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Explicit State Elicitation Is Not Enough: A Controlled Audit of Memory-Policy Classification

Yihang Chen, Pin Qian, Su Wang, Chong Peng, Huan Xu, Shuaiting Li, Yiqi Sun

arXiv:2608.17247v1cs.AI

TL;DR

Personalized agents must classify whether retrieved memory should be used, ignored, updated, or queried, but evidence for explicit state elicitation remains limited. This paper audits that claim with controlled counterfactual evaluations and finds that adding a state-output field does not reliably improve policy accuracy.

  • Problem

    Memory-policy classification must determine whether retrieved user memory should be used, ignored, updated, or queried before answering current requests.

  • Method

    The study applies a five-stage audit protocol and evaluates matched prompt changes on a frozen 160-example counterfactual set with 40 four-way scenario families.

  • Results

    Explicit state output is null for Llama and marginal for GPT-OSS overall, while supplied benchmark-associated labels shift routing without evidencing a faithful state mechanism.

  • Takeaways & Limitations

    Explicit state elicitation alone is insufficient; structured-output claims require audits of shortcuts, prompt components, label conditioning, instability, and provider failures.

  • Takeaways & Limitations

    The evaluation uses synthetic, rule-derived benchmarks and measures policy classification only, not downstream responses, tool actions, retrieval, or memory-store mutation.

Abstract

from arXiv · show

Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this setting to develop an empirical audit protocol for structured intermediate outputs: first audit dataset shortcuts, then isolate bundled prompt changes, check whether intermediate labels are answer-associated, test decomposed semantic evidence, and audit provider-level execution failures. A 480-example synthetic development set initially suggested large gains from a state-structured prompt bundle, but TF-IDF diagnostics showed lexical separability and no positive standalone Ignore cases. We therefore construct a frozen 160-example controlled counterfactual set with 40 matched four-way families and rule-derived reference policies. On this set, exposing the four state definitions improves accuracy, but an isolated explicit state-output field does not significantly improve policy accuracy for Llama-3.3-70B and gives only a marginal, non-significant gain for GPT-OSS-120B. Supplying benchmark-associated state labels shifts policy predictions, but because those labels deterministically map to policies, this is a label-conditioning diagnostic rather than evidence of a faithful internal mechanism. Family-level and seed-stability analyses further show that example-level accuracy overstates counterfactual consistency: complete four-way family success is rare. An exploratory follow-up that elicits decomposed semantic evidence also fails to improve routing for the cleanly evaluated endpoint; the corresponding GPT-OSS condition was unavailable because of provider-side request validation. We evaluate policy classification only, not downstream responses, tool actions, or memory-store mutation.

1 Introduction

This study audits memory-policy classification—whether retrieved history should be Used, Ignored, Updated, or queried—by testing prompt claims on a controlled counterfactual benchmark. It finds that apparent development-set gains do not establish benefits from explicit state elicitation, while externally supplied labels reflect answer-associated conditioning rather than mechanism evidence.

  • Problem setting: Memory-policy classification decides whether retrieved user history should be Used, Ignored, Updated, or queried before answering, without evaluating responses, tools, or memory mutation.The experiments stop at policy prediction.
  • Development-set audit: 480 synthetic development examples suggested gains from state-structured prompts, but TF-IDF diagnostics revealed lexical separability and no standalone positive Ignore cases.The development set is treated as motivation rather than a decisive out-of-distribution test.
  • Controlled benchmark: 160 controlled counterfactual examples form 40 matched scenario families spanning Use, Ignore, Update, and Ask variants with rule-derived reference policies.Current-task text is byte-identical across each family’s four variants, and the set passed structural and semantic validation.
  • Controlled results: Explicit state output does not significantly improve Llama policy accuracy and yields only a marginal gain for GPT-OSS, revising the apparent development-set benefit.A separate diagnostic finds routing sensitive to supplied benchmark-associated state labels, which deterministically map to policies and therefore do not establish a faithful mechanism.
  • Audit protocol: The paper contributes a reusable audit protocol separating dataset shortcuts, prompt changes, label conditioning, semantic-evidence elicitation, and provider-level execution failures.Its instantiation includes matched counterfactual families, family-cluster analysis, stochastic-stability diagnostics, and rule-based error summaries.

