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Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

Yu Luo, Jiamin Jiang, Yimin Zuo, Xidao Wen, Rongchen Gao, Yongqian Sun, Shenglin Zhang, Guiyang Liu, Cheng Zhang, Fang Situ, Qi Zhou, Dan Pei

arXiv:2610.01415v1cs.AI

TL;DR

Long-horizon agents need more than retained interaction history: changing worlds can make earlier evidence and judgments difficult to reconcile. PoS maintains task-conditioned beliefs, validates updates, and recovers from Belief Trapping; it achieves the highest overall performance across four benchmarks with three LLM backbones, at additional token and computational cost.

  • Problem

    Long interaction histories mix evidence about past states with intermediate judgments, so access to retained or reorganized evidence alone does not ensure a coherent, revisable estimate of the current world.

    Source: §1 · p. 2

  • Method

    PoS maintains a task-conditioned belief combining a structured world estimate with unresolved requirements, validates updates, and uses factorized diagnosis and recovery for Belief Trapping.

    Source: §1 · p. 2, §3.1 · p. 4

  • Results

    PoS achieves the highest overall performance on all four benchmarks with each of three backbones, with relative gains over the strongest baseline reaching 22.68% on ALFWorld.

    Source: §1 · p. 2, §4.1 · p. 7

  • Takeaways & Limitations

    Across four benchmarks and three backbones, the results support explicit belief construction and continual maintenance as an approach to long-horizon context management beyond history retention and compression.

    Source: §5 · p. 10

    PoS incurs additional token and computational costs, and repeatedly expressing and revising beliefs in language may introduce redundancy, omissions, or semantic distortion.

    Source: G Limitations and Future Directions · p. 26

Abstract

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Large language model (LLM) agents can now undertake increasingly complex tasks, but the way they organize interaction history into memory does not ensure a coherent understanding of the current world. We introduce PoS, an inference-time framework that constructs and continually maintains explicit belief states as the agent's decision context. Each belief combines an estimate of the current world state with unresolved task requirements, making explicit what the agent still needs to learn and accomplish. To keep this belief reliable and actionable, PoS validates its consistency and monitors task progress to detect Belief Trapping, where the agent continues to act without making meaningful progress toward the goal. Recovery is then tailored to both the trapping pattern and the type of unresolved task requirement. Experiments on four benchmarks spanning execution and diagnosis show that PoS achieves the highest overall performance on every benchmark with all three LLM backbones. Ablations demonstrate the importance of consistency validation and recovery, while context-scaling experiments show resilience to context growth. Together, these results support belief construction and continual maintenance as a foundation for long-horizon context management beyond history retention and compression.

1 Introduction

Long-horizon interaction makes it harder to distinguish current facts from outdated evidence and intermediate judgments; retaining or reorganizing history alone does not ensure a coherent estimate of the current world. PoS maintains task-conditioned beliefs, validates them, and recovers from Belief Trapping, and achieves the highest overall performance across four benchmarks and three LLM backbones.

1 Introduction

  • Longer interaction histories mix evidence about past states with intermediate judgments, making it harder to tell which facts remain valid and what is unresolved.The paper argues that access to evidence alone does not ensure an explicit, coherent, revisable estimate of the current world.Source: §1 · p. 2
  • PoS combines task-conditioned belief modeling, consistency validation, and trapping-aware recovery for long-horizon agent decision-making.Its beliefs represent task-relevant world state and unresolved epistemic and achievement gaps; recovery is tailored to trapping dynamics and blocked gap type.Source: §1 · p. 2
  • PoS achieves the highest overall performance on all four benchmarks with each of three LLM backbones.Relative gains over the strongest baseline reach 22.68% on ALFWorld and 37.89% on RCA-100 joint accuracy.Source: §1 · p. 2
  • Consistency validation and trapping recovery both matter beyond explicit belief construction, while further analyses examine context growth and token-performance tradeoffs.The supplied passage reports these as ablation findings and analysis topics.Source: §1 · p. 2

2 Related Work

The related work distinguishes context management by what it exposes to an agent: retained or transformed history, versus representations of the current world state. Prior work advances both lines, while the cited approaches to stalled progress stop or truncate interaction rather than restore progress during inference.

