Generalizability of Large Language Model-Based Agents: A Comprehensive Survey

1 Introduction

• Agent generalizability means maintaining high performance across varied instructions, tasks, environments, and domains, especially scenarios absent from fine-tuning data.The introduction links this reliability to stakeholder trust and highlights particular risks in high-stakes domains.Source: §1 · pp. 2–3

1.1 Challenges and Contributions

• A central obstacle is the lack of a standardized framework defining the boundaries of agent generalizability.The survey proposes a hierarchical domain-task ontology to specify the scope of claims and enable fair comparisons.Source: §1.1.1 · pp. 3–4

1.1.1 Challenges.

• A second challenge is establishing comprehensive measurement, theoretical guarantees, and practical improvement methods for agent generalizability.The survey points to unbiased benchmarks and training-data diversity, as well as improvements to individual components and their coordination.Source: §1.1.1 · p. 4
• A generalizable framework does not guarantee a generalizable agent, which must perform reliably across varied and unseen scenarios after fine-tuning.The survey distinguishes a framework that supports performance when paired with scenario-specific fine-tuning data from an agent that generalizes across scenarios.Source: §1.1.1 · p. 4

1.1.2 Our Contributions.

• Generalizability is the ability to maintain consistently high performance across varied instructions, environments, tasks, and domains, especially those differing from fine-tuning data.The survey proposes a hierarchy spanning broad domains, sub-domains, and fine-grained tasks to clarify scope and support standardized comparisons.Source: §1.1.2 · pp. 4–5
• The survey reviews datasets, task setups, and evaluation metrics, analyzes their limitations, and categorizes improvement methods by target: the backbone LLM, agent components, or their interactions.It identifies the lack of a unified, systematic treatment integrating measurement and improvement.Source: §1.1.2 · p. 5

1.1.1 Challenges.

• The survey distinguishes generalizable frameworks from generalizable agents and examines how methodological generalizability can be translated into consistent agent behavior across scenarios.It argues that frameworks may perform robustly when paired with scenario-specific fine-tuning yet fail to yield agents that generalize beyond that data.Source: §1.1.1 · p. 4, §1.1.2 · p. 5

1.2 Distinctions from Related Surveys

• The survey addresses generalizability’s limited treatment in prior work and examines its importance across multiple stakeholder usage contexts.It identifies the absence of a formal definition, standardized benchmarks, and a comprehensive review of improvement methods.Source: §1.1.2 · p. 5, §1.2 · p. 6
• The survey analyzes generalizability across agent modules and their coordination, extending beyond the backbone LLM to perception, memory, and tools.It presents component-level analysis as a distinct contribution and a more holistic view of agent architecture.Source: §1.1.2 · p. 5, §1.2 · p. 6

2 LLM-based Agents and Ecosystems

• The section also considers how consumers, developers, providers, and regulators evaluate and influence agent generalizability.Source: §2 · p. 7

2.1 LLM-based Agent Architecture Overview

• The architecture follows a feedback loop: the agent processes instructions, plans and acts, then uses updated environmental observations to guide later decisions.The survey illustrates the loop with web search and household tasks, where actions change the environment and feedback arrives through results, sensors, or user responses.Source: §2.1 · p. 7

2.1.1 Components of LLM-Based Agents.

• A backbone LLM interprets instructions, plans, coordinates components, and issues natural-language outputs or function calls; perception, memory, and tool units enable environmental interaction.The units respectively process observations, maintain session or external knowledge, and invoke APIs, software commands, or physical actuators.Source: §2.1.1 · pp. 7–8