2 Related Work

Related work frames agent memory as an architectural and behavioral component, while personalization, clarification, prompting, and faithfulness studies motivate evaluating memory-policy routing separately and conservatively.

  • Memory systems: Long-term memory systems address storing, organizing, retrieving, and revising information across interactions, including hierarchical memory, dynamic evolution, evaluation, profiling, and task-oriented action use.Recent multi-agent work further treats memory as an active determinant of downstream social behavior rather than merely expanded context.
  • Personalization and retrieval: Personalized language-modeling and retrieval benchmarks test how profile information, assigned traits, and user preferences support classification, generation, and interaction.Examples include LaMP, PersonaLLM, PrefEval, and PrefIx.
  • Controlled evaluation: Adjacent controlled evaluations emphasize that conclusions depend on cohort, target definitions, confounded intervention changes, and the reported metric.These studies serve as methodological analogies for controlled evaluation design, not evidence about memory-policy routing.
  • Clarification and updating: Clarification research treats asking as a first-class action, while knowledge-editing methods study updating stored facts through direct parameter changes.CLAQUA, CLAM, self-knowledge work, and MEMIT motivate distinct query and update policies.
  • Prompting and decomposition: Intermediate-text prompting decomposes reasoning into traces, subproblems, or questions, while semantically equivalent formulations can produce systematic performance differences.This motivates isolating prompt components before attributing behavioral changes to a particular intermediate representation.
  • Faithfulness and shortcuts: Shortcut-learning and explanation-faithfulness research caution that an intermediate label can affect an answer without demonstrating that it reflects the model’s internal decision process.The paper therefore interprets its forced-state diagnostic conservatively.

3 Task Formulation

The task formulation evaluates an intermediate memory-policy decision rather than the assistant’s final response. Models may also emit a diagnostic state, but the benchmark’s state and policy labels are task-specific abstractions rather than universal latent mechanisms or ontologies.

  • Policy evaluation: The paper estimates only an intermediate policy π over retrieved user history and the current task, stopping before response generation.The deployed assistant ultimately produces a response R, but experiments stop at estimating π.
  • Diagnostic state: State-structured prompting allows the model to emit a diagnostic state s alongside the policy decision.
  • Label semantics: The four benchmark-associated states map deterministically to Use, Ignore, Update, and Ask, so they are not independent evidence of a latent mechanism.The policy labels are a benchmark-specific routing abstraction, not a complete, universal, or mutually exhaustive ontology for deployed memory systems.

4 Audit Protocol and Prompt Arms

The study uses a five-stage audit protocol to separate dataset shortcuts, prompt-arm effects, semantic interventions, and operational execution failures. Its controlled evaluation freezes five prompt arms, with state-output versus policy-only as the primary matched comparison, while treating supplied state labels as a separate label-conditioning diagnostic.

  • Audit protocol: The five-stage protocol audits dataset construction before interpreting prompt gains and retains operational records such as parse failures, provider-validation failures, retries, and terminal outcomes.Stage 1 checks class coverage, label distributions, lexical separability, template predictability, leakage, grouped generalization, and transfer limitations; Stage 5 also covers endpoint amendments, token/cost accounting, and release-package rebuilds.
  • Diagnostic separation: The label-conditioning diagnostic is separated from semantic-evidence interventions because supplied benchmark-state labels deterministically map to policy labels and do not test mechanism faithfulness.The diagnostic uses six artifact conditions, with the wrong-state construction described in Appendix E.
  • Prompt arms: All evaluated arms use single-call prompts that provide history and task inputs and require strict JSON outputs.Development arms include implicit-state/no-state-output prompts, a state-structured bundle, and policy-only baselines.
  • Prompt arms: The old state-structured bundle changes taxonomy, schema, wording, rationale requirements, prompt length, and policy descriptions simultaneously, preventing attribution of its gains to one factor.Historical internal names are retained only as artifact identifiers.
  • Controlled ablation: The frozen matched-ablation set contains five pre-specified arms, with state-output versus policy-only designated as the primary matched comparison.The arms are policy-only, taxonomy-only, state-output, state-output-with-rationale, and deterministic-routing with policy definitions; policy definitions and formatting are held constant where possible.

