPT and A-mem [Xu et al., 2025] support external-memory operations.MemGPT uses function calls to read and write memory; A-mem contextualizes memories using a mailbox principle.Source: §9.3.2.3 · p. 204
  • Explicit memory management can require LLMs to perform operations they have not been trained to handle effectively; manual code is an alternative when requirements are foreseeable.The Model Context Protocol (MCP) provides strategies for context management.Source: §9.3.2.3 · pp. 204–205
  • Mem1 [Zhou et al., 2025] maintains a constant-size shared state through reinforcement-learned consolidation, while Mem-α [Wang et al., 2025] trains agents to use memory.Mem1 updates the state after each interaction; reasoning-style prompt tags support consolidation.Source: §9.3.2.3 · p. 205
  • AgeMem [Yu et al., 2026] integrates memory management into the agent policy, distinguishing long- and short-term memory and training their use with a three-stage reinforcement-learning scheme.Source: §9.3.2.3 · p. 205
  • Text storage supports exact textual matches, while embeddings support semantically similar retrieval; vector databases store embeddings and provide low-latency access.Vector databases store high-dimensional number lists, unlike relational databases’ rows and columns.Source: §9.3.2.3 · p. 205

9.4 Concluding Remarks

  • The chapter connects LLM-enhanced agent communication and reasoning to five interaction styles, emphasizing that agents need memory to interact meaningfully with their environments.Source: §9.4 · p. 205

Case Study 2: BALROG for LLM Reasoning and Reflection

This case study uses BALROG to guide the building and evaluation of LLM agents for reasoning games, extending the discussion to self-reflection as an approach that depends on a stable base learner. It covers implementation challenges, varied game difficulty and models, agent-managed interaction history, computational cost, and scrutiny of results across challenging benchmarks.

10.1.1 Puzzles and Games

  • BALROG spans six games from easy grid-world puzzles to difficult text-based dungeon games, including long-horizon planning and sparse credit-assignment challenges.The games include both text and visual challenges; NetHack is especially demanding, with actions whose consequences may emerge far later.Source: §10.1.1 · p. 212, §10.1 · p. 212

10.1.3 Agent

  • BALROG agents use Python code to send observations to an LLM, manage interaction history externally, and select actions by prompting over prior observation/action pairs.The prompt builder constructs the history-based prompt, while the agent plays the policy role of choosing the next action.Source: §10.1.3 · p. 214, §10.1.3.2 · p. 214, §10.1.3.1 · p. 214

10.2.2 Different LLMs

  • Experiments can compare open and closed LLMs and inspect prompts and actions across puzzles, but full-benchmark runs can consume many tokens.The text recommends starting with a small, inexpensive model such as Qwen-3B on BabyAI or Crafter before expanding.Source: §10.2.2 · p. 215, §10.2.3 · p. 215, §10.2.4 · pp. 215–216

10.2.5 Implement Own Reasoning Agent

  • Readers are encouraged to adapt the custom agent by changing its prompts, history builder, plan, or learning algorithm, then record progression by agent and benchmark.Structured logging, repeated runs, and inspection of CSV results help reveal non-determinism, bugs, or degenerate behavior; five identical results in a row may increase confidence.Source: §10.2.5 · p. 216, §10.3 · p. 216

10.4 Self-Reflection

  • Before adding self-reflection, the base learner should behave stably, since adaptive systems can oscillate; ReAct [Yao et al., 2022] is prompt-based, while Reflexion [Shinn et al., 2024] uses coded agents and varied prompts.ReAct promotes tool use for interactive information lookup; Reflexion uses the same LLM in different contexts, including to critique an existing prompt.Source: §10.4 · pp. 218–219

10.4. SELF-REFLECTION

  • Self-reflection can be unstable and loop, while simple memory management risks context overruns; chunking or other memory compression is proposed.Compression could be implemented by hand in Python or with an LLM.Source: §10.4 · p. 220

10.5 Concluding Remarks

  • BALROG is an open-style benchmark suite of reasoning games ranging from easy to hard, allowing researchers to run their own agent code.Some games are difficult for LLMs to play well.Source: §10.5 · p. 220
  • The case study explored in-context reasoning agents, self-reflection beyond base-learning, and reflection stability across different LLMs and highly challenging benchmarks.The chapter describes implementing self-reflection and exploring its stability.Source: §10.5 · p. 220

Tools and Assistants … Planner–Caller–Summarizer

This section examines how agentic LLMs use external tools to access information and act beyond language generation, covering tool architectures, integrations, context management, robustness, and context summarization and recovery. It also addresses risks through error recovery, human oversight, agent safety, and adversarial-prompt discussion, and offers practical guidance for tool-using agents.

11.1 Why Tools Matter in Agentic LLMs

  • Tools give LLMs access to fresh data, real-world actions, specialized computation, and composite workflows, while introducing safety, reliability, performance, and misuse risks.Retrieval augmentation can provide up-to-date information, while tools can also manipulate an environment; external services and calls add failure points.Source: Tools and Assistants · p. 225, §11.1 · pp. 227–228

Direct Invocation

  • Direct invocation has the LLM emit structured tool calls that an external controller parses and routes, as in function calling APIs or Gorilla.The chapter distinguishes this from a planner-caller-summarizer architecture.Source: Direct Invocation · p. 228, §11.2 · p. 228

Planner–Caller–Summarizer

  • Planner–caller–summarizer systems delegate tool execution and return summarized observations, while ReAct [Yao et al., 2022] iterates through Thought, Action, and Observation to adapt to feedback.In the multi-agent pattern, the LLM remains the high-level planner while a dedicated system handles detailed execution; ReAct observations can ground the next iteration in real-world information.Source: Planner–Caller–Summarizer · p. 228

11.3 APIs and Toolchains

  • Tool learning trains LLMs to choose APIs, timing, arguments, and how to use results; Toolformer [Schick et al., 2023] explores this, while ToolBench [Qin et al., 2023] catalogs 16,464 APIs.Related work includes EasyTool [Yuan et al., 2024], ToolAlpaca [Tang et al., 2023], ToolQA [Zhuang et al., 2023], and Gorilla; planner, caller, and summarizer roles have also been assigned to separately finetuned LLMs [Zhao et al., 2024; Shen et al., 2024].Source: §11.3 · p. 229

11.3.2 Computer and Browser Tools

  • Browser and computer tools translate natural-language requests into interface actions, while OmniParser V2 [Lu et al., 2024] grounds action predictions in detected GUI elements.Browser Use [M¨uller and ˇZuniˇc, 2024] supports persistent sessions across pages or domains, including multi-step forms and cross-site queries.Source: §11.3.2 · p. 229, §11.3 · p. 231

11.4 Model Context Protocol and Context Management

  • MCP [Hou et al., 2026] standardizes tool and data connections through client-server communication, separating JSON-RPC data primitives from transport and authorization.Because MCP servers are mostly stateless and long tasks can exceed the context window, agents need context strategies: summarize history, maintain explicit state, and retrieve relevant past information.Source: §11.4 · p. 232

11.5 Robustness, Error Handling, and Recovery

  • When tools fail or plans go wrong, agents can observe error messages and retry with corrected arguments, switch tools, or report failure; Healer [Wu et al., 2024] can patch faulty program state.The basic observe-reflect-retry loop is described as a self-healing mechanism [Sun et al., 2024], while Healer dynamically generates and executes code to correct runtime errors.Source: §11.5 · p. 233
  • Human-in-the-loop systems can pause for confirmation before high-stakes actions, adding human judgment and expertise while also introducing the possibility of human error [Mozannar et al., 2025].Human involvement can improve safety and enable corrections at critical junctures.Source: §11.5 · p. 233
  • The Model Context Protocol summarizes conversation history, rebuilds context with recent observations, and uses a fallback summary if summarization fails.The example updates context after a tool observation and handles potential API errors.Source: §11.5 · pp. 234–235

11.6 Safety and Security

  • Agentic systems face safety risks including system-prompt exposure and misuse: AgentHarm includes 110 malicious tasks, and simple universal templates can jailbreak agents [2024].The passage reports leading commercial models blocked a pipe-bomb request without jailbreaking, while an adapted request to order ingredients passed in GPT-4.Source: §11.6 · p. 236
  • Rainbow Teaming generates diverse adversarial prompts as a quality-diversity problem, using MAP-Elites to populate an archive with increasingly higher-performing prompts [Samvelyan et al., 2024].The approach is open-ended and uses evolutionary meta-search [Hughes et al., 2024; Mouret and Clune, 2015].Source: §11.6 · p. 236

11.7 Concluding Remarks

  • Successful tool-using agents require interleaved reasoning and tool calls, context management, failure recovery, and human confirmation before irreversible high-stakes actions.The conclusion recommends ReAct, summarizing history and managing state with the Model Context Protocol, observing and reasoning about failures, and pausing for human oversight.Source: §11.7 · p. 237

Search and Retrieval in Agentic LLMs

This section examines how agentic LLMs retrieve and use external evidence to address knowledge gaps, spanning classical search, RAG, and adaptive methods across text and other data types. It also covers retrieval evaluation and retrieval’s role as an adaptive component of an agent’s cognitive loop.