2.1.2 Limitations in LLM-Based Agent Architectures and Future Directions.

• Insufficient communication can cause inconsistent behavior: observation compression may lose details and lead to unsafe or inaccurate actions [117].Compression is intended to reduce context length and improve inference efficiency, but may leave the backbone LLM with an incomplete view of the environment.Source: §2.1.2 · p. 8
• Absent communication about component intents and internal strategies, including summarization heuristics or compression levels, prevents coordinated adaptation and leads to suboptimal agent behavior; incompatible memory representations or objectives may hinder retrieval and use [44] [129].Source: §2.1.2 · p. 8
• Sharing components’ intents or assumptions could let the backbone adapt its plans to information loss and prioritize generalizability or accuracy according to task needs.For example, a perception unit could signal that it supplied abstract visual summaries, prompting the backbone to account for missing detail.Source: §2.1.2 · p. 9
• The backbone may struggle to orchestrate heterogeneous components with incompatible specifications, such as provider-specific tools, perception representations, and memory query parameters.The section illustrates this with components from different providers, including tools organized by interaction type and perception outputs represented as semantic graphs [8] [117] [129].Source: §2.1.2 · p. 9
• Prompting the backbone about component functions can add latency and be prompt-sensitive, while fine-tuning may be blocked by access or data limits or risk catastrophic forgetting [21] [90] [113].The proposed fine-tuning example uses query-knowledge pairs in the specific query language supported by a memory system’s DBMS.Source: §2.1.2 · p. 9
• Basic information exchange does not ensure coordination among heterogeneous components; the survey proposes a mechanism to manage interactions, configuration mismatches, representation translation, and information flow.Tool-calling template incompatibilities are one example of the problem. The proposed mechanism would not require further customization of the backbone LLM.Source: §2.1.2 · p. 9

2.2 Multi-Stakeholder Perspectives on LLM-Based Agent Generalizability

• The survey frames generalizability around consumers, model and data providers, agent developers, and regulators; consumers value one agent serving diverse tasks without switching among specialists [22].Deploying organizations can support multiple domains or shifting priorities with minimal retraining, while platform owners benefit when agents continue functioning after interface changes [142].Source: §2.2 · p. 10, §2.2.1 · pp. 10–11

2.2.2 Model Providers and Data Providers.

• A key future direction is building “agent-friendly” LLMs that integrate smoothly with diverse configurations of other agent components, including tools, memory, and perception and processing units [90].Most agent-task datasets include user-instruction and suggested-final-action pairs, optimizing the backbone LLM’s action planning while treating specialized components as immutable black boxes with fixed configurations [72] [93].Source: §2.2.2 · pp. 11–12
• “Agent-friendly” datasets could encode component configurations and interactions for end-to-end joint training, allowing agents to adjust strategies to task requirements rather than rely on fixed settings [42] [90].The proposed examples include learning stricter memory-similarity thresholds in regulatory domains and looser ones in creative domains, enhancing generalizability and security.Source: §2.2.2 · p. 12

2.2.3 Agent Developers.

• Agent developers integrate components from different providers, but communication channels and orchestration of heterogeneous components remain necessary for seamless integration [62] [42].The described integration role includes establishing data-exchange or message-passing protocols and coordinating components within the architecture.Source: §2.2.3 · p. 12
• Fine-tuning the backbone LLM on available tools can improve its integration with the tool unit, but suggested tools may still mismatch the tools actually available [79].Instruction-tool pairs can teach tool use, while component heterogeneity can make the backbone’s suggestions incompatible with the tool unit.Source: §2.2.3 · p. 12
• A central coordination unit, trained on task-configuration pairs, could configure tool selection, perception granularity, and memory retrieval independently of the backbone LLM [42].The unit could be implemented as a lightweight LLM, neural network, or rule-based system to adapt component settings to task demands.Source: §2.2.3 · p. 13
• Multi-agent architectures could make specialized components autonomous agents that negotiate and exchange information through communication protocols, helping resolve incompatibilities from heterogeneity [39] [111].Examples include specialized LLMs for tool use [79] or memory retrieval [114], with components adapting to one another’s representations.Source: §2.2.3 · p. 13
• Generalizable agents can reduce developer maintenance costs and expand market reach by adapting to new domains with minimal fine-tuning, avoiding costly data curation and retraining [LLM usage charges].The passage identifies reduced maintenance costs and avoided data curation and LLM usage charges during retraining as potential returns.Source: §2.2.3 · p. 13