5 Data

The development set is balanced by generation category but heavily skewed toward Use and is lexically separable, so it serves only as development evidence. A separate frozen counterfactual set uses balanced, rule-derived four-way families with validated structure and substantially harder lexical baselines.

  • Development set: The 480-example development set has 240 Use, 120 Update, 120 Ask, and 0 Ignore references, with labels assigned by the generator.It is balanced across Stable, Contextual, Updated, and Ambiguous generation categories; Always-USE reaches 50.0%.
  • Development set: 89.6±3.5% policy accuracy from supervised TF-IDF under random folds indicates lexical separability, limiting this set to development evidence.The historical prompt-bundle result is treated as motivation only.
  • Frozen controlled set: The frozen controlled set contains 160 examples organized as 40 scenario families crossed with Use, Ignore, Update, and Ask, with policies derived deterministically from structured semantic slots.Each example has exactly four history records, and task text is byte-identical across variants within each family.
  • Frozen controlled set: The controlled set passes structural and semantic validation, including policy balance, four records per example, within-family task identity, type compatibility, and no direct label leakage.It is synthetically generated and rule-labeled.
  • Frozen controlled set: 0.390 word TF-IDF macro F1 and 0.368 character n-gram macro F1 show that the frozen controlled set is harder for evaluated lexical baselines.These figures come from grouped diagnostics in the controlled-set audit.

6 Experimental Setup

The experiments span development and frozen controlled-set phases using specified hosted endpoints, with Llama-3.3-70B bridging both phases after Qwen3-32B became unavailable. Controlled runs use frozen prompts, strict logging and parsing, bounded cost, and family-clustered uncertainty analysis.

  • Models and endpoints: Llama-3.3-70B and Qwen3-32B support development-set evidence, while controlled experiments use Llama-3.3-70B and GPT-OSS-120B.Qwen3-32B was replaced before any controlled-set hosted result because its endpoint became unavailable; Llama is the bridge endpoint across phases.
  • Uncertainty analysis: Primary uncertainty uses scenario-family clustering with paired accuracy differences, 10,000-resample percentile bootstrap 95% intervals, and paired family-level sign-flip permutation tests.A plus-one correction is applied, and non-significant p-values are not interpreted as equivalence.

7 Results

On the frozen controlled set, taxonomy exposure improved policy accuracy, but explicit state emission did not reliably help. Label sensitivity, rare complete-family success, and semantic-evidence results further limit claims that elicited intermediate states faithfully improve routing.

  • Matched ablation: State-output improved over clean policy-only by 0.6 points for Llama and 3.3 points for GPT-OSS, but neither endpoint showed a significant improvement.Llama: 95% family-cluster interval [-2.9, 4.0], p=0.8084; GPT-OSS: p=0.0710, Holm-adjusted p=0.1420.
  • Matched ablation: Taxonomy-only prompting improved Llama accuracy from 44.0% to 53.1% (+9.17 points) and GPT-OSS accuracy from 54.8% to 59.8% (+5.00 points).Both improvements were statistically significant after Holm adjustment: Llama p=0.0012 and GPT-OSS p=0.0135.
  • Label-conditioning diagnostic: Supplying benchmark-associated state labels improved routing by 7.3 points for Llama and 11.9 points for GPT-OSS, demonstrating sensitivity to externally provided labels.Because benchmark states map deterministically to policies, this is a label-conditioning diagnostic rather than evidence of a faithful internal mechanism.
  • Semantic-evidence follow-up: Explicit semantic-evidence output reduced accuracy relative to policy-only by 7.5 percentage points, with a 95% CI of [-12.7, -2.5] and Holm-adjusted p=0.0146.This follow-up used decomposed fields for relevance, temporal evidence, supersession, sufficiency, and risk or confirmation; its accuracies are not directly comparable with other protocols.
  • Counterfactual consistency: Complete four-variant family success was rare: GPT-OSS reached 1.7% of family-seed units under policy-only and 5.0% with state output, while Llama reached none.Under policy-only prompting, Llama had exactly 2/4 correct variants in 65.0% of family-seed units.