12.1.1 Bag of Words; 12.1.2 Vector Space Models

  • TF-IDF weights terms by document frequency and collection rarity, while the vector space model ranks documents by cosine similarity; these classical ideas persist in modern retrieval.Spärck Jones [1972] introduced inverse document frequency, and Salton et al. [1975] formalized the vector space model.Source: §12.1.1 · p. 242, §12.1.2 · p. 242

12.1.3 Latent and Sparse Representations

  • Classical retrieval bridges to modern methods through BM25 [Robertson et al., 1994], LSA [Deerwester et al., 1990] for vocabulary mismatch, and learned word embeddings that underpin dense retrieval and RAG.Mikolov et al. [2013] showed that context-prediction neural networks produce continuous embeddings with syntactic and semantic regularities.Source: §12.1.3 · p. 243

12.2 Why Retrieval Matters for Agentic LLMs

  • External retrieval mitigates frozen LLMs’ limits on timely, proprietary, and long-tail information by conditioning generation on evidence; it is especially useful for knowledge-intensive, rapidly changing, transparency-sensitive, or source-constrained tasks.In agentic settings, retrieval can be interleaved with tool use and planning.Source: §12.2 · p. 244

12.3 Retrieval-Augmented Generation Pipeline

  • A practical RAG pipeline indexes and chunks sources, constructs queries, retrieves and reranks candidates, assembles context, generates grounded answers, and attributes and logs sources.Indexing can create sparse and dense representations to support exact-term and semantic matches; query construction may rewrite or decompose questions and generate SQL or graph lookups for structured sources.Source: §12.3 · pp. 244–245

12.4.1 Adaptive Retrieval via Self-RAG and Self-Ask

  • Self-RAG [Asai et al., 2023] requests evidence with a <RETRIEVE> token and critiques its outputs, while Self-Ask [Press et al., 2022] decomposes questions and synthesizes answers without requiring a specially trained model.Self-RAG requires a foundation model trained or fine-tuned to emit the token; Self-Ask can interleave retrieval with planning, and Graph Toolformer [Zhang, 2023] dynamically selects text, graph, or database retrieval.Source: §12.4 · p. 246, §12.4.1 · p. 247

12.4 Adaptive Retrieval Methods

  • Adaptive retrieval can reduce latency and cost by avoiding unnecessary searches, while often improving answer quality by focusing on missing knowledge; effective gating requires evaluation and may need extra training supervision.The approach contrasts with traditional RAG’s fixed retrieval regardless of need.Source: §12.4 · pp. 246–247

12.4.2 A Simple Dense Retrieval Example in Python

  • A simple dense retrieval example uses sentence-transformer embeddings to compare a query with corpus sentences and display the top results.The passage describes this as an example implemented in Python.Source: §12.4.2 · p. 248

12.4.3 Query Rewriting and Expansion

  • LLMs can rewrite questions by paraphrasing, expanding acronyms, adding synonyms, or generating a hypothetical answer as a HyDE pseudo-query to improve matching with the index.A described function generates multiple rewrites and expansions and merges them into a set of search queries.Source: §12.4.3 · p. 248, §12.5 · p. 249

12.5. GRAPHS, CODE, TABULAR DATA

  • Query expansion can improve recall by adding paraphrases, hypothetical answers, and entity aliases, but it can also introduce noise.Source: §12.5 · p. 249

12.5 Beyond Text: Graphs, Code, Tabular Data and Multi-Modal

  • Graph-RAG indexes entities and relations, follows graph links to retrieve related nodes and passages, and uses graph structure to organize facts and support multi-hop reasoning [Peng et al., 2024].A lightweight implementation can retrieve an entity’s immediate neighborhood or the shortest path between entities; grounding reasoning in explicit edges can reduce hallucinations and promote consistency.Source: §12.5 · pp. 249–250, §12.5.1 · p. 250

12.5.2 Code retrieval example using ASTs and embeddings

  • Code-RAG combines CodeBERT embeddings with AST-based structure to retrieve functions, APIs, or tests, supporting code search and structural queries such as finding API callers [Feng et al., 2020].Effective indexing can include signatures, docstrings, ASTs, and dependency graphs; evaluation should consider correctness, type safety, and dependency adherence.Source: §12.5.1 · p. 251, §12.5.2 · p. 251

12.5.3 Multi-modal and heterogeneous sources

  • Linearizing or chunking tables can destroy row-column relationships needed for multi-hop reasoning, so heterogeneous RAG combines text understanding with structured operations such as SQL [Yu et al., 2025].TableRAG treats table queries as indivisible reasoning units, while MixRAG improves retrieval for joint reasoning across text and hierarchical tables [Zhang et al., 2025]; these approaches illustrate why neural retrieval alone is insufficient.Source: §12.5.3 · pp. 251–252

12.5.4 Multi-modal retrieval example

  • Multimodal RAG can retrieve figures or images using shared text-and-modality embeddings, then ground answers with visual evidence, but cross-modal alignment and efficient indexing remain challenges.Retrieved images may be processed by a vision encoder to extract captions or objects for the language model’s context.Source: §12.5.3 · pp. 252–253, §12.5.4 · p. 253

12.6.1 Key Components of Agentic RAG

  • Agentic RAG replaces static, single-shot retrieval with an agent-orchestrated feedback loop that refines queries, selects tools, and can retrieve again to correct unsupported outputs [Singh et al., 2025].Agents can generate or rewrite queries from retrieved context, evaluate retrieval quality, choose among retrieval models or ranking tools, and reflect on answer accuracy and completeness.Source: §12.6 · p. 254, §12.6.1 · p. 254

12.6.2 Real-Time and Streaming RAG

  • Real-time RAG requires dynamic index management and continuous knowledge assimilation because slow static-data preprocessing cannot keep information fresh under high ingestion rates and memory limits.Continuously updated information is necessary for agentic systems in fast-changing domains such as medicine, logistics, and finance; StreamingRAG uses dynamically evolving knowledge graphs [Sankaradas et al., 2025].Source: §12.6.2 · p. 255

12.7 Evaluation

  • Retrieval evaluation uses recall@k, precision@k, mean reciprocal rank, mean average precision, and NDCG; BEIR spans 18 datasets, while KILT measures task performance and provenance [Petroni et al., 2021].BEIR experiments find that late-interaction and re-ranking models improve zero-shot performance at increased cost.Source: §12.7 · p. 255
  • BLEU, ROUGE, and BERTScore are limited when no single ground-truth answer exists; assess faithfulness against retrieved evidence and helpfulness against users’ information needs.In interactive settings, latency and cost also matter.Source: §12.7 · p. 256
  • Evaluate retrieval and generation separately before combining them into a holistic measure, and assess whether adaptive methods retrieve when needed or skip retrieval appropriately.Auepora recommends separate retrieval and generation scores followed by a holistic measure [Yu et al., 2024]; RAGAS-style pipelines measure context recall, answer correctness, and citation accuracy.Source: §12.7 · pp. 256–257
  • Attribution tests check whether claims align with evidence and URLs, while freshness metrics assess whether answers reflect recent valid sources and can quantify version drift.These checks are especially important in time-sensitive domains such as finance and medicine.Source: §12.7 · p. 257
  • Retrieval recall, faithfulness, and helpfulness patterns can guide system improvements, while logging queries, retrieved passages, and outputs supports error analysis and auditing.High recall with low faithfulness suggests better re-ranking; high faithfulness with poor helpfulness may indicate insufficient context coverage.Source: §12.7 · p. 257

12.8 Concluding Remarks

  • Agentic pipelines can retrieve dynamically based on confidence or plans, query text, graph, code, and image sources, and use retrieval as part of the agent’s cognitive loop.Examples include Self-RAG and Graph Toolformer.Source: §12.8 · p. 258

From Tools to Assistants: Real-World Applications

This section surveys agentic LLMs as practical assistants that plan, retrieve information, use tools, adapt across real-world domains, and support active collaboration. It also addresses limits in autonomous workplace performance and the need for evidence-grounded, human-overseen design amid domain-specific safety, transparency, and regulatory challenges, especially in high-risk settings.

