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Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems
Tao Feng, Pengrui Han, Guanyu Lin, Ge Liu, Jiaxuan You
TL;DR
LLMs remain constrained when incorporating massive external knowledge because retrieval is limited by context windows and top-K raw chunks. Thought-Retriever builds a validated, self-evolving memory of intermediate thoughts and retrieves it with external data, outperforming baselines across tasks while showing improvement through interaction.
Problem
Existing LLMs and retrieval-augmented systems struggle to incorporate massive external knowledge because context windows limit the raw chunks they can retrieve.
Method
Thought-Retriever retrieves external data and historical thoughts, then filters meaningless and redundant thoughts before storing validated novel thoughts in memory.
Results
Thought-Retriever outperforms state-of-the-art baselines, with at least 7.6% higher average F1 and 16% higher win rate across various tasks.
Takeaways & Limitations
Performance increases with more thoughts, and the framework can use deeper thoughts for more abstract queries while serving as a persistent long-term memory module.
Takeaways & Limitations
The experiments and AcademicEval dataset primarily use AI-related papers, which may limit generalizability.
Abstract
from arXiv · showhide
Large language models (LLMs) have transformed AI research thanks to their powerful internal capabilities and knowledge. However, existing LLMs still fail to effectively incorporate the massive external knowledge when interacting with the world. Although retrieval-augmented LLMs are proposed to mitigate the issue, they are still fundamentally constrained by the context length of LLMs, as they can only retrieve top-K raw data chunks from the external knowledge base which often consists of millions of data chunks. Here we propose Thought-Retriever, a novel model-agnostic algorithm that helps LLMs generate output conditioned on arbitrarily long external data, without being constrained by the context length or number of retrieved data chunks. Our key insight is to let an LLM fully leverage its intermediate responses generated when solving past user queries (thoughts), filtering meaningless and redundant thoughts, organizing them in thought memory, and retrieving the relevant thoughts when addressing new queries. This effectively equips LLM-based agents with a self-evolving long-term memory that grows more capable through continuous interaction. Besides algorithmic innovation, we further meticulously prepare a novel benchmark, AcademicEval, which requires an LLM to faithfully leverage ultra-long context to answer queries based on real-world academic papers. Extensive experiments on AcademicEval and two other public datasets validate that Thought-Retriever remarkably outperforms state-of-the-art baselines, achieving an average increase of at least 7.6% in F1 score and 16% in win rate across various tasks. More importantly, we further demonstrate two exciting findings: (1) Thought-Retriever can indeed help LLM self-evolve after solving more user queries; (2) Thought-Retriever learns to leverage deeper thoughts to answer more abstract user queries.
1 Introduction and Related Work
Existing approaches remain limited when LLMs must incorporate extremely large external knowledge bases. Thought-Retriever addresses this by organizing validated historical responses into a self-evolving thought memory and reports gains across multiple tasks.
- Long-context LLMs expand working memory but do not fundamentally solve interaction with arbitrarily large external knowledge.
- Thought-Retriever is an LLM-agnostic retrieval framework that transforms historical responses into thoughts and organizes them for future queries.
- Thought-Retriever filters meaningless and redundant thoughts before incorporating novel, high-quality thoughts into long-term memory.
- 7.6% average F1 increase and 16% win-rate increase are reported across various tasks against state-of-the-art baselines.
- The paper contributes AcademicEval, a benchmark requiring accurate use of extensive context from real-world academic papers.
2 Thought-Retriever: Effectively Equip LLMs with External Knowledge
Thought-Retriever defines validated thoughts as query-conditioned abstractions and retrieves them alongside raw data. Its pipeline generates answers, evaluates and deduplicates thoughts, and stores only confident, novel additions.
- 2.1 Preliminaries: A thought is a query-conditioned, abstractive, validated knowledge unit derived from a query, answer, and retrieved context.
- 2.1 Preliminaries: Thought provenance recursively maps each thought to the raw chunks underlying its retrieved components, preserving factual grounding.
- 2.2 Motivating Examples: Standard RALMs can achieve perfect precision yet lower recall when their context window cannot hold all chunks required for a query.
- 2.2 Motivating Examples: Unlike static hierarchical summaries, Thought-Retriever creates interaction-conditioned thoughts that flexibly balance low-level facts and high-level information.
- 2.3 Thought-Retriever Framework: The framework retrieves from external knowledge and thought memory, generates an answer, then produces a thought and binary confidence score.
- 2.3 Thought-Retriever Framework: A generated thought enters memory only when it is meaningful and non-redundant according to confidence and similarity checks.