8 Discussion

The discussion shows that explicit state elicitation is not a reliable auditing guarantee: supplied labels can steer routing, while state fields redistribute policies without consistent improvement. Counterfactual family analysis and input normalization further indicate that apparent performance gains can obscure unstable decision mapping.

  • Label conditioning: Supplied benchmark-associated labels steer policy routing, but they are not self-eliciting states or evidence of a faithful internal mechanism.Correct supplied labels improve routing, whereas conflicting labels shift predictions away from the reference mapping.
  • Prompt confounds: The taxonomy-only gain is confounded because the four state definitions closely paraphrase the four policies, making their mapping recoverable without explicit labels.For example, Conflicting-or-Superseded mirrors Update, while Stale-or-Irrelevant separates Ignore from Ask.
  • Label conditioning: Approximately 15 points separate forced-wrong-state from forced-correct-state routing for both endpoints; relative to policy-only, the gaps are 7.9 points for Llama and 3.1 points for GPT-OSS.The result cautions against treating explicit benchmark-state outputs as an automatic auditing guarantee.
  • Model dependence: Model dependence makes a universal state-first recommendation too strong: GPT-OSS benefits from natural state-first prompting, whereas Llama does not and performs better with policy-first ordering.The state field pushes each model toward different policies and away from others, including opposite effects on Ask.
  • Counterfactual consistency: Family-level exact match rarely captures all four matched counterfactual variants, revealing instability that example-level accuracy and single policy changes can conceal.Pairwise transition analysis distinguishes correct policy changes from transitions to another wrong label.
  • Policy redistribution: State output redistributes policies rather than improving them: Llama shifts toward Use (+17.5 pp, 95% CI [6.67, 28.33], Holm p=0.010) and away from Ask (-14.2 pp, 95% CI [-22.50, -6.67], Holm p=0.010), while GPT-OSS shifts toward Ignore (+15.8 pp, 95% CI [7.50, 25.00], Holm p=0.003) and away from Update (-8.3 pp, 95% CI [- 13.33, -3.33], Holm pThese opposing class-level effects cancel in the overall matched-ablation result.

9 Conclusion

The controlled counterfactual audit does not support explicit state output as a reliable improvement to memory-policy classification. It instead reframes MemoryPolicy-Bench around robust semantic evidence, calibrated asking, provider-compatible protocols, and downstream evaluation.

  • Core conclusion: Explicit state output did not reliably improve memory-policy classification: the matched ablation was null for Llama and marginal for GPT-OSS.This conclusion comes from the frozen four-policy controlled counterfactual set.
  • Core conclusion: Approximately 15 points separated routing with correct versus conflicting supplied labels relative to the forced-correct condition.This label-conditioning diagnostic shows that benchmark-associated labels shift routing, but does not establish a faithful internal mechanism.
  • Implications: MemoryPolicy-Bench is best viewed as an audit of memory-policy decisions and the intermediate fields used to justify them.Development-set prompt-bundle gains remain useful signals, but controlled counterfactual evidence limits their interpretation.
  • Implications: Future progress should target robust semantic-evidence extraction, calibrated asking, provider-compatible structured-output protocols, and downstream response/action evaluation.The conclusion argues against relying on a generic state-first prompt recipe.

Limitations

The study is limited by synthetic, rule-derived benchmarks, changing endpoint panels, and a narrow evaluation of policy classification. Its taxonomy omits important deployed-agent behaviors, while hosted API evolution can introduce execution failures.