2.2.4 Regulators and Policy-Makers.

• Regulators need agents to perform consistently across demographic groups, geographic regions, and task contexts, since inconsistency risks bias or unreliability in high-stakes settings.The passage gives comparable diagnostic accuracy across patient populations and clinical environments as a possible FDA requirement.Source: §2.2.4 · p. 13
• Regulators also seek to prevent sensitive-data leaks and malicious behavior, with restrictions potentially limiting access to vulnerability databases or unlicensed copyrighted training data [85].These constraints influence agent training, information processing, and real-world deployment.Source: §2.2.4 · p. 13
• Regulators may require standardized generalizability benchmarks before deployment and transparency through model documentation and interpretability to support explainable, auditable behavior.Future frameworks may formalize these expectations as requirements for trustworthy agents aligned with social and legal norms.Source: §2.2.4 · p. 13

3.1.1 Navigation.

• Navigation datasets probe linguistic ambiguity and environmental diversity, from DNIA’s disfluent spoken commands to RoboTHOR’s varied household scenes and target-object navigation.DNIA [100] includes indoor and outdoor scenarios; RoboTHOR [25] features furnished rooms and 32 object types.Source: §3.1.1 · p. 14

3.1.2 Household.

• Household benchmarks span structured activities, text-and-visual interaction, and adversarial robustness.VirtualHome [77] models activities as atomic action sequences; ALFWorld [93] combines text-based and visual 3D environments; EIRAD [59] tests adversarial navigation and manipulation.Source: §3.1.2 · p. 14

3.1.3 Web.

• Web benchmarks include simulated e-commerce measured by WebShop [131] and complex web-task instruction following by Mind2Web [26], which contains 2,350 tasks from 137 websites spanning 31 web-based domains.Source: §3.1.3 · p. 15

3.1.4 Operating System.

• OSWorld [127] evaluates agents in Ubuntu, Windows, and macOS using applications and cross-platform tasks such as document editing and email management.The benchmark is designed to assess interaction with diverse operating-system environments.Source: §3.1.4 · p. 15

3.2 Establishing Evaluation Dimensions for Agent Generalizability

• Generalizability is formally defined as the region in which an agent consistently demonstrates high task performance, providing a clear evaluation boundary; standardized framing makes agent scope clear and supports choosing appropriate evaluation methods.Source: §3.2 · pp. 15–16
• The proposed evaluation dimensions increase in difficulty, beginning with instruction-level variation, unseen tasks, and the same task across environments.The excerpt defines instruction-level generalizability with task and environment fixed, and task-level generalizability as performance on unseen tasks in the same environment; it introduces environment-level evaluation as consistency on the same task across environments.Source: §3.2 · p. 16
• Evaluation dimensions include transfer between structurally different environments and transfer of core skills across domains, such as moving from e-commerce tasks to travel tasks.The examples distinguish web platforms and airline booking sites as different environments, and product purchasing or returns from hotel booking or refunds as cross-domain tasks.Source: §3.2 · p. 16

3.3 Evaluation Metrics

• Task evaluation uses Success Rate (SR) [62] (also TGC [107] or VRR [125]), while Step SR tracks intermediate execution and Human Alignment Scores capture preference-rated output quality.Step SR assesses correct intermediate actions; Human Alignment Scores use human ratings of outputs against structured criteria.Source: §3.3 · pp. 16–17
• Overall Score [129] [133] aggregates category-specific success rates with weights that may reflect sample counts or task importance, but can obscure uneven performance across heterogeneous tasks.Task-level measures vary by task, including Success Rate, Human Alignment Scores, and Step Success Rate.Source: §3.3 · p. 17

3.4 Limitations and Future Directions in LLM-based Agent Generalizability Evaluation

• Current evaluation can conflate narrow task accuracy with broad generalizability, overlook category disparities, and miss tradeoffs between specialization and generalization.The survey calls for fairer, more comprehensive, and scientifically grounded evaluation.Source: §3.4 · p. 17

3.4.1 Standardized Framework for Defining Agent Generalizability Boundaries.

• A standardized hierarchical domain-task taxonomy could define agent capability boundaries and make generalizability comparisons more transparent, meaningful, and reproducible.Without common boundaries, inconsistent definitions and granularity make comparisons ambiguous and unfair.Source: §3.4.1 · pp. 17–18