2 Related Work

  • The review organizes long-horizon context management by whether decision context is memory or belief.The distinction concerns what context is exposed to the agent.Source: §2 · p. 3

2.1 Memory as Context

  • Memory-as-context methods retain, compress, structure, or let agents curate historical evidence, but the resulting context remains a transformed record rather than a consistent estimate of the current world.Examples include retrievable gist memories, progress briefs, and tools for actively curating working context.Source: §2.1 · p. 3

2.2 Belief as Context

  • Belief-as-context work externalizes current-state representations, while LongHorizon-Harness depends on independently verifiable intermediate results and T3 and CGDP terminate or truncate unproductive interaction.PoS is contrasted as maintaining and validating task-conditioned beliefs and using factorized recovery to redirect subsequent actions.Source: §2.2 · p. 3

3 Methodology

PoS uses a task-conditioned belief as its decision context, combining a structured estimate of the world with unresolved requirements. It validates belief updates, tracks progress, and diagnoses and recovers from trapping using constraints tailored to the trapping pattern and blocked requirement.

3 Methodology

  • PoS comprises Belief Modeling, Belief-Guided Interaction, and Trapping-Aware Recovery.These components construct a task-conditioned belief, select actions and validate updates, then detect and recover from Belief Trapping.Source: §3 · p. 3, Figure 2 · p. 4

3.1 Belief Modeling

  • The Entity–State–Relation world model records task-relevant entities, their understood states, and directed relations, with provenance and confidence.It retains records that can affect action feasibility, outcome assessment, or goal satisfaction rather than modeling the environment exhaustively.Source: §3.1 · p. 4
  • The task-conditioned belief combines a structured world estimate with the task objective and unresolved epistemic and achievement gaps.Epistemic gaps capture what remains unknown or insufficiently supported; achievement gaps capture what must change to reach the goal.Source: §3.1 · pp. 4–5

3.2 Belief-Guided Interaction

  • PoS selects an active gap as the current focus while retaining the full belief, and applies recovery constraints only after Belief Trapping is detected.The active gap is chosen as valuable to address given the current world state and task objective.Source: §3.2 · p. 5
  • The Belief Sentinel audits candidate updates for internal conflicts and contradictions with interaction evidence before the revised belief is committed.The resulting validated belief becomes the next decision state and basis for belief-health estimation.Source: §3.2 · p. 5

3.3 Trapping-Aware Recovery

  • PoS assesses progress across validated transitions using gap persistence, progress stagnation, and belief recurrence, with task-specific progress signals.For diagnosis, progress aggregates confidence changes; for execution, a transition counts when it reduces or informs the active gap or advances a plausible path to the goal.Source: §3.3 · pp. 5–6
  • After detecting trapping, PoS diagnoses both agent dynamics and blocked gap type, then combines pattern-specific escape and gap-specific progress constraints.Static suppresses an ineffective transition, Cycle breaks a recurrent transition, and Drift re-anchors action selection to the active gap; constraints are released when belief health rises above threshold.Source: §3.3 · p. 6

4 Experiments

Experiments span execution and diagnosis tasks, where PoS achieves the highest overall performance across four benchmarks and three backbones.

4 Experiments

  • Evaluation covers ALFWorld and LOCA-Bench execution tasks and RCA-100 and ClinDiag diagnosis tasks, against baselines spanning raw history, compression, adaptive retention, hierarchical memory, and explicit task state.All methods were evaluated with three backbones: Qwen3.7-Plus, Kimi-K3, and GLM-5.3.Source: §4 · pp. 6–7

4.1 Main Results

  • Context-management baselines vary by task: PACE trails Raw Trajectory on LOCA-Bench, while LongHorizon-Harness is less consistent on diagnosis tasks.On ClinDiag with GLM-5.3, no context-management baseline outperforms Raw Trajectory.Source: §4.1 · p. 7
  • PoS achieves the highest overall performance on all four benchmarks across three backbones, with relative gains up to 22.68% on ALFWorld and 37.89% on RCA-100 joint accuracy.The relative gains are 7.53% on LOCA-Bench and 11.31% on ClinDiag; Table 1 reports the four benchmark metrics.Source: §4.1 · pp. 7–8, Table 1 · p. 7