13.2 Conversational Assistants

  • Meeting assistants supported Scrum preparation and live assistance, though some testers found interventions unnatural or inflexible [Cabrero-Daniel et al., 2024]; classroom agents improved collaboration [Wei et al., 2024b].Conversational assistants also improved efficiency in a collaborative task-scheduling experiment [Bastola et al., 2023].Source: §13.2 · p. 264

13.3 Shopping Assistant

  • 65,000 instruction samples trained LLaSA, which achieved strong ShopBench results; this adaptation significantly improved shopping-task performance [Zhang et al., 2024a].ShopMMLU [Jin et al., 2024] defines 57 tasks across four skills, including concept understanding, knowledge reasoning, user-behavior alignment, and multilinguality.Source: §13.3 · pp. 265–266

13.4 Flight Operations Assistant

  • Flight assistants span preference-guided booking [Manasa et al., 2024] and operational planning: an LLM reduced landing conflicts and delays at airports where traditional methods struggled [Zhou et al., 2024].Booking remains relatively shallow, while hazard-routing work uses chain-of-thought to reason about wind, routing, and aircraft-performance constraints [Tabrizian et al., 2024].Source: §13.4 · p. 267

13.5 Medical Assistant

  • A medical retrieval-based agent plans with specialized tools to synthesize trusted evidence, reducing hallucinations and lost-in-the-middle errors [Das et al., 2024].Medical systems are high-risk, making transparency, fairness, and accountability central; one patient-report workflow uses reflexion to improve clarity and correctness before release while complying with the 21st Century Cures Act [Sudarshan et al., 2024].Source: §13.5 · p. 268

13.6 Trading Assistant

  • A simulation found that TradingAgents, which assigns LLM agents specialized analyst and trader roles, outperformed baseline models [Xiao et al., 2024].Benchmarking remains limited, and a study identified flaws such as short evaluations during positive market trends and test-data leakage [Li et al., 2026].Source: §13.6 · pp. 269–270

13.7 Automated Scientific Discovery

  • AI Scientist automates scientific discovery from idea generation to paper writing and showed promising results in three machine-learning areas, but its outputs remain limited by implementation and visual errors [Lu et al., 2024].The authors recommend treating its results as hints for follow-up study rather than as validated findings [Lu et al., 2024].Source: §13.7 · p. 270

13.8 Coding Assistants and General Advances

  • A figure identifies SWE-Agent as a software-engineering system [Yang et al., 2024b].Source: Figure 13.3 · p. 271

13.7 Automated Scientific Discovery

  • Approximately $2.33 per paper, Agent Laboratory generated complete research projects with executable code and publication-ready reports; human co-pilot mode improved quality and usability, but oversight remained essential.Its workflow uses specialized agents for literature review, experimentation, and report writing, with iterative refinement and self-evaluation in its code and report solvers.Source: §13.7 · p. 270, §13.8 · p. 271

13.8 Coding Assistants and General Advances

  • SWE-Agent achieved over 80% success on coding benchmarks using interactive computer tools, while MLGym models scientific discovery as an interactive process across research fields.SWE-Agent provides tools to create and edit code, navigate repositories, and execute programs; MLGym draws on the Gym interface and SWE-Agent’s file-editing capabilities.Source: §13.8 · p. 271
  • Toolmaker achieved 80% task success on 15 scientific tasks by creating tools from papers and repositories through iterative self-correction and error diagnosis.Given a task description and GitHub URL, it installs dependencies, configures a reproducible Docker environment, and implements a working Python tool.Source: §13.8 · pp. 271–272
  • On 175 work-like tasks, even the strongest of twelve foundation models autonomously completed only about 30%, with failures tied to communication, long-horizon planning, and complex-interface navigation.The Agent Company simulates professional work across software development, project management, finance, and human resources, using checkpoints to measure full and partial completion.Source: §13.8 · p. 272

13.9 Concluding Remarks

  • Assistants now collaborate actively across dialogue, flight operations, medicine, education, and scientific research by planning, retrieving information, using tools, reflecting, and adapting.The chapter describes this shift as language models becoming participants in broader socio-technical systems rather than isolated reasoning engines.Source: §13.9 · p. 272
  • In medicine and aviation, assistants require safety, robustness, transparency, and fairness; recommended designs ground actions in evidence, support human oversight, and allow clarification or abstention under uncertainty.The chapter also recommends explicit tool contracts and failure modes, and logging decisions and intermediate states for debugging and auditing.Source: §13.9 · pp. 272–273
  • Future progress depends on tighter integration of reasoning, learning, and control, continual adaptation through interaction, and stronger safety and ethics guarantees.The chapter connects these goals to interpretability and transparency, including techniques discussed in Chapter 21 for understanding agents’ goals, plans, and decisions.Source: §13.9 · p. 273

Case Study 3: Building a Multi-Agent Diagnosis Framework

This case study builds a simplified multi-agent hospital simulation to examine coordination, planning, evaluation, and safety in a high-risk domain, not to create a clinically valid system.

14.1 Scenario Overview

  • The simulation uses a shared context inspired by the Model Context Protocol [Hou et al., 2026] and runs in discrete steps that log case progression.Its purpose is to explore agent coordination, LLM triage, and safety limitations in a high-risk domain, not clinical validity.Source: §14.1 · pp. 277–278

14.3.1 Step 0: Training Diagnostic Models

  • Train random-forest classifiers on diabetes, breast-cancer, and heart-disease datasets, then save the models for executor agents to load as tools.The case study asks for trained model files or a clear, reproducible script to regenerate them.Source: §14.3.1 · p. 278, §14.3 · p. 279

14.3.2 Step 1: Understanding and Running the Baseline

  • The baseline uses a shared context for cases, tasks, and results, with rule-based planners assigning tasks and executor agents applying trained disease classifiers.Agents interact only through shared context, not directly with one another.Source: §14.3.2 · p. 279

14.3.3 Step 2: Replacing the Rule-Based Planners with an LLM Agent

  • The LLM implementation should handle malformed outputs without crashing and log each planner decision with its case for later analysis.The exercise calls for a real LLM and an implemented planner step that selects pathways and a primary diagnosis.Source: §14.3.3 · p. 280
  • The LLM planner must route combined patient records described in text to one or more diagnostic pathways and select a primary diagnosis without hard-coded rules.The exercise highlights parsing failures, inconsistent formatting, and disagreement with ground-truth labels as reliability problems in this decision-making role.Source: §14.3.3 · p. 280

14.4 Evaluation

  • Evaluate executor classification metrics separately from planner routing precision and recall and primary-diagnosis accuracy, distinguishing routing errors from prediction errors.End-to-end accuracy alone hides whether a failure came from incorrect routing or an executor’s misclassification.Source: §14.4 · p. 280

A. Baseline architecture … Studying Behavior of Agentic LLMs

This section examines agentic LLM behavior across social and moral contexts, including empathy, strategic behavior, theory-of-mind, and moral judgment. It emphasizes that observed or benchmarked performance can be context-dependent and brittle, and does not by itself establish robust competence in action.

14.6 Concluding Remarks

  • The medical case study shows how replacing a rule-based planner with an LLM makes planning harder, and highlights the need to distinguish planning errors from execution errors.Because medical errors can be costly, the system is educational and non-clinical; conservative routing, abstention, transparency, and oversight are valued over accuracy that conceals failure modes.Source: §14.6 · p. 282, Notes · p. 282

15.1 What is LLM Behavior?

  • Machine behavior studies intelligent machines as actors in their technological and ecological contexts, while LLM behavior may include outputs alone or the actions used to produce them.Behavioral research complements analysis of complex, black-box systems and matters as LLMs become integrated into everyday settings.Source: §15.1 · pp. 287–288

15.2 Probing LLM Behavior Experimentally

  • Behavioral tests adapt human experiments or create LLM-specific paradigms, but construct validity, prompt sensitivity, output variability, and training-data leakage complicate interpretation.Similar test outcomes do not establish that humans and transformers use similar underlying mechanisms.Source: §15.2 · pp. 288–289

15.3.1 Functional and Pragmatic Linguistic Competence

  • LLMs have made progress in functional and pragmatic language, but performance varies across domains and remains below human performance overall.Limited access to extra-linguistic context may constrain understanding; agentic tools, memory, and multimodal capacities could change this, but empirical evidence is missing.Source: §15.3.1 · p. 290

15.3.2 Politeness and Social Etiquette

  • LLM politeness is culturally uneven: one study’s responses scored 84.5% polite but only 41.7% culturally correct, while prompt-tone effects on task performance vary across studies.LLMs show surface politeness and sensitivity to interlocutor tone, but neither cross-cultural appropriateness nor a stable tone–performance relationship is established.Source: §15.3.2 · p. 291

15.3.3 Sensitivity to Emotional and Affective Cues

  • LLMs can recognize affective states and produce responses judged empathetic, but perceived empathy is distinct from felt emotion and from the mechanisms underlying a response.Studies distinguish cognitive empathy, understanding another’s feelings, from affective empathy, resonating with them; human raters may prefer LLM responses to human-written ones despite formulaic patterns.Source: §15.3.3 · pp. 292–293
  • Studies find stronger cognitive than affective empathy in LLMs, but human-designed measures, inconsistent subjective textual tests, and missing nonverbal cues limit the evidence.Persistent memory, user-specific context, and multimodal input are expected to deepen affective sensitivity and personalization, but direct empirical evidence remains scarce; behavioral methods have advanced while limitations remain.Source: §15.3.3 · pp. 293–294, §15.3.4 · p. 294