3.4.2 Variance-Based Metrics for Performance Disparities.

• A 43.1% Overall Score can mask 61.3% on social forum opinion exchange versus 14.6% on content management, which represents 5.91% of training data [129], motivating variance-based measures [68].The weighted score can understate poor performance on categories with little influence on the aggregate.Source: §3.4.2 · p. 18
• Standard deviation across categories indicates consistency, but variance must accompany Overall Score because it cannot distinguish uniformly poor from uniformly strong agents.The survey describes robust generalizability as requiring both high Overall Score and low performance variance.Source: §3.4.2 · p. 18

3.4.3 Evaluation Metrics for Generalizability Cost.

• Evaluation rarely quantifies the cost of broader scope: performance on specific tasks can decline through resource sharing, while current practices assess narrow accuracy or multitask generalizability in isolation.This gap limits principled choices between specialization and generalization.Source: §3.4.3 · p. 18

3.4.3 Evaluation Metrics for Generalizability Cost

• GC measures the average task-performance sacrifice when extending an agent from a narrow specialist to a broader generalist.A task-specific specialist is trained only on the current task, whereas a generalist spans all n tasks.Source: §3.4.3 · p. 19
• Lower GC indicates stronger retained task-specific performance, while higher GC signals greater specialization sacrifice; the metric supports setting acceptable tradeoffs and comparing agents.It is intended to help evaluate whether generalization gains justify task-specific performance sacrifices and identify agents that balance specialization and generalization efficiently.Source: §3.4.3 · pp. 18–19

4.1.1 Training Data Design.

• Deng et al. [26] curate over 2,000 open-ended tasks from 137 websites across 31 domains, using real-world data to capture broader user interaction patterns.Training data spanning multiple domains and task types can support generalization across interaction modalities and planning contexts.Source: §4.1.1 · pp. 19–20
• Wang et al. [112] propose instruction-tuning with both textual feedback and non-linguistic reward signals, learning from linguistic feedback and maximizing rewards from non-linguistic inputs.This contrasts with earlier work that primarily used textual feedback and relied on rewards only for filtering data.Source: §4.1.1 · p. 20

4.1.2 Training Objective Design.

• Training exclusively on agent-specific data degrades agent generalizability, whereas a mixed objective improves performance on both agent-specific and general tasks.Zeng et al. [133] combine agent-specific tasks with general-purpose data such as math reasoning, coding, and natural-language understanding.Source: §4.1.2 · p. 20

4.1.3 Limitations and Future Directions.

• Static fine-tuning datasets may leave model weaknesses unaddressed; dynamic task generation targets failure cases but can be prohibitively slow in interactive environments.A future direction is to adapt dynamic curricula to sandbox environments, enabling faster iterations without real-world execution overhead while retaining adaptive training.Source: §4.1.3 · p. 20

4.2.1 In-Context Learning with Prompted Examples.

• In-context learning supplies task-relevant examples directly in prompts or retrieves them from memory, helping the LLM infer task structure and expected action formats in unseen scenarios.The passages identify direct prompts [136] [142] and memory-retrieved indirect prompts [44] [85] [124].Source: §4.2.1 · p. 21

4.2.2 Structured Planning with Domain-Invariant Representations.

• Structured planning separates reusable, domain-invariant action schemas and rules from environment-specific facts, allowing plans to transfer without domain-specific adaptation or retraining.PDDL can represent environments in a standardized planning language while keeping the planning logic consistent.Source: §4.2.2 · p. 21

4.2.3 Case Study: Generalization via PDDL in Household Environments.

• In the household PDDL case study, a fixed domain file defines universal action schemas, while the backbone LLM generates a problem file for each environment.The examples of shared schemas include pick-up, open-door, and move.Source: §4.2.3 · pp. 21–22
• PDDL search over symbolic environment representations generates valid, optimized action sequences and supports generalization to unseen household layouts and object arrangements.The approach decouples static planning logic from dynamic world states.Source: §4.2.3 · p. 22

5.1 The Perception and Processing Unit

• Perception and processing capabilities help agents capture environmental input and transform it into forms the backbone LLM can use.The section identifies observation perception and observation processing as the two capabilities to improve.Source: §5.1 · p. 22