3 AcademicEval: New Benchmark for Long-Context LLM Understanding
AcademicEval is introduced to assess long-context memory use through academic-paper tasks. Its datasets evaluate single- and multi-paper abstract generation and related-work generation from heterogeneous paper segments.
- AcademicEval addresses limitations of existing long-context benchmarks using academic papers collected from arXiv and provides two datasets.
- AcademicEval-abstract: AcademicEval-abstract evaluates abstract generation from one or multiple papers with the original or expert-generated summaries as references.
- AcademicEval-related: AcademicEval-related tests whether an LLM can connect heterogeneous memory segments to write a target paper’s related-work section.
- The benchmark overview reports task types, average input length, and number of cases for its datasets.
4 Experiment
Experiments evaluate Thought-Retriever on AcademicEval, two public datasets, factual transfer from other LLMs, abstraction-aware retrieval, and scaling with thought-memory size. Across these settings, it improves retrieval and generation performance while showing self-evolution and deeper retrieval for more abstract queries.
- Experiment Setup: Experiments cover AcademicEval, GovReport, WCEP, factual transfer from other LLMs, abstraction-aware retrieval, and varying numbers of thoughts.The public datasets test lengthy government reports and clusters of news articles, while additional experiments examine expert-LLM transfer and thought depth.
- Factual Knowledge Retrieval: Thought-Retriever significantly outperforms most baselines across AcademicEval and public benchmarks, with average gains of at least 7.6% in F1 and 16% in win rate.The comparisons use F1 and AI-evaluator win rate under shared baseline settings.
- Factual Knowledge Retrieval: Thought-Retriever can quickly learn from other LLMs with specialized roles and background knowledge when original facts are unavailable.The factual-transfer experiment studies whether expert LLM interactions can help form useful thought memories.
- Qualitative Analysis: More abstract questions tend to retrieve information with higher abstraction levels, indicating that the method retrieves deeper thoughts for more abstract queries.Abstraction level assigns raw data level 1 and increases recursively when thoughts are generated from retrieved sources.
- New Findings: Increasing the number of thoughts produces increasing F1 scores on Abstract-multi and Related-multi, indicating self-evolution after more user queries.The authors describe this relationship as a new scaling law for agentic memory.
- Precision and Recall: Thought-Retriever improves recall while retaining moderately high precision when only 8 information chunks can be retrieved.Its thoughts combine information from multiple papers, increasing coverage without significantly compromising relevant retrieval.
5 Additional Related Works
Thought-Retriever is positioned against long-context, retrieval-augmented, and context-compression approaches by storing validated reasoning abstractions rather than raw observations. Its training-free, model-agnostic design targets efficient retrieval from persistent agent memory.
- LLM Agent Memory and Experiences: Agent memory frameworks store past observations or behaviors, whereas Thought-Retriever stores validated and deduplicated reasoning abstractions.This representation is described as more information-dense and retrieval-friendly for agentic systems.
- Long-context LLMs: Long-context methods expand the context window through larger models, fine-tuning, positional encoding, or context compression.These approaches address long-context processing by increasing or compressing the available working context.
- Retrieval-Augmented Language Models: Retrieval-augmented language models retrieve relevant information using embeddings, keyword search, rerankers, or retrieval-verification losses.The passage presents retrieval as a flexible alternative to expanding the context window.
- Context Compression for LLMs: Context-compression methods condense long inputs but may require extra training, fine-tuning, or inaccessible model internals.Thought-Retriever is contrasted as lightweight, training-free, model-agnostic, and compatible with open-source models and closed-source APIs.
6 Conclusion
The paper introduces Thought-Retriever as a memory module that generates and retrieves intermediate thoughts to use external knowledge beyond context limits. It also presents AcademicEval and reports improved performance, self-evolution through interaction, and deeper-thought use for abstract queries.
- Conclusion: Thought-Retriever dynamically generates and retrieves intermediate thoughts, enabling LLMs to use external knowledge beyond context limits.The framework is described as a lightweight, model-agnostic persistent memory module for LLM-based agents.
- Conclusion: AcademicEval benchmarks academic tasks including abstract and related-work generation using extensive context.The benchmark complements the algorithmic contribution by evaluating ultra-long-context use in academic settings.
- Conclusion: Thought-Retriever outperforms existing methods, evolves through interaction, and shows potential for real-world applications.The conclusion summarizes these outcomes without specifying a single benchmark or metric.
7 Limitations
The authors identify scope and evaluation boundaries: experiments focus mainly on AI papers and English, the benchmark depends on arXiv data quality, and real-world large-scale robustness remains insufficiently tested.