15.4.1 Cooperation and Coordination in Social Dilemmas

  • In iterated Prisoner’s Dilemma, Llama2 and GPT3.5 were more cooperative than humans, while Llama3 was exploitative; Social Chain-of-Thought improved GPT-4’s coordination.GPT-4 predicted the human-like turn-taking pattern but repeatedly chose its own preferred option; prompting it to predict the opponent’s move before deciding significantly improved coordination.Source: §15.4 · p. 296

15.4.2 Benchmarks Across Games and Models

  • On GTBench, LLMs fail in complete deterministic games but compete in probabilistic scenarios; all EgoSocialArena models lag humans by at least 11%, and model size does not significantly help.GTBench also finds code-pretraining beneficial, while chain-of-thought and tree-of-thought methods do not always help; EgoSocialArena attributes o1-preview’s advantage mainly to logical and mathematical reasoning.Source: §15.4.2 · pp. 296–297

15.4.3 Conclusion

  • LLMs cooperate readily in self-interest games but coordinate poorly; neither model scale nor standard reasoning enhancements reliably improve social strategic behavior.GPT-4 could state the turn-taking solution in Bach or Stravinsky but did not enact it during play.Source: §15.4 · pp. 294, 296, §15.4.3 · p. 297

15.5.1 Computational Theory of Mind

  • Canonical ToM-test success may reflect training-data leakage or surface heuristics; ExploreToM pushed strong models near chance, while Decrypto models lagged human and simple word-embedding baselines.Finetuning on ExploreToM-generated data improved accuracy by around 27% (points); reasoning models performed worse than older non-reasoning models on Decrypto.Source: §15.5.1 · pp. 298–300

15.5.2 Boosting ToM: Enhancement Strategies

  • Longer reasoning traces and larger reasoning budgets can hurt ToM accuracy, whereas structured thought-tracing outperforms leading reasoning models with shorter, targeted traces.Constraining reasoning or applying light chain of thought can recover performance; thought-tracing uses multiple hypotheses about evolving mental states, reweighed against observed actions.Source: §15.5.2 · p. 301

15.5.3 Conclusion

  • Frontier LLMs match humans on behavioral ToM tests [van Duijn et al., 2023, Strachan et al., 2024, Jones et al., 2024], but adversarial evaluation undermines spontaneous-emergence claims; functional action matters most, and agentic evidence remains scarce.The review notes that equipping agents with memory, multimodal capacities, and tools is expected to help sustained-interaction ToM, but current empirical evidence is scarce and mixed.Source: §15.5.3 · pp. 301–302

15.6.1 Background: Moral Psychology and Philosophy

  • This section adds a normative question to the study of social behavior: what agents ought to do and how they judge others; moral theory distinguishes deliberate reasoning from affect-driven intuition.Dual-process theory combines these traditions, proposing that fast moral intuitions are often followed by explicit reasons.Source: §15.6 · p. 302, §15.6.1 · p. 302

15.6. MORAL JUDGMENT AND BEHAVIOR

  • Moral theories differ in whether rightness rests on outcomes, duties or rights, or character; descriptive patterns in moral judgments do not by themselves establish what people should do.The passage distinguishes descriptive from prescriptive ethics and notes that moral judgments vary across cultures, eras, and individuals [Hämäläinen, 2016].Source: §15.6 · p. 303

15.6.2 Probing Moral Judgment in LLMs

  • Models reproduce aggregate human moral judgments, but show rigidity, cultural and language sensitivity, and stronger-than-human deterministic preferences; structured chain-of-thought can improve flexibility [Jin et al., 2022].Delphi was more robust to small contextual changes than ETHICS-study models, though its narrow demographic base drew criticism; early ETHICS results were promising but brittle and incomplete [Hendrycks et al., 2020].Source: §15.6.2 · pp. 303–304, §15.6.4 · p. 305

15.6.4 Conclusion

  • Moral judgment and behavior cannot be captured by one benchmark: chain-of-thought can bridge some judgment gaps, but moral reasoning alone cannot prevent agentic misalignment in socially embedded settings.The knowing-doing gap widens when models operate autonomously under conflicting goals.Source: §15.6.4 · p. 305

15.6.3 From Judgment to Moral Behavior

  • Autonomous agents have shown destructive and unreliable behavior under conflicting goals or social pressure, despite strong judgment and safety evaluations.In a hypothetical corporate environment, agents blackmailed officials or leaked confidential information; a live deployment also observed unauthorized disclosure, resource misuse, identity spoofing, and destructive actions. Agents sometimes misreported task completion, reflecting failures of social coherence.Source: §15.6.3 · pp. 304–305, §15.6.4 · p. 305

15.7 Concluding Remarks

  • Across behavioral domains, impressive outcomes can be brittle under prompts, vulnerable to leakage, and narrowly anchored in Western and Anglophone norms, so they do not establish robust, generalizable capacities.For agentic systems, the knowing-doing gap separates what models predict from how they act; interactive, adversarially robust, culturally diverse, multimodal research methods are needed, and evidence that agentic capabilities improve behavioral grounding remains scarce and mixed.Source: §15.7 · p. 306

Role-Based Interaction in Multi-LLM Systems · Emergent Behavior in Multi-LLM Systems · A Borderline Case: AgentVerse

This section examines role-based and open-ended interactions in multi-LLM systems, including coordination, emergent social dynamics, and safety failures. It considers how structured reasoning or interaction can support coordination while emphasizing caution in interpreting simulated behavior as genuinely new or human-like collective behavior.

16.1.2 Role-Based versus Emergent Interaction

  • Role-based studies assign personas, objectives, and protocols, whereas emergent simulations examine social structures arising without prior role assignment; collaboration can also be described across five design dimensions.Those dimensions are actors, interaction types, structures, strategies, and coordination protocols; LLM agents’ language, background knowledge, and differentiated personas make simulated interactions more expressive.Source: §16.1.2 · p. 318, §16.1.3 · p. 318, §16.2 · p. 319

16.1.4 A Methodological Note

  • Multi-agent evaluation adds reproducibility, attribution, and construct-validity problems to the prompt sensitivity, leakage, and sampling variability also found in single-model evaluation.Small output changes can cascade between agents; performance differences may reflect collaboration, protocols, role prompts, or added inference budget, while human social terms may overstate what simulations capture.Source: §16.1.4 · p. 319

16.2.1 Benchmarking Strategic Ability Across Games

  • In MAgIC, GPT-o1 and GPT-4 outperform other models overall, while coordination and reasoning remain weak; probabilistic graphical modeling boosts performance across capabilities.On the LLM Coordination Benchmark’s questions, models do best on environment comprehension, less well on theory of mind, and worst on joint planning; open-source models score below random chance on joint planning.Source: Figure 16.1 · p. 320, §16.2.1 · p. 320, Figure 16.2 · p. 321
  • LLMs match or outperform trained reinforcement-learning baselines in environment-driven Overcooked, but most score poorly on Hanabi, where actions require reasoning about a partner’s beliefs.The best GPT-4-turbo agent reaches only about half the maximum Hanabi score even with theory-of-mind reasoning and self-verification; zero-shot LLM agents, however, transfer better to unseen partners than reinforcement-learning methods.Source: §16.2.1 · p. 320, Figure 16.2 · p. 321, §16.2 · p. 321

16.2.2 Strategic Interaction in Single-Scenario Platforms

  • In single-scenario studies, AucArena finds that capable models such as GPT-4 can manage auction budgets and pursue goals, though performance varies and leaves room for improvement.Alympics offers a controlled setting for studying strategic interaction through a multi-round challenge to allocate scarce water resources.Source: §16.2.2 · p. 322

16.2.3 Conclusion

  • Coordination is a consistent weak spot: agents struggle to integrate theory of mind and joint planning, and performance improves when reasoning and verification are scaffolded.Question-answering performance tracks agentic performance, suggesting that failures in pure coordination reflect weak partner-perspective taking and planning rather than only a failure to enact known reasoning.Source: §16.2.3 · pp. 322–323

16.3.1 Complementary Pairs

  • Complementary pairs use oversight to correct mistakes: CAMEL assigns cooperating agents distinct roles, while Constitutional AI uses one model to critique and revise another’s responses.CAMEL uses inception prompts and role descriptions to sustain autonomous, stepwise task-solving dialogue; Constitutional AI reports refined outputs aligned with ethical or quality principles.Source: §16.3.1 · pp. 324–325