5.1.1 Observation Perception.

• Perception must handle changing environments and recognize operational boundaries; generalizing across fundamentally different domains remains an open challenge.The section cautions against universal perception systems stretched across disparate domains.Source: §5.1.1 · p. 22

5.1.2 Observation Processing.

• Simplifying observations while preserving key information can help agents generalize across websites and tasks by filtering distractions and retaining planning-relevant features.Yang et al. [129] propose this approach; it is especially relevant to memory-based planning, where stored observations serve as in-context samples [99].Source: §5.1.2 · p. 23

5.2 The Memory Unit

• Memory supports generalizability through flexible management and through how stored information is used.The section organizes memory contributions around management flexibility and data storage and utilization.Source: §5.2 · p. 23, §5.2.1 · p. 23, §5.2.2 · p. 23

5.2.1 Management Flexibility.

• Flexible memory insertion, updating, and deletion can keep task-relevant knowledge current and support adaptation to unseen tasks.The example describes adding hotel-booking examples to memory for an agent trained primarily on airline booking.Source: §5.2.1 · p. 23

5.2.2 Data Storage and Utilization.

• Short- and long-term memories can ground reasoning in past examples, but irrelevant stored data can reduce generalizability.Wang et al. [112] emphasize diverse linguistic feedback and non-linguistic reward signals; Yang et al. [129] report harm from irrelevant memory.Source: §5.2.2 · p. 23

5.3 The Tool Unit

• Callable external tools broaden environmental interaction, with tool diversity and management flexibility identified as design factors.The section treats the tool unit as a means of solving a broader range of problems.Source: §5.3 · p. 23

5.3.1 Tool Diversity.

• A diverse toolset supports varied operations, enabling generalization across web domains and application types.Examples include clicking, selecting, typing, and submitting forms rather than only reading content or following links.Source: §5.3.1 · p. 23

6 Generalizability of Interactions between the Backbone LLM and Agent Specialized Components

• Unfiltered transfer raises planning complexity and inference costs, while identical static text and clickable elements can be confused, producing ineffective actions despite an available link.An agent may then incorrectly conclude that a task is impossible.Source: §6 · p. 24
• When the same text describes a static label and an actionable link, retrieved context can force the LLM to disambiguate them and choose the wrong element.That error can lead to nonfunctional subsequent actions on unseen websites.Source: §6 · p. 24

6.1 Interaction between the Perception and Processing Unit and the Memory Unit

• Filtering observations before storage reduces irrelevant memory content and inference complexity, improving cross-environment generalizability.Unfiltered memory can mix transferable task patterns with site-specific noise that overloads context and attention during planning.Source: §6.1 · p. 25
• Yang et al. [129] use tree-structured parsing to store pivotal task elements with their structural context, preserving functional relationships while omitting decorative or variable content.The retained context can include ancestor, sibling, and descendant nodes around a pivotal element.Source: §6.1 · pp. 25–26
• Ji et al. [48] select observations by embedding similarity to the current action and stored knowledge, retaining segments that correlate with the task goal.This relevance-based approach is presented as complementary to structured filtering.Source: §6.1 · p. 26

6.2 Interaction between the Memory Unit and the Backbone LLM

• During planning, the memory unit should retrieve only knowledge relevant to the current objective rather than all similar-task records, giving the backbone LLM compact signals and avoiding irrelevant context.Relevant content may include task-related thoughts, actions, and observations.Source: §6.2 · p. 26
• Retrieving Delta-specific interface details for United seat selection can trigger nonexistent UI actions, causing failed sequences and task abandonment.Examples include seeking a “blue Confirm Seat” button or rows 12–15 that are absent in the new environment.Source: §6.2 · p. 26
• Summarized, abstracted memories preserve transferable planning patterns while discarding environment-specific details, enabling successful adaptation to unseen interfaces.In the seat-selection example, this lets the agent carry over comfort preferences and fee-aware decisions from Delta to United despite different layouts or row numbering.Source: §6.2 · p. 26
• Yang et al. [129] organize tasks in a tree and retrieve only prior subtasks sharing a parent with the current subtask, filtering out irrelevant experience.For seat selection, the agent retrieves seat-related experience rather than baggage-selection or payment segments.Source: §6.2 · pp. 26–27
• Hu et al. [44] instead retrieve a compressed summary of the entire prior task, generated by an LLM or dedicated module, that preserves planning-relevant information while abstracting implementation details.A high-level booking narrative can replace the full action-and-observation sequence.Source: §6.2 · p. 27
• Both strategies limit irrelevant memory data and supply summarized experience or transferable patterns, better positioning the agent to generalize planning across new environments.Source: §6.2 · p. 27