- Scope: Experiments and AcademicEval primarily use AI-related papers, which could limit the generalizability of the findings.The authors suggest extending evaluation to a broader range of disciplines.
- Language: Experiments and evaluations are conducted in English, potentially overlooking challenges and nuances associated with other languages.The authors propose multilingual datasets and evaluations for broader assessment.
- Data dependence: AcademicEval depends on the availability and quality of papers uploaded to arXiv.The benchmark is dynamically updated, so its usefulness assumes continued production of novel, high-quality papers.
- Deployment: Robustness, scalability, and adaptability to real-world, extremely large-scale applications have not yet been fully tested.The authors report effectiveness in their experiments but leave broader deployment evaluation for future work.
A.1 Dataset Documentation
AcademicEval documents datasets and prompts for evaluating long-context academic summarization and related-work generation. The documentation covers data formats, retrieval and generation procedures, expert-produced labels, query formation, evaluators, retrievers, and backbone adaptability.
- Dataset format: AcademicEval-abstract contains single- and multiple-paper summarization cases with abstracts and conclusions removed from the inputs.Abstract-single uses the original abstract as the label, while Abstract-multi uses an expert-LLM summary of five abstracts.
- Abstract-Single: Abstract-single retrieves information from a paper’s main content, generates an abstract, and compares it with the original abstract.The documented procedure uses a retrieval prompt followed by a generation prompt.
- Abstract-Multi: Abstract-multi retrieves information from five papers, generates a combined abstract, and evaluates it against an expert-LLM summary of their abstracts.The summary label is produced using the dedicated Abstract Multi Ground Truth Prompt.
- Related-Multi: Related-work generation conditions on a paper’s abstract and retrieves materials from real citations and random papers.The generation prompt asks the LLM to write a related-work section introducing the paper.
- Benefits and Contributions: AcademicEval uses continuously updated arXiv papers, reducing risks from static-benchmark overfitting and label leakage while supporting self-adaptability evaluation.The benchmark also supports expert-generated labels without additional manual labeling cost.
- Prompts and baselines: The documented evaluation setup includes template- and LLM-based query formation, AI-based win-rate judging, heuristic and neural retrievers, and advanced retrieval or compression baselines.The AI evaluator compares generated answers with a ground-truth answer, while the listed retrievers include BM25, TF-IDF, Contriever, DPR, DRAGON, Qwen3-Embed-8b, IRCoT, and RECOMP.
- Backbone adaptability: Thought-Retriever remains adaptable across LLM backbones, showing consistent top performance on Qwen-7B and Llama-3-70B across multiple tasks.The passage attributes this adaptability to the framework not being tailored to a specific model.
F Example Outputs Comparison of Different Methods
Thought-Retriever produces more comprehensive and coherent academic-paper abstracts than the compared retrieval and long-context methods. The framework is also presented as adaptable to different LLM backbones and as a basis for real-world academic assistance.
- Example comparisons: Thought-Retriever’s abstract was judged best aligned with the original, capturing key points and practical implications for NLP evaluation.The expert comparison highlighted its coverage of leaderboard limitations, energy efficiency, model size, inference latency, and reporting transparency.
- Backbone adaptability: Table 7 compares Thought-Retriever with baselines on Abstract-single and Abstract-multi using Qwen-7B and Llama-3-70B.The caption states that Thought-Retriever consistently delivers the best results across these model backbones and tasks.
- Example comparisons: DPR, TF-IDF, and long-context baselines produced less comprehensive or differently focused abstracts than Thought-Retriever.The comparison describes DPR and the long-context model as less comprehensive, while TF-IDF diverged most from the target argument.
- Applications and future work: The discussion proposes applications in customer service, healthcare, and legal advisory, while identifying scalability, human-like reasoning, and ethics as future challenges.The stated future concerns include computational efficiency, nuanced reasoning, privacy, security, and societal norms.
- Real-world application: Arxiv Copilot applies Thought-Retriever to researcher profiles, personalized research trends, and interactive answer feedback.Its interface includes profile generation, trend and idea retrieval, and preference-based selection between two answers.
I Further Results on QA and Reasoning Task
Further experiments evaluate Thought-Retriever on QA, reasoning, retrieval, and robustness settings. Across these studies, it remains effective with different retrievers and long-context comparison methods, while filtering redundant thoughts and tolerating noisy ones.
- QA and reasoning: Thought-Retriever consistently outperformed all baselines on LooGLE QA and reasoning tasks.The evaluation reports strong performance in both question-answering and reasoning accuracy.
- Thought filtering: Thought-Retriever’s thought pool filters semantically redundant candidates using cosine similarity to preserve unique thoughts.This mechanism is intended to maintain diversity and reduce duplication in stored memories.