4.2 Belief Trapping Analysis

  • Trapping is common, its dominant pattern differs by benchmark, and PoS’s factorized recovery outperforms a generic recovery prompt across all four benchmarks.Cycles dominate ALFWorld, drift dominates LOCA-Bench and RCA-100, and static stagnation dominates ClinDiag.Source: §4.2 · p. 8, Figure 3 · p. 8

4.3 Cost–Performance Tradeoff

  • On RCA-100 with Qwen3.7-Plus, PoS raises joint accuracy from 24.27% to 38.83% versus Raw Trajectory while reducing Task Agent tokens by 20.9%, but total tokens rise to 5.06×.Removing trapping diagnosis and recovery saves 9.2% of total tokens but reduces joint accuracy by 4.85 points.Source: §4.3 · pp. 8–9, Table 2 · p. 9

4.4 Scaling under Context Growth

  • On LOCA-Bench, PoS remains broadly stable from 96K to 256K context and exceeds the strongest baseline by 10.67–16.00 points at 256K.Figure 4 compares success rates as context grows from 8K to 256K across three backbones.Source: §4.4 · p. 9, Figure 4 · p. 9

5 Conclusion

The conclusion presents PoS as a framework for maintaining task-conditioned beliefs and reports improvements across four benchmarks and three LLM backbones. The appendix notation table summarizes the principal notation used in PoS.

5 Conclusion

  • PoS combines task-conditioned belief construction with consistency validation and factorized recovery from Belief Trapping.The conclusion reports consistent improvements across execution and diagnosis benchmarks, together with resilience to context growth.Source: §5 · p. 10
  • PoS uses more tokens, and reducing this overhead while preserving performance gains is identified as future work.Source: §5 · p. 10

A Notation Summary

B Supplementary Method Details

The supplementary methods describe PoS’s inference workflow, progress and trapping assessment, and recovery design, followed by benchmark task categories. They explain how health signals distinguish trapping from unresolved requirements alone.

B.1 End-to-End Workflow of PoS

  • PoS validates each candidate belief update before commitment, advances the active gap until resolution, and assesses trapping after validated transitions are incorporated into Belief Health.When trapping is detected, the workflow classifies dynamics and selects recovery constraints for subsequent action selection.Source: §B.1 · pp. 16–17

B.2 Belief Trapping Detection and Recovery

  • Diagnostic progress aggregates absolute confidence changes between validated beliefs, while execution progress is assessed relative to the active gap.Trapping assessment begins after K validated transitions; historical states are projected onto the current active gap, and persistence, stagnation, and recurrence contribute to health estimation.Source: §B.2 · pp. 17–18

B.3 Design Rationale for Belief Trapping

  • PoS combines unresolved-gap persistence with progress stagnation or belief recurrence, rather than treating persistent gaps alone as trapping.The geometric-mean formulation can miss trapping when either stagnation or recurrence is absent, while a maximum formulation can mistake an unresolved but progressing gap for trapping.Source: §B.3 · p. 18

C.1 Task Taxonomy

  • The task taxonomy groups ALFWorld and LOCA-Bench by task type, and RCA-100 and ClinDiag by diagnostic targets or case subsets.Source: §C.1 · pp. 18–19

C.1.1 ALFWorld

  • ALFWorld tasks comprise Place, Transform, and Inspect categories.Place includes pick-and-place tasks; Transform includes clean, heat, or cool then place; Inspect includes examining in light.Source: §C.1.1 · p. 19

C.1.2 LOCA-Bench

  • LOCA-Bench tasks comprise Cross-source Workflows, Structured Analytics, and Environment Operations.Source: §C.1.2 · p. 19

C.1.3 RCA-100

  • RCA-100 evaluates Fault Triage, Root-Cause Localization, and Joint correctness of both targets.Source: §C.1.3 · p. 19

C.1.4 ClinDiag

  • ClinDiag distinguishes Common real-world clinical cases from Rare-disease cases.Source: §C.1.4 · p. 19

C.2 Dataset Statistics and Splits

The evaluation uses benchmark-specific splits, including context-length variants for LOCA-Bench and matched Common and Rare ClinDiag samples. The available Emergency subset is excluded under the stated data-access setting.