7 Generalizable Frameworks vs. Generalizable Agents: Differences and Connections

• Generalizable frameworks perform strongly across scenario types with associated fine-tuning data, whereas generalizable agents sustain high performance across diverse, unseen scenarios.A broadly generalizing framework does not necessarily guarantee a broadly generalizing agent.Source: §7 · p. 27

7.1 Differences Between Generalizable Frameworks and Generalizable Agents

• A framework paired with task-specific fine-tuning data can perform that task well, but this alone does not establish transfer beyond the fine-tuning distribution.The section frames bridging methodological and agent-level generalizability as a central research challenge.Source: §7.1 · pp. 27–28
• The survey groups potential generalizable frameworks into six themes, from instruction optimization and iterative action generation to reflection, uncertainty, human alignability, and component interactions.These categories provide a structured view of framework approaches.Source: §7.1 · p. 28
• The six themes span workflow stages: instruction clarification before planning, action decomposition during planning, refinement mechanisms, and faithful execution of planned actions.Reflection, uncertainty analysis, and human alignability are described as post-planning refinement mechanisms.Source: §7.1 · p. 28
• Empirical benchmarks are still needed to assess cross-scenario generalizability, since a framework may perform well in one scenario but poorly in another.The literature also rarely specifies which performance aspect a proposed framework seeks to improve.Source: §7.1 · pp. 27–28

7.2 User Instruction Optimization and User Intent Capture

• Ambiguous or incomplete instructions can lead to misinterpreted intent and suboptimal planning, while well-structured instructions can help agents use their capabilities.Useful structure may include task descriptions, constraints, sample tool APIs, and reminders to use memory and observations.Source: §7.2 · p. 28
• Instruction optimization methods can rank and refine candidate instructions, while stored successful intent-capturing actions can be reused for similar new instructions.The cited examples are Chen et al. [19] and Yang et al. [130], respectively.Source: §7.2 · pp. 28–29
• Text-focused agents may overlook intent cues in audio or images, and converting speech directly to text can discard tone, pitch, or hesitation cues.The passages connect this limitation with hallucinations and reduced performance in non-text scenarios.Source: §7.2 · p. 29
• An audio-guided navigation framework combines semantic uncertainty with vocal uncertainty to model uncertainty in user instructions.The vocal cues include pitch, loudness, and speech rate.Source: §7.2 · p. 29
• Future instruction understanding work should let LLMs reason over raw multimodal signals and incorporate visual cues, gestures, and image context.The passages name AudioLM [13] and Whisper [80] as examples of advances, and identify visual signals as an open area.Source: §7.2 · p. 29

7.2.1 Limitations and Future Directions.

• The section calls for ambiguity classification and component-level contradiction detection, alongside human-in-the-loop clarification that balances efficiency with user satisfaction.The stated aim is to avoid excessively burdensome clarification dialogues [137] [138].Source: §7.2.1 · p. 29
• Ambiguous instructions can trigger cascading errors across components, such as irrelevant tool use and misguided perception, motivating cross-component ambiguity detection and resolution.Perception, memory, and tool signals could be combined; contextual observations may clarify vague references, but systematic strategies beyond household scenarios are needed.Source: §7.2.1 · p. 29
• Ambiguity can produce conflicting component interpretations, as when room temperature, a remembered preference, and clothing cues point to different meanings of comfort.The cited example gives a room temperature of 82°F and a remembered preference for 72°F.Source: §7.2.1 · p. 29