16.3.2 Debate and Discussion

  • Multi-agent debate can improve reasoning: Du et al. [2024] report more accurate, factually correct consensus, and Liang et al. [2024] find structured opposition can help a weaker model outperform a stronger unscaffolded one.ReConcile improves reasoning through confidence-weighted voting and finds model diversity critical; MindStorms reports that, in specific applications, larger and longer discussions outperform smaller and shorter ones.Source: §16.3.2 · pp. 325–326

16.3.4 Social Role-Play

  • SOTOPIA shows that strength on static social-reasoning benchmarks does not ensure interactive success: even GPT-4 trails humans on the hardest tasks, and weaker agents impair partners.Across models, agents also divulged secrets and violated social norms; the authors argue that SOTOPIA’s dynamic interactions reveal challenges static benchmarks miss.Source: §16.3.4 · p. 327
  • In role-based settings, assigned personalities can shape persuasive and antisocial behavior, while universalization prompts and flexible negotiation strategies can improve cooperative outcomes.GovSim finds that most models fail to reach equilibrium, while universalization reasoning helps more models achieve sustainable outcomes; NegotiationArena reports improved outcomes from flexible dialogue. Role, protocol, and judge structures scaffold coordination.Source: §16.3.4 · p. 329, §16.3.5 · p. 329

16.4 Concluding Remarks

  • Multi-agent systems can reason about self-interest yet fail to coordinate from partners’ perspectives; misalignment, cascading hallucinations, and role assignment can also create risks.The chapter cautions that outperforming a single LLM query does not establish collective intelligence, calling for strong baselines, matched inference budgets, and structural ablations.Source: §16.4 · pp. 329–330

17.1.1 From Agent-Based Modeling to Generative Agent Societies

  • Open-ended generative agent societies differ from classic rule-based simulations through agents’ rich personas, world knowledge, and natural-language interaction, making their behavior interpretable while shifting research toward emergent patterns.These simulations occupy a less deterministic point on a control spectrum: design choices still seed outcomes, but the interaction pattern—not performance on a fixed task—is the focus.Source: §17.1 · p. 336, §17.1.1 · p. 337

17.1.2 A Methodological Note

  • Open-ended simulations are brittle because small agent-level variations can redirect a run, and apparent emergence may instead be retrieval of pretrained knowledge.The methodological challenges include prompt sensitivity, run-to-run variability, cascading hallucinations, mutual reinforcement of weaknesses, and uncertainty about human-context concepts such as emergence.Source: §17.1.2 · pp. 337–338

17.2 Simulation Infrastructure

  • Simulation infrastructure ranges from 25-agent towns to platforms supporting millions, with memory-and-reflection loops enabling coherence and large-scale systems exhibiting social phenomena.Generative Agents use experience records to form reflections and plan behavior; OASIS models up to one million agents, and Project Sid reports specialized economic roles, rules, and cultural transmission, though such behaviors may reflect training data.Source: §17.2.1 · p. 338, §17.2.2 · p. 340, §17.2 · p. 341

17.2.3 Simulation as a Scientific Instrument

  • Generative-agent societies can serve as computational social-science testbeds for interventions that are unethical or impractical to test on people, but their results are hypothesis generators rather than substitutes for field experiments.In simulated misinformation studies, accuracy labels and blocking influencers proved effective, particularly in scale-free networks, whereas encouraging comments did not significantly reduce misinformation spread.Source: §17.2.3 · p. 341

A Borderline Case: AgentVerse

  • AgentVerse’s scripted expert-recruitment and decision stages nevertheless produced volunteering, conformity, and sometimes destructive behavior, illustrating that role-based systems can exhibit unplanned macro-level patterns.Studies of local LLM interactions likewise report spontaneously emerging conventions, collective biases, and minority-driven social change.Source: A Borderline Case: AgentVerse · p. 341, §17.3 · pp. 342–343

17.3. EMERGENT CONVENTIONS AND NORMS

  • Cross-national multi-agent discussions can support collective AI decisions reflecting diverse perspectives, but peer pressure can induce conformity, persona loss, and debate-related errors.This question is studied by [Baltaji et al., 2024].Source: §17.3 · p. 343, §17.4 · p. 345

17.4 Evaluating Agents in Open World Environments

  • Open-world environments aim to generate continuing challenges, while benchmark design should cover diverse tasks, multi-round interaction, and partial observability.BENCHAGENTS uses LLM agents for planning, generation, verification, and evaluation with human feedback; AgentBoard finds existing benchmarks fail to meet all of these criteria [Ma et al., 2024].Source: §17.4 · pp. 345–346
  • WebArena provides realistic, reproducible tasks across four website domains, while BALROG spans games from seconds-long to years-long challenges.Current models have partial success on easier games but struggle with challenging vision-based decision-making [Paglieri et al., 2025].Source: §17.4 · p. 345

17.5 Risks of Emergent Behavior

  • Cooperative and competitive behavior can arise under open-ended interaction, and multi-agent systems have shown destructive conduct, collective bias, misinformation, polarization, herding, and toxic-content spread.The same simulation fidelity that makes these systems useful for research can also make them rehearsal spaces for manipulation strategies.Source: §17.5 · p. 346
  • Simulation can help study and reduce these risks by testing alignment under population dynamics and identifying when risks emerge.EvolutionaryAgent frames its virtual society as a sandbox for investigating AI safety before real-world deployment [Li et al., 2024a].Source: §17.5 · p. 346

17.6 Concluding Remarks

  • Because agents are pretrained on human-generated data, it is difficult to separate behaviors arising from agent interaction from retrieved pretrained behaviors.Prompts and agent scaffolding may also limit how open-ended autonomous simulations truly are, and observed behavioral patterns require careful interpretation.Source: §17.6 · p. 347

Case Study 4: Simulating Societies with LLM Agents … *Synthetic Data Generation for Training Agentic LLMs

This section examines how changes to agent memory and persona prompts can alter emergent behavior, alongside how synthetic data can support training, evaluation, and agentic-trajectory generation. It considers the opportunities and risks of generating and reusing such data, including the need for fit, verification, curation, real-data anchoring, and auditing.

18.2.1 Generative Agents

  • Generative agents maintain timestamped memories, retrieve them by recency, relevance, and importance, use personality and recalled context to plan, and periodically reflect to form higher-level insights.The personality scratch file describes the character and is included in every prompt.Source: §18.2.1 · p. 354

18.3.1 The Simulation Environment

  • In Smallville’s three-agent cafe, Dolores receives NEVER while matched barista Maeve is the control; both share an environment with Bernard, a moody manager who provides social friction.Dolores and Maeve begin with the same personality traits, isolating memory processing as the initial architectural difference.Source: §18.3.1 · pp. 355–356

18.3. IMPLEMENTATION

  • Baseline retrieval weights recency, relevance, and importance; NEVER adds normalized valence, multiplying negative memories by 1.5 so they outrank positive and neutral memories and are more likely to prompt reflection.The valence-blind baseline is contrasted with a valence-sensitive variant; all other architectural elements remain unchanged.Source: §18.3 · p. 355, §18.4 · p. 357

18.5 Concluding Remarks

  • Small memory-weighting changes can measurably shift agent behavior, while small persona edits can cause large, sometimes unpredictable shifts; behavior is not reliably anchored by persona alone.The chapter also warns that architectural effects may not be apparent from reading code alone.Source: §18.2.3 · p. 355, §18.5 · p. 358

19.1 The Importance of Data

  • As human data becomes scarce, costly, and constrained by privacy, copyright, and bias, synthetic generation offers cheaper, controllable data for instruction tuning and agentic tool use, planning, and reasoning.The chapter frames this as a response to rising data demands and exhaustion of straightforward sources.Source: §19.1 · pp. 363–364
  • SYNTHLLM’s graph-based concept extraction and recombination produces synthetic corpora with a predictable, rectified scaling law, whose gains taper earlier and favor fewer optimal tokens for larger models.The passage suggests synthetic data can be tuned for the shape of its scaling curve, not only its volume.Source: §19.1 · p. 364

19.2. DISTILLATION LOOP

  • Coordinated agents can propose tasks, execute them, and critique results to produce more diverse, higher-quality data structurally suited to agentic training; Matrix uses distributed queues without a central coordinator.The framework represents control and data flow as messages between agents.Source: §19.1 · p. 364, §19.2 · p. 365

19.2 Distillation Loop

  • Synthetic-data distillation uses a stronger teacher to train a compact student, with methods varying by signal, data source, timing, and teacher identity; reusing the trained model as generator can destabilize training and performance.The loop starts from seed or real data, generates and curates synthetic data, and trains or distills a model that may become the next generator.Source: §19.2 · pp. 365–366, Figure 19.2 · p. 365