- Thought diversity: The AcademicEval thought pool had an average pairwise cosine similarity of 0.32 across repeated samples, indicating semantic diversity.The analysis sampled 50 thoughts and repeated the calculation 10 times.
- Retriever and baseline comparisons: Thought-Retriever remained effective with NV-Embed and in comparisons against RECOMP on Abstract-single and Abstract-multi.These experiments support compatibility between the framework and alternative retrievers, as well as effectiveness against a compressor-based long-context baseline.
- Robustness: F1 scores of 0.207 without relevant thoughts and 0.208 with raw chunks only indicate limited sensitivity to extremely low-quality thoughts.The robustness experiment injected irrelevant thoughts or omitted thoughts entirely on Related-multi.
N Computational analysis
The computational analysis frames retrieval as relatively inexpensive compared with LLM inference and relates inference cost directly to the number of retrieved tokens. The supplied material provides no additional quantitative computation result.
- Cost breakdown: Retrieval operations can run on the CPU, while most RAG computation is allocated to LLM inference.The analysis distinguishes retrieval compute from generation compute when discussing system efficiency.
- Cost breakdown: Thought-Retriever maintains nearly identical retrieval compute costs across experiments by using the same retriever.Its inference cost instead varies with the number of retrieved tokens.
O Human evaluations on the quality of retrieved thoughts
Human and comparative evaluations examine thought quality, robustness to irrelevant memories, user preferences, and efficiency against hierarchical long-context retrieval. The results support reliable thought filtering and broad evidence coverage under fixed context limits.
- Human evaluation: On Abstract-single, generated thoughts matched human judgment 96% of the time.Ten volunteers evaluated thoughts independently, with each thought reviewed by five annotators.
- Robustness: F1 scores of 0.207 without relevant thoughts and 0.208 with raw chunks only show robustness to noisy or irrelevant thoughts.The two settings produced nearly identical performance on Related-multi.
- Real-world validation: Approximately 75% of 500 Arxiv Copilot users preferred answers incorporating historical thoughts and original data chunks.The deployment compared responses based only on original chunks with responses using both chunks and thoughts.
- Comparison with MemWalker: Thought-Retriever consistently outperformed MemWalker in F1 and win rate, including 0.290 versus 0.268 on Abstract-single.MemWalker also incurred high latency because each query required sequential tree-navigation inference.
- Comparison with Oracle: Thought-Retriever exceeded the Oracle baseline on AcademicEval, reaching F1 0.290 versus 0.278 on Abstract-single.The comparison attributes this result to compressed, information-dense thoughts fitting more relevant details within the fixed context window than verbose raw chunks.
- Retrieval granularity: HRALM achieved slightly higher precision than Thought-Retriever on Abstract-single, but its rigid hierarchy had low recall on cross-cluster Rel-multi queries.The reported precision was 0.82 versus 0.80, while Rel-multi recall was 0.52 for HRALM.
Q Causal Analysis of Self-Evolution on Held-out Sets
Thought-Retriever’s held-out evaluation separates thought accumulation from testing and compares evolved memory with cold start on unseen queries. The results show transferable gains from accumulated thoughts, while the broader analysis also documents efficiency and increasingly abstract query handling.
- Evaluation Design: 50% of queries form an Evolution Set for accumulating thoughts, while the remaining 50% comprise a strictly unseen Held-out Test Set.The held-out design prevents test queries from contributing to the memory used during evaluation.
- Evaluation Design: The cold-start baseline evaluates test queries with an empty thought memory, whereas the evolved condition uses thoughts accumulated from the Evolution Set.This comparison isolates the effect of prior memory evolution on held-out performance.
- Held-out Results: 6.4% relative F1 improvement on Abstract-multi follows from pre-accumulated thoughts compared with the cold-start baseline.The reported held-out result is presented as causal evidence that accumulated thoughts transfer to novel, unseen queries.
- Efficiency: Thought-Retriever achieves a 3× speedup over IRCoT on Abstract-multi, reaching 3.20s per query versus 10.50s.The difference is attributed to retrieving pregenerated thoughts rather than repeating iterative retrieval cycles at inference time.
- Efficiency: Thought-Retriever achieves an 8× speedup over Nous Hermes-32k on Gov Report, reaching 3.50s versus 28.50s per query.Retrieving concise thoughts reduces the input token load relative to processing ultra-long contexts.
- Abstract Query Handling: Retrieving Thought T2 lets the system apply a RAG-plus-relevance-check principle to a query that does not explicitly mention hallucinations or RAG.The case study presents this as handling a more abstract, application-oriented task through accumulated thoughts.