7.3 Iterative Action Generation

• Iterative action generation adapts step by step to prior reasoning, outcomes, observations, and intermediate results.ReAct [132] alternates thoughts, actions, and observations; a self-improving variant filters and reuses thought-action sequences to fine-tune the backbone LLM [4] [38].Source: §7.3 · p. 29
• Reward-based approaches use Monte Carlo sampling to estimate candidate-action rewards [128], MCTS to explore and evaluate action sequences [20], and an auxiliary LLM to model sequence rewards and select the highest-reward sequence [63].Source: §7.3 · p. 30

7.3.1 Limitations and Future Directions.

• Long-horizon tasks with interdependent actions and delayed rewards challenge iterative planning, while explicit reward modeling can be computationally costly and rely on sparse signals.The passages cite web navigation [78] and device control [29] as examples; these costs and sparse rewards can hinder responsiveness and may degrade plan quality.Source: §7.3.1 · p. 30
• High-level strategy followed by environment-specific execution and memory retrieval have been tried, but these approaches generally lack explicit intermediate-action reward modeling.Without it, they have limited ability to evaluate and adjust ongoing plans.Source: §7.3.1 · p. 30
• Dynamic reward models that adapt to context are a proposed direction, including hybrid models balancing long-term outcome awareness with computational efficiency.Some intermediate-action rewards may depend on the eventual outcome, while others can be computed efficiently from intermediate results.Source: §7.3.1 · p. 30

7.4 Reflection

• Reflection evaluates and revises planned actions based on feedback, with internal feedback generated within the agent and external feedback supplied by the environment.External signals can include binary rewards or interpreter and API error messages.Source: §7.4 · p. 30
• Internal reflection prompts the LLM to critique or revise planned actions, and can also verify output formats.Rigid predefined API formats can restrict the action space available to the agent.Source: §7.4 · p. 30
• Planned actions can be translated into executable Python and run by an interpreter, expanding the effective action space while maintaining verifiability.Source: §7.4 · p. 30
• With negative external feedback, a hierarchical tool strategy identifies the failed invocation and replans with alternatives, helping localize errors and recover from execution failures.The approach is described by Du et al. [27].Source: §7.4 · p. 30

• Uncertainty can prompt additional planning or human intervention, including clarification, when an agent detects low confidence in an action.The section illustrates this with an agent that selects a soap bottle instead of a soap bar and assigns its action 10% confidence.Source: §7.5 · p. 31
• Agent uncertainty methods can use environmental signals: Fang et al. [31] measure alignment between inferred user tasks from action-observation sequences and ground-truth tasks, while Zheng et al. [143] use image-input MLLMs for failure detection.Source: §7.5 · p. 31
• Most work emphasizes instruction or action uncertainty, while corrupted or incomplete observations can degrade performance or cause catastrophic failures.The section calls for comprehensive observation-level uncertainty analysis, including noisy advertisements and products seen from only one brand.Source: §7.5 · p. 31
• Memory retrieval creates a context trade-off: recent sub-tasks may provide too little information, while full histories may overwhelm the model with irrelevant material.The section recommends adaptive retrieval that prioritizes uncertainty-reducing knowledge and expands retrieval only when confidence thresholds are unmet.Source: §7.5 · p. 31
• Whole-sequence uncertainty assessment becomes computationally expensive on long tasks, whereas step-local assessment can miss dependencies between actions.The section proposes hierarchical approaches that combine local assessments with selective reasoning over critical global dependencies.Source: §7.5 · p. 32
• The section concludes that uncertainty analysis supports proactive error detection and cautious decisions, with observation-level, retrieval, and scalability gaps remaining.Future work should address all three gaps in integrated uncertainty frameworks.Source: §7.5 · pp. 31–32

7.6 Human Alignability

• Human alignability includes human-like reasoning and learning social norms and values through self-evolving interactions.The section describes agents that infer rewards, generate feedback beyond scalar ratings, and adapt from environmental observations.Source: §7.6 · p. 32
• Studies associate enhanced human alignability with improved interpretability and general performance, including task accuracy.Source: §7.6 · p. 32

7.6.1 Alignability in General LLMs.

• SFT uses curated instruction-output pairs but has high data-creation costs and limited generalizability beyond fine-tuning data.RLHF trains a reward model from human feedback, but faces evaluator bias and difficulty representing diverse human values.Source: §7.6.1 · p. 32