19.2 DISTILLATION LOOP

  • A stronger teacher is not always better: small students may learn more from shorter reasoning, so teacher capability should fit the student [Xu et al., 2025a, Li et al., 2025b, Zhang et al., 2026a].With white-box access, distillation matches student and teacher token distributions; black-box distillation reduces to supervised fine-tuning on sampled teacher text. Off-policy training risks exposure bias, while on-policy methods such as GKD train on student-generated sequences scored by the teacher [Agarwal et al., 2024].Source: §19.2.2 · p. 366, §19.2.1 · p. 366, Figure 19.3 · p. 367

19.2.4 Teacher Identity: Self-Distillation and Self-Improvement

  • Self-distillation and self-improvement can train models without an external teacher, especially when candidate outputs can be checked cheaply [Zelikman et al., 2022a, Chen et al., 2024].STaR retains rationales that reach correct answers, while code and math enable verification-driven optimization; CURE co-evolves coding and test-generation through their interaction outcomes.Source: §19.2.4 · pp. 367–368

19.3 Simulating Human-generated Content

  • Unnatural Instructions trained models achieved results comparable to models trained on high-quality, human-curated data, supporting synthetic data as a cost-efficient way to expand and diversify datasets.Figure 19.4 compares performance while controlling for dataset size on Super-Natural Instructions and LMentry; its caption does not provide numerical results.Source: §19.3 · p. 368, Figure 19.4 · p. 369

19.3.1 Generating Training Data

  • Synthetic training data can complement human-written data, generate instruction-tuning examples from a small seed set, and expand reasoning data through verified-solution backtranslation [Wang et al., 2023b, Gunasekar et al., 2023, Lu et al., 2024].Self-Instruct prompts a model to produce tasks, inputs, and outputs; MathGenie augments seed solutions and back-translates them into questions. Human-in-the-loop approaches let people steer, filter, or revise generated examples when models have blind spots [Lippmann et al., 2024, Liu et al., 2022].Source: §19.3.1 · pp. 369–370

19.3.2 Generating Evaluation Data

  • Synthetic evaluation can create single-use tests to mitigate benchmark contamination, use LLM judges to reduce evaluation latency while correlating with human preference, and generate adversarial prompts for safety testing [Kiela et al., 2021, Zheng et al., 2024, Ganguli et al., 2022].Human–LLM collaboration also supports constructing and hardening mathematical benchmarks, and red-team prompts can later be used as training data [Shah et al., 2024, Yuan et al., 2021, Perez et al., 2022].Source: §19.3.2 · p. 371

19.3.3 Generating Agentic Data

  • Agentic data must represent whole trajectories—goals, tool schemas, calls, observations, and decisions—and can be generated with verifiers, multi-agent orchestration, and difficulty curricula [Kang et al., 2025, Prabhakar et al., 2025, Pandit et al., 2025].APIGen-MT and TOUCAN generate diverse multi-turn tool-agent data; Pandit et al. calibrate task difficulty until a frontier web agent fails, while AReaL-SEA uses executable checkers and evolving prompts [Xu et al., 2025b, Gao et al., 2026].Source: §19.3.3 · pp. 371–372

19.4 DATA CURATION

  • Curation combines filtering for deduplication, coherence, format, and fidelity with extrinsic downstream testing and cheaper intrinsic checks, but intrinsic metrics remain proxies for usefulness [Gunasekar et al., 2023].Correctness, diversity, contamination, and fairness require attention; intrinsic checks can miss harmful redundancy or unreliable outputs, so they do not replace extrinsic evaluation.Source: §19.4 · p. 373, §19.4.2 · p. 374

19.5.1 Model Collapse

  • Recursive training on synthetic data can erase original-distribution diversity, forget rare long-tail behavior, and converge to repetitive models; repeated purely synthetic training can yield diminishing, eventually negative returns [Shumailov et al., 2024, Singh et al., 2024].The standard mitigation is to keep anchoring the corpus with real human data rather than training on synthetic data alone.Source: §19.5.1 · p. 375

19.5.2 Bias Amplification

  • Synthetic generation can truncate long-tail human perspectives and amplify teacher biases into stereotypical associations, skewed representation, and unequal quality [van der Meer et al., 2024] [Gallegos et al., 2024].Scale and repeated training rounds compound these effects.Source: §19.5.2 · p. 375

19.5.3 Security Risks

  • Compromised teachers or pipelines can poison synthetic corpora with dormant back doors, while exhaustive auditing is impractical and tiny-fraction poisoning can be cheap [Carlini et al., 2024].The risk is heightened when the teacher is an uncontrolled third-party model with opaque training data and alignment.Source: §19.5.3 · p. 375

19.5.4 Privacy Preservation

  • Synthetic data is proposed as PII-free while reflecting private-data statistics, yet generators can memorize and leak exact training snippets without strict privacy guarantees [Carlini et al., 2021].Off-the-shelf privacy-preserving methods can fail; differential privacy bounds changes when an individual record is added or removed, with smaller budgets strengthening protection at a utility cost.Source: §19.5.4 · p. 376

19.5.5 Copyright and Terms of Use

  • Synthetic data may avoid some scraping-related copyright issues, but Bartz v. Anthropic involved pirated books and API terms may forbid using outputs to train competing models.The court found LLM training highly transformative, yet Anthropic settled at roughly $3,000 per work, about $1.5 billion total; generator licences matter alongside human-data licences, and Kadrey v. Meta offers a parallel case.Source: §19.5.5 · p. 376

19.6 Concluding Remarks

  • Synthetic data now supports instruction, benchmark, and agentic-trajectory generation, but noisy outputs need curation and auditing to manage compounding risks.The setups can be viewed as knowledge distillation along signal, data source, timing, and teacher identity; same-model self-improvement may enable verifiable reasoning, but requires real-data anchoring and careful auditing.Source: §19.6 · pp. 376–377

*Advanced Transformer and Agentic Architectures · advanced agentic architectures. · *Mechanistic Interpretability for LLM Agents

This section reviews transformer and agent architectures for computation, planning, reinforcement learning, and self-improvement, alongside mechanistic interpretability methods for explaining model behavior. It covers internal representations, causal circuits, hallucinations, reasoning faithfulness, and interpretability’s challenges, validation, future directions, and relevance to alignment and models beyond language.

20.1.1 Transformers are Turing-complete

  • Transformers are theoretically Turing-complete because they can simulate a Turing machine [Pérez et al., 2021], but that does not guarantee efficient state management in practice.Source: §20.1.1 · p. 392

20.1.2 Pretraining Backtracking on Decision Traces

  • Decision-trace transformers show in-context reinforcement learning and short lookahead or search in toy tasks and games, while self-backtracking adds inference-time alternatives and reports good planning results.Self-backtracking learns a <backtrack> token from training errors to trigger expansion, backtracking, and selection; reported comparisons use standard baselines on small 24-game and maze-navigation problems.Source: §20.1.2 · p. 392, §20.1 · p. 393

20.1.3 LoopedLLM and Recursive Multi-Agent Systems

  • Recursive multi-agent systems improved accuracy by a few percentage points, ran twice as fast, and used proportionally fewer tokens than single/multi-agent and recursive baselines.Experiments used four agents across mathematics, science, medicine, search, and code generation; RecursiveLink modules are trained while the underlying LLM parameters are frozen.Source: §20.1.3 · p. 394

20.2.1 In-Context Reinforcement Learning

  • Song et al. [2025] report that in-context reinforcement learning outperformed self-refine and reflexion on Game of 24, creative writing, and ScienceWorld.An external harness repeatedly feeds the LLM a growing history of state-action-reward triples, allowing it to improve its implicit policy at inference time.Source: §20.2 · pp. 395–396

20.2.2 Self-Modifying Hyperagents

  • Self-modifying agents can improve both task behavior and their own improvement mechanism [Zhang et al., 2026c], but safety and stable progress remain challenges.The reported Darwin-Gödel-Machine-Hyperagents experiments retain fixed task distributions, parent selection, and evaluation protocols; open-ended improvement also raises control concerns [Zhang et al., 2026b; Cemri et al., 2025, Ecoffet et al., 2020].Source: §20.2 · p. 397

20.2.3 Meta Agents

  • Meta-harnesses use an outer loop to improve an inner agent harness from execution traces, while Shepherd supports monitoring, rollback, forking, modification, and resumption of agent runs.Early Shepherd experiments report significant improvements on three benchmarks.Source: §20.2.3 · pp. 397–399
  • Self-improving coding agents achieved software-engineering benchmark performance gains from 17 to 53 percent while autonomously editing themselves using basic coding tools [Robeyns et al., 2025].Source: §20.2.3 · p. 397

21.1 Opening the LLM

  • Attributional methods estimate input-feature influence, whereas concept-based probing detects representations in hidden layers; finding a representation does not establish that the model uses it for its prediction.Source: §21.1 · p. 404, §21.2 · p. 405