C.2 Dataset Statistics and Splits

  • 134 ALFWorld valid_unseen tasks span 41 Place, 75 Transform, and 18 Inspect cases, with identical cases used across methods and backbones.Source: §C.2 · p. 20
  • 525 LOCA-Bench cases cover seven context lengths from 8K to 256K tokens, with 75 cases at each length.The 15 seed tasks each have five environment seeds at every target length; each of the three task categories contains 175 cases.Source: §C.2 · p. 20
  • RCA-100 evaluation uses all 103 released cases, scoring Fault Triage, Root-Cause Localization, and their joint correctness on the same cases.Source: §C.2 · p. 20
  • ClinDiag evaluation uses 302 Common and 302 Rare cases; its access-controlled Emergency subset is excluded.The Common cases are sampled once, held fixed across methods and backbones, and balanced with the Rare cases for a 604-case evaluation set.Source: §C.2 · p. 20

C.3 Interactive Evaluation Protocols

All methods use benchmark-specific interfaces, with context-management baselines in a shared ReAct harness and LongHorizon-Harness retaining its native control structure. Each benchmark defines its own action-observation loop and terminal evaluation.

C.3 Interactive Evaluation Protocols

  • All methods use the same benchmark-specific interface; context-management baselines share the ReAct harness, while LongHorizon-Harness retains Manage–Execute–Audit.Source: §C.3 · p. 20
  • ALFWorld agents execute admissible commands from a natural-language goal and observation, succeeding when the environment confirms goal completion.Cases otherwise terminate when the action budget is exhausted.Source: §C.3 · p. 20
  • LOCA-Bench agents use case-specific MCP tools and claim_done; an evaluator judges success, with unfinished cases ending at budget exhaustion.Source: §C.3 · p. 20
  • RCA-100 agents acquire diagnostic signals through tools and submit a fault type and root-cause entity for separate and joint scoring.Cases without a valid submission before the action budget count as failures.Source: §C.3 · p. 20
  • ClinDiag agents gather evidence by asking one specific diagnostic question per action and submit a diagnosis for evaluation against the reference.Cases without a valid submission before the action budget count as failures.Source: §C.3 · p. 20

C.4 Metrics, Budgets, and Termination

These sections define how benchmark outcomes are scored and describe the shared evaluation setup. They also explain the backbone and baseline choices used for cross-domain comparisons.

C.4 Metrics, Budgets, and Termination

  • For LOCA-Bench, overall results pool cases across environment lengths and random seeds, while context-scaling results report each length separately.ALFWorld and LOCA-Bench overall results are micro-averaged over evaluated cases rather than averaged across category-level success rates.Source: §C.4 · p. 20
  • Joint accuracy counts a case as correct only when both the fault type and root-cause entity are identified correctly.ClinDiag diagnosis accuracy is based on whether the submitted final diagnosis is judged clinically equivalent to the reference.Source: §C.4 · p. 21
  • Cases that exhaust their interaction budget without successful completion count as failures, while valid terminal submissions end cases regardless of correctness.Invalid actions and failed tool calls consume an interaction opportunity; infrastructure failures are retried and are not scored as task failures.Source: §C.4 · p. 21, Table 6 · p. 21

D.1 Backbone and Agent Configuration

  • All three backbones—Qwen3.7-Plus, Kimi-K3, and GLM-5.3—are used for PoS and baselines, with no stronger auxiliary model for PoS components.ClinDiag’s provider and judge are fixed to Qwen3.7-Plus across settings; agent-side calls use deterministic decoding.Source: §D.1 · pp. 21–22

D.2 Baseline Selection

  • The baselines span raw history, compression, adaptive context granularity, hierarchical working memory, and explicit audited task-state maintenance.The selection prioritizes methods adaptable to the same four environments and backbone settings, rather than exhaustively benchmarking every related method.Source: §D.2 · p. 22

D.3 Baseline Implementation

Baseline implementations preserve each method’s defining context-management mechanism while adapting it to shared benchmark interfaces. PoS’s update, audit, and trapping-detection schedules vary by benchmark and task type.