7.6.2 Alignability in LLM-based Agents.

• Agent alignment approaches include a self-critic that adjusts behavior from feedback and policies fine-tuned on rewards inferred from environmental payoffs.Liu et al. [58] describe self-critique; Tennant et al. [105] infer rewards from outcomes such as an Iterated Prisoner’s Dilemma.Source: §7.6.2 · p. 32

7.6.3 Distinctive Features and Future Directions.

• Agents can generate feedback through self-critique [58] or environmental signals [105], which may suit unpredictable settings where preset human feedback cannot fit every scenario.Human evaluation can also introduce latency in time-sensitive applications [110].Source: §7.6.3 · pp. 32–33
• Future directions include combining feedback sources, richer social learning, and benchmarks for alignment to human values as well as task accuracy.The section frames these directions as steps toward robust, human-centered generalizability.Source: §7.6.3 · p. 33

• Without awareness of tool capabilities and limitations, the backbone LLM may produce infeasible or unsafe plans and invoke unsupported functions.Xiang et al. [124] observe that the model may attempt to call functions the tool unit does not support.Source: §7.7 · p. 33
• Tool enumeration alone may leave the model unable to compose tools for complex tasks, leading to narrow or suboptimal plans.The section also notes that predefined action formats improve consistency while constraining the agent’s action space.Source: §7.7 · p. 33
• Tools can fail, return inconsistent outputs, or require specific conditions, so robust planning requires awareness of tool scope and failure modes.In the financial-advisory example, a tax-impact tool may fail on large datasets, and access privileges may need checking before tool use.Source: §7.7 · p. 33
• Informing the backbone LLM about tool functionalities, dependencies, and error profiles supports tool composition, failure anticipation, and robust plans.The section presents these as ways to enhance performance in complex applications.Source: §7.7 · p. 33

7.8 From Generalizable Frameworks to Generalizable Agents: Future Directions

• Methodological generalizability does not yet establish agent-level generalizability, motivating systematic evaluation and principled integration with agent design.Source: §7.8 · p. 33

7.8.1 Benchmarks for Framework-level Generalizability.

• Framework-level benchmarks should test diverse instructions, tasks, environments, or domains using multiple training and testing datasets.They should assess performance after scenario-specific fine-tuning and enable fair comparison of frameworks.Source: §7.8.1 · p. 34

7.8.2 Benchmarks for Agent-level Generalizability.

• Agent-level benchmarks should train on held-in tasks and test on held-out datasets; consistently high held-out performance would provide evidence of agent generalizability.The section gives ReAct [132] or Reflexion [91] with ALFWorld and BabyAI training, then HotPotQA or GSM8K evaluation, as an example.Source: §7.8.2 · p. 34

7.8.3 Synergistic Integration of Frameworks and Agent-level Techniques.

• A generalizable framework may not directly yield a generalizable agent, so the section recommends combining frameworks with diverse data, component improvements, or better LLM-component interactions.Source: §7.8.2 · p. 34, §7.8.3 · p. 34
• Combining reflection with training-diversity strategies has outperformed either approach alone [32], motivating benchmarks of framework and agent-technique combinations.Such benchmarks would test whether hybrid approaches generalize better than individual methods.Source: §7.8.3 · p. 34

• The survey formalizes agent generalizability, presents a taxonomy for measuring and improving it, and clarifies how generalizable frameworks differ from generalizable agents.Source: §8 · p. 34

Novel Architectural Frameworks for Component Coordination

• Future architectures should coordinate backbone LLMs and specialized components; central units could resolve incompatibilities and optimize information flow.Source: §8 · p. 34

Standardized Frameworks for Defining Agent Generalizability

• A standardized hierarchical domain-task ontology could define capability boundaries and support rigorous, transparent, reproducible evaluation across agents.Source: §8 · p. 35

Benchmarks for Framework-to-Agent Generalizability Translation

• Proposed benchmarks would test both framework generalizability and whether deployed frameworks yield generalizable agents, including combinations with agent-level techniques.Source: §8 · p. 35

• The authors conclude that systematic evaluation, architectural improvement, and methodological innovation are all necessary to realize the potential of generalizable agents in real-world applications.Source: §8 · p. 35