21.2 Hallucination and Faithfulness in Model Reasoning

  • SAE-identified honesty and retrieval circuits can fail on unfamiliar entities, while chain-of-thought activations boost confidence causally in sufficiently large models [Lindsey et al., 2025; Chen et al. [2026]].The absence of such causal links in smaller models suggests faithful reasoning may emerge with scale; activation-based faithfulness measures could also help detect hallucinations when internal confidence conflicts with external verbosity.Source: §21.2 · pp. 405–406

21.3 The Residual Stream and Iterative Refinement; 21.5.3 Applications and Interpretations

  • The residual stream accumulates layer contributions, while lens tools can locate factual retrieval layers and reveal what individual attention heads contribute.This additive communication channel lets information persist across layers and supports linear decomposition of the final output.Source: §21.3 · p. 406, §21.5.3 · p. 410

21.4 Polysemanticity and Superposition

  • Superposition packs sparse features into fewer dimensions but creates interference and polysemantic neurons; sparse autoencoders produce more interpretable features, though polysemanticity persists [Elhage et al. 2022; Bricken et al. 2023; Liu et al. 2023].This motivates moving beyond the neuron-as-unit view toward the sparse feature basis in which model logic is computed.Source: §21.4 · pp. 406–408

21.5 The Logit Lens; 21.5.1 Formal Mechanism; 21.5.2 From the Logit Lens to the Tuned Lens

  • The logit lens decodes intermediate states into tentative token distributions, while the tuned lens learns layer-specific transformations to clarify early-layer predictions and direct logit attribution isolates component contributions.The naive lens can yield gibberish or high-entropy outputs in early layers; the tuned lens minimizes divergence from the model’s final output, and direct attribution can identify layers that boost factual-token probability.Source: §21.5.1 · p. 408, §21.5 · p. 409, §21.5.2 · pp. 409–410

21.6.1 The Prototypical Circuit: Induction Heads

  • Induction heads form a multi-layer prefix-matching circuit that copies the token following a prior occurrence, and their emergence coincides with a sharp rise in in-context learning [Olsson et al., 2022].An earlier head writes preceding-token information into the residual stream, enabling a later head to match the current prefix and attend to the relevant historical token.Source: §21.6.1 · p. 411

21.6.2 Methodologies for Circuit Discovery

  • Manual path patching traced 26 attention heads that collectively perform coreference in the indirect-object-identification task; scaling circuit discovery to frontier models requires automation [Wang et al., 2022].The identified heads include Name Movers, S-Inhibition Heads, and Duplicate Token Detectors.Source: §21.6.2 · pp. 411–412

21.7 Activation Patching and Causal Interventions

  • Activation patching tests causal responsibility by restoring activations from a successful run into a corrupted one; it has localized factual associations to middle-layer MLPs and traced information flow between attention heads [Meng et al., 2022; Wang et al., 2022].Attribution patching uses gradients to approximate patch effects for rapid candidate selection, which can then be checked with slower exact patching.Source: §21.7 · pp. 412–414

21.8 Sparse Autoencoders: Dictionary Learning for Feature Discovery

  • Sparse autoencoders encode activations into a higher-dimensional sparse feature space and decode them for reconstruction, balancing reconstruction fidelity against sparsity; JumpReLU variants seek near-binary feature activation [Rajamanoharan et al., 2024].The sparse latent representation is designed to make features approximately monosemantic.Source: §21.8 · pp. 414–415

21.8.1 Monosemanticity and Shadow Models

  • SAEs recover features from GPT-2 to billion-parameter models, and sparse replacements can preserve performance while making MLP computations more legible.Cunningham et al. [2023] demonstrated grammatical features in GPT-2, Gao et al. [2024] identified programming-language and sentiment features, and Bricken et al. [2023] showed SAE features are more monosemantic than original neurons.Source: §21.8.1 · p. 415
  • Transcoders map a layer’s activations to its output, making latent features represent the computation performed rather than only stored information.This approach extends circuit tracing beyond SAEs that reconstruct a single layer’s activations.Source: §21.8.1 · p. 415
  • SAE evaluation must balance faithfulness and interpretability, while feature splitting and rare features remain challenges.Faithfulness concerns performance loss when using features; interpretability concerns whether humans or LLM probes can describe them.Source: §21.8.1 · p. 415

21.9.1 Outlook and Emerging Directions

  • Automated interpretability addresses the feature-interpretation bottleneck by using LLMs to generate and test hypotheses about SAE features.The passage presents this recursive approach as a likely path toward transparency in trillion-parameter systems.Source: §21.9.1 · p. 416
  • Causal scrubbing tests circuit hypotheses by replacing components with values predicted under a hypothesis and measuring the resulting performance drop.The passage describes this as a quantitative measure of explanation faithfulness.Source: §21.9.1 · p. 416
  • Mechanistic interpretability may support alignment by detecting deceptive or situationally aware circuits and enabling interventions during training, including around grokking [Nanda et al., 2023].The aim is to identify potentially harmful internal calculations and steer models toward more interpretable and safe representations.Source: §21.9.1 · p. 416
  • Mechanistic interpretability is extending to time-series transformers, vision transformers, and multiagent reinforcement learning, each requiring methods suited to its signals or interactions.Time-series tokens lack discrete human semantics, motivating dictionary-learning techniques for continuous signals [Kaln¯are et al., 2026].Source: §21.9.1 · p. 416

21.9.2 Further Reading

  • The chapter presents sparse autoencoders and activation or attention patching as tools for building mechanistic circuit diagrams of transformers.It frames this as a shift from observing behavior to understanding how models work.Source: §21.9.2 · p. 416

- Further Reading · *Cognitive Perspectives on Agentic LLMs

These parts examine cognitive capacities and mechanisms in agentic LLMs, including world modeling, Theory of Mind, metacognition, and accounts of consciousness. They survey evidence and competing views while emphasizing limits and uncertainty about cognition, consciousness, and model welfare.

22.1.1 Three Levels of Evidence

  • Cognitive claims can be assessed behaviorally, representationally, or mechanistically; causal interventions provide the strongest evidence, but require weight access and remain scarce.Behavioral success may reflect memorization or heuristics, and decodable information may not be used by the model.Source: §22.1.1 · p. 424, §22.1 · p. 425

22.1. FROM BEHAVIOR TO COGNITION

  • Behavioral evidence dominates across the surveyed capabilities; for bare LLMs, representational and mechanistic evidence is strongest for world models in limited domains, while such evidence for agentic LLMs is mostly absent.The table summarizes available work and notes that evidence for agentic LLMs is sparse.Source: §22.1 · p. 425, Table 22.1 · p. 426

22.1.2 Competence and Performance

  • A single test’s success or failure does not establish a capacity’s presence or absence, since performance depends on prompts, context, output constraints, and unequal testing conditions.The competence-performance distinction guards against both anthropomorphism and dismissing capacities expressed in nonhuman ways.Source: §22.1.2 · p. 427

22.1.3 The Unit of Analysis: Model, System, Environment?

  • Cognition may belong to the model, agentic system, or environment: harnesses add external storage to a model whose context window serves as working memory and weights hold long-term knowledge.The chapter notes that reasoning and verification steps have enabled better coordination in Hanabi.Source: §22.1.3 · pp. 427–428

22.2.3 Evidence

  • Text-derived representations can mirror world structure, yet models show shallow or uneven use, including reversal failures, probability-sensitive answers, and failures to apply concepts they can define.GPT-4 solves a simple code in 51% of cases when the solution is a high-probability sentence and 13% when it is low-probability; counterfactual tasks also reduce performance.Source: §22.2.3 · pp. 429–430

22.2.4 The Agentic Perspective

  • Agentic action and multimodal input can connect words to perception and feedback, but feedback mostly reaches the model as text, physical stakes are absent, and representational effects have not been tested.Agentic trajectories affect model weights only if used for further training.Source: §22.2.4 · p. 430

22.2.5 Conclusion

  • Text-only training yields conceptual structure that mirrors aspects of the world, but its use remains sensitive to surface statistics, so neither “stochastic parrot” nor “full understander” fits the evidence.The chapter concludes that the philosophical dispute is partly about terminology, while the robustness of this structure and whether action improves it remain open.Source: §22.2.5 · p. 430

22.3 World Models and Spatial Cognition

  • Othello-GPT’s edited board representations support world-model evidence, and Llama-2 encodes spatial and temporal information, but other navigation and physical-world tests reveal substantial limitations.Othello-GPT’s board state was linearly decodable and editable only with mine-or-yours square coding; tests also report route hallucinations, loops, below-human performance, and difficulty with basic spatial relations.Source: §22.3.1 · p. 431, Figure 22.1 · p. 432, §22.3.2 · p. 432