D.3 Baseline Implementation

  • Methods share benchmark adapters, tools, task instructions, termination protocols, and action budgets, but their interaction trajectories and exposed evidence can differ.Raw Trajectory, ACON, PACE, and HiAgent use a common ReAct harness; LongHorizon-Harness retains its native control structure.Source: §D.3 · p. 22, Table 7 · p. 23
  • ACON retains runtime history compression but uses fixed, case-independent preservation guidelines instead of benchmark-specific learned guidelines or compressor distillation.Compression thresholds and output budgets are fixed at the benchmark level and shared across backbones.Source: §D.3 · pp. 22–23
  • PACE preserves adaptive chunk granularity and relevance-based retrieval, but its original encoder is replaced with text-embedding-v4 for relevance scoring.The implementation is reproduced from published equations, default settings, and Algorithm 1 because the linked repository was inaccessible.Source: §D.3 · p. 23
  • HiAgent retains active-subgoal trajectories in full and represents completed subgoals with summaries while keeping their original trajectories retrievable.Its subgoal mechanism is ported to the shared interface and structured action format.Source: §D.3 · p. 23
  • LongHorizon-Harness preserves its Manager–Executor–Auditor workflow and audited task-state representation, adapting only benchmark-facing interfaces and action formats.Auditor environment queries count against the same environment-action budget as Executor actions.Source: §D.3 · pp. 23–24

D.4 PoS Hyperparameters

  • PoS updates beliefs and performs local consistency audits every interaction step; global audits occur every 32 LOCA-Bench steps and every 8 steps elsewhere.Diagnostic progress thresholds apply to RCA-100 and ClinDiag, execution progress is assessed by the Belief Sentinel, and trapping detection begins after eight validated transitions.Source: §D.4 · p. 24, Table 8 · p. 24

E Fine-Grained Main Results

The fine-grained results distinguish where explicit belief maintenance helps, how validation and recovery contribute, and what decision context cannot replace. A case study traces recovery from stalled diagnosis to a correct submission.

E Fine-Grained Main Results

  • On RCA-100, removing validation lowers FT by 6.80 points with Qwen3.7-Plus but RCL by 0.97 points under each backbone.Removing recovery reduces RCL by 3.88–4.85 points; on ClinDiag’s Rare subset, validation losses are 0.33 points and recovery losses are 2.31–3.64 points.Source: E Fine-Grained Main Results · pp. 24–25
  • Qwen3.7-Plus ALFWorld Transform success rises from 56.00% to 81.33%, while Place matches the strongest baseline at 97.56%.The results breakdown covers task categories and diagnostic dimensions.Source: E Fine-Grained Main Results · p. 24, Table 9 · p. 25
  • Better decision context does not replace execution skills or domain knowledge; LOCA-Bench Env.Ops success remains 36.00%–41.14% despite PoS leading every backbone.ClinDiag trace analysis also finds incorrect diagnoses after correct information is received but not adequately interpreted.Source: E Fine-Grained Main Results · p. 25

1. Investigation continues, but the diagnostic question remains unresolved

  • In RCA-100 case t024, Kimi-K3 continues investigating without distinguishing database waiting from JVM processing; at step 22 PoS identifies Static Stagnation with an Epistemic Gap.The illustrated episode is one of three recovery episodes in a 28-action run.Source: F Case Study · p. 26, §1 · p. 26

2. Factorized recovery redirects investigation toward the unresolved gap

  • Recovery constrains the agent to avoid ineffective repetition and seek evidence that distinguishes database waiting from JVM processing, without prescribing a tool call or diagnosis.The agent chooses to inspect CPU activity.Source: §2 · p. 26

3. New evidence changes the belief and advances diagnosis

  • After three further low-progress actions, CPU evidence combined with earlier GC observations shifts belief toward JVM processing, resolves the gap at step 26, and precedes a correct inventory / memoryPressure diagnosis at step 27.The CPU evidence supports the belief revision but does not uniquely determine the final failure type.Source: §3 · p. 26

G Limitations and Future Directions

The limitations identify costs of language-based belief maintenance and dependence on domain and task knowledge. Future work is proposed to address both constraints.

G Limitations and Future Directions

  • Natural-language belief maintenance adds token and computational costs through repeated text processing and generation.Repeatedly expressing and revising beliefs can introduce redundancy, information omission, or semantic distortion; latent task-conditioned representations are proposed as a possible direction.Source: G Limitations and Future Directions · p. 26
  • PoS does not systematically retrieve external domain knowledge, so evidence selection and interpretation depend largely on the backbone’s existing knowledge.Insufficient knowledge may lead to omitted facts or incorrect causal and diagnostic judgments even when the Belief State is internally consistent.Source: G Limitations and Future Directions · pp. 26–27
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