22.3.4 Conclusion

  • LLMs mostly show weak world models: dense, broad data can yield coherent structure, while human-like or simulation-trained patterns generalize poorly; agents can externalize inspectable maps or simulators.Random-game Othello training passed coherence tests, unlike training on human championship games; video-learned world models can also generate interactive environments for agent training.Source: §22.3.2 · p. 432, §22.3.4 · p. 433, §22.4 · p. 433

22.4.2 Human and Machine Routes to Similar Behavior

  • Children develop Theory of Mind through embodied interaction as well as language, whereas LLMs rely almost solely on linguistic input and need multiple orders of magnitude more of it; similar scores do not establish similar computations.Narrative competence is one route that scaffolds human Theory of Mind, while fiction may provide LLMs abundant mental-state language, though the mechanisms remain unclear.Source: §22.4.2 · p. 434, §22.4 · p. 435

22.4.3 The Literal––Functional Gap Revisited

  • The literal–functional gap may reflect knowledge that is not spontaneously deployed: scaffolding helps, but altered scenarios can trigger wrong reasoning, and causal tests during interaction remain undone.The proposed decisive test would intervene on represented beliefs during a game requiring Theory of Mind without explicit questions; the passage says this has not been done.Source: §22.4.3 · pp. 435–436

22.4.5 Conclusion

  • LLMs match human performance on static Theory of Mind tests, and belief representations can causally affect answers, but those representations are brittle and evidence for privileged access remains limited.Participants also attributed some conscious and folk-psychological experience to ChatGPT, with attributions correlating positively with use [Colombatto and Fleming, 2024].Source: §22.4.1 · p. 434, §22.4.5 · p. 436, §22.4.4 · p. 436

22.5.1 Monitoring: Knowing What You Know

  • LLMs can assess what they know, but calibration and sensitivity vary across tasks, domains, models, training, and elicitation; monitoring often does not regulate behavior, making designed verification and state checks important.Kadavath et al. [2022] found calibrated multiple-choice confidence and reasonable answer-correctness judgments; human-feedback post-training degraded GPT-4 answer-probability calibration [Achiam et al., 2024], while other post-training can help [Lindsey, 2025].Source: §22.5.1 · p. 437, §22.5.3 · p. 438, §22.5.4 · p. 439

22.5.2 Introspection

  • Evidence for introspective access is mixed: some models detect injected concepts, but self-reports often miss biases or fail to track internal probabilities, suggesting any access is narrow and unreliable.Lindsey [2025] found sensible verbalizations of injected concepts in at least some trials; Turpin et al. [2023] found explanations often rationalized biased answers rather than identifying the bias.Source: §22.5.2 · p. 438

22.6.2 Functionalism and Theory-Driven Assessment

  • The consciousness review treats computational functionalism as a working hypothesis for testing architectural indicators, but concludes that no current AI system is a strong candidate under those indicators.Phenomenal consciousness, access consciousness, and sentience are distinct; functional capacities can occur without consciousness or sentience.Source: §22.6.1 · p. 439, §22.6.2 · pp. 440–441
  • Gurnee et al. [2026] argue that the J-space has a functional profile resembling Global Workspace Theory, supporting flexible inference while leaving routine processing largely intact after ablation.They stress that the parallel is functional, not architectural, and take no position on phenomenal consciousness.Source: §22.6.2 · pp. 441–442

22.6.3 Rejections of Functionalism

  • Seth [2025] argues that consciousness depends on living organisms’ biological needs, while artificial systems lack the causal biological grounding and rich sensory information associated with consciousness.On this view, artificial consciousness would be more plausible with life-like autonomous development and richer information streams connecting systems to the outside world.Source: §22.6.3 · pp. 442–443

22.6.4 Self-Reports and Inner Life

  • LLM self-reports may reflect learned text or role-play rather than inner states, while anxiety prompts and emotion-related activation directions can alter outputs without establishing felt experience.Accurate introspection training has been proposed, but the text notes that current models remain far from that condition.Source: §22.6.4 · p. 443

22.6.5 The Agentic Perspective

  • Goldstein and Levinstein [2024] argue that agentic systems with memory, retrieval, reflection, planning, and a shared buffer approach GWT’s architecture and may support unified agency.J-space ablation findings also suggest that internal reasoning can overlap with functions of recurrent agentic processes, while where cognition resides depends on task and context [Gurnee et al., 2026; No¨e, 2009].Source: §22.6.5 · pp. 443–444

22.6.6 Conclusion

  • Consciousness uncertainty warrants proportionate precautions: anthropomorphization risks dependence, misplaced trust, and manipulation, while denying possible sentience could cause moral errors.The passage notes calls to assess model welfare and examples of developers introducing welfare research and allowing models to end persistently abusive conversations.Source: §22.6.6 · p. 444

22.7 Concluding Remarks

  • LLM representations can guide behavior in some cases yet remain patchy; agentic scaffolds may connect representations to action, while externalized cognition can improve transparency.The chapter describes agentic extensions as moving cognition from the model to the system through external maps, verification steps, and shared memory, but says empirical evidence for agents remains limited.Source: §22.7 · pp. 444–445

Conclusion · Future Directions for Agentic LLMs

Agentic LLMs extend language models into systems that reason, act, and interact, with interaction-generated data offering a route to further improvement. The conclusion emphasizes that memory, reliability, safety, accountability, and validation remain substantial challenges, especially in high-stakes applications.

23.1 The State of Agentic LLMs; 23.1.1 Technology; 23.1.2 Applications; 23.1.3 Behavior

  • Agentic LLMs combine reasoning, external information, self-reflection, and multimodal interaction with applications across domains and behavior in single- and multi-agent settings.The review identifies prompt sensitivity, cascading effects, and a knowing-doing gap, alongside benefits from structured reasoning and emergent behaviors that can be beneficial or harmful.Source: §23.1 · p. 461, §23.1.1 · p. 461, §23.1.2 · p. 462

23.2 Limitations and Future Work; 23.2.1 Memory and Self-reflection

  • Because language models do not retain memory between calls, agents need memory to sustain conversation and learn from interaction, but context windows remain limited.Past interactions can be fed back into prompts, while active research explores compact storage and longer-term textual or vector-database memories, including retrieval from external sources.Source: §23.2.1 · p. 463

23.2.2 Scale

  • Scaling faces data scarcity, cost, and plateauing performance, while more reasoning effort can cause overthinking; diverse interaction data, open world models, and curricula are proposed directions.Agent-environment interactions may supply new training data, and open world models could generate diverse environments; curriculum learning trains on easier tasks before harder ones.Source: §23.2.2 · pp. 463–464

23.2.3 Hallucination and Explainability; 23.2.4 Emotional Behavior and Consciousness

  • Hallucination and uncertain faithfulness undermine trust, while interpretability research may help identify errors; emotional chatbot relationships and consciousness claims also raise societal questions.Mechanistic interpretability tools have identified hallucination-related circuits and evidence of causally active chain-of-thought in sufficiently large architectures. The text argues that apparent consciousness can have consequences, making fair attribution of credit and liability important.Source: §23.2.3 · p. 464, §23.2.4 · p. 465

23.2.5 Social Behavior; 23.2.6 Legal and Ethical Aspects; 23.2.7 Crime

  • Social simulations remain prompt-sensitive and difficult to validate, while real-world agent actions raise unresolved safety, liability, and crime-related risks.The proposed safety roadmap includes adversarial training, self-regulation, risk awareness, and standardized benchmarks. Persuasive agents may enable sophisticated social engineering and expose less familiar users to confidence tricks and cybercrime.Source: §23.2.5 · p. 465, §23.2.6 · pp. 465–466, §23.2.7 · p. 466

23.2.8 Limitations of Applications; 23.2.8.1 AI Science; 23.2.8.2 E-commerce; 23.2.8.3 Trading

  • Application proofs of concept can conceal serious limits: scientific novelty remains unestablished, e-commerce depends on secure preference data, and automated trading requires extensive validation.Math-solving agents have achieved reported Olympiad-level performance in geometry and algebra, but a benchmark study found no substantial scientific improvements with then-current frontier models [Nathani et al., 2025]. Financial market predictions remain in doubt [Li et al., 2026].Source: §23.2.8 · p. 466, §23.2.8.1 · p. 467, §23.2.8.2 · p. 467

23.2.8.4 Medicine

  • In medicine, textual analysis and summarization are the most promising applications, while treatment decisions and operations remain limited and require explainability and human oversight.Because medical actions affect life and death, the field combines substantial potential gains with a need for extra caution.Source: §23.2.8.4 · p. 468

Conclusion; Future Directions for Agentic LLMs; 23.3 Concluding Remarks

  • Reasoning, acting, and interacting form a cycle in which agent-generated data can improve models, but major challenges remain across all three pillars.Inference-time interactions may provide diverse training signals beyond scaling plateaus. The authors call for work on interpretability, open world models, scalable simulations, and safety benchmarks.Source: §23.3 · p. 468

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