  • Limitations: Both datasets are synthetic, and rule-derived labels encode the designers’ intended policy abstraction rather than every reasonable human judgment.The frozen set is counterfactual, with labels deterministically validated and matching two annotators’ judgments on all 160 examples.
  • Limitations: Changing endpoint panels weaken direct two-model replication claims across development and controlled-set phases.Qwen3-32B was unavailable before any controlled-set hosted result; a third endpoint reproduced the state-output direction but had elevated parse failures.
  • Limitations: The experiments measure policy classification only, not downstream response quality, memory application, tool success, retrieval quality, or memory-store mutation.These omissions exclude operational behaviors such as transactional commits, provenance-aware sharing, rollback repair, and proactive recovery from execution failures.
  • Limitations: The four-policy taxonomy is intentionally narrow and excludes multi-action strategies, deletion, forgetting, privacy and scope constraints, confirmation, and multi-turn repair.Exact-match single-label scoring therefore does not cover the full strategy space of deployed memory agents.
  • Limitations: Hosted API behavior and model endpoints may evolve, creating operational failures that require execution-accounting records for later audit.GPT-OSS produced provider-side JSON validation failures for the explicit-evidence arm under the frozen request format.

Ethics Statement … P Model-Protocol Amendment

The paper audits memory-policy classification with controlled synthetic data, matched prompt ablations, label-conditioning diagnostics, counterfactual consistency tests, and provider-aware follow-ups. It finds that explicit state elicitation alone does not reliably improve policy accuracy or establish a faithful internal mechanism, while deployment risks, shortcut diagnostics, and protocol limitations remain important.

  • Ethics Statement; A Controlled V3 Construction and Validation: Frozen V3 contains 160 synthetic examples in 40 matched four-way families, with byte-identical tasks, four history records, rule-derived policies, and unanimous validation across two blinded annotators.The dataset excludes real user logs and personally identifiable information, so conclusions require validation before deployment with real histories.
  • B Frozen-V3 Matched-Ablation Prompts; C Frozen-V3 State-Intervention Prompts: The matched arms expose the same history and task while varying policy definitions, state definitions, state-output ordering, rationale, and deterministic state-only routing.The four policies are Use, Ignore, Update, and Ask; the four states are Active, Stale_or_Irrelevant, Conflicting_or_Superseded, and Underspecified.
  • C Frozen-V3 State-Intervention Prompts; E Label-Conditioning Diagnostic and Wrong-State Construction; D Artifact Name Mapping: Forced-state conditions are label-conditioning diagnostics because Frozen V3 deterministically maps Active→Use, Stale-or-Irrelevant→Ignore, Conflicting-or-Superseded→Update, and Underspecified→Ask.Wrong states are sampled using fixed seed 20270719 and held constant across models and seeds, testing sensitivity to supplied labels rather than proving faithful internal states.
  • F Full Frozen-V3 Results; F.1 Per-Policy Decomposition of the Matched Ablation; F.2 Third-Endpoint Replication (llama-3.1-8b-instant); G Family-Level Counterfactual Analysis: 13.8% and 13.6% are Llama expected-transition accuracies for policy-only and state-output, while GPT-OSS improves from 23.5% to 29.2% with state output but still misses 32.8% and 30.3%.Supplied reference labels improve expected transitions, consistent with answer-associated label sensitivity; family exact match requires all four variants to be correct.
  • H Seed-Stability Diagnostics: Among unanimous examples, per-call accuracy is 50.5% and 48.4% for Llama policy-only and state-output, versus 61.7% and 63.9% for GPT-OSS.With only three seeds, unanimity and entropy are stochastic-instability diagnostics rather than calibrated uncertainty estimates.
  • I Normalized-Input Probe Protocol; J Rule-Based Error Taxonomy; K Per-Policy Metrics and Confusion Matrices; L Statistical Procedures: The normalized-input leakage probe decodes at 0.375 word-grouped accuracy and 0.33125 character-grouped accuracy, below the 0.9 near-trivial threshold and therefore not flagged as answer leaking.The probe used 160 examples, 40 families, three seeds, temperature 0.7, and 0 parse failures for each normalized-input model-arm.
  • M Shortcut-Transfer Diagnostic; J Rule-Based Error Taxonomy; K Per-Policy Metrics and Confusion Matrices; L Statistical Procedures: The development-set TF-IDF transfer diagnostic reaches 25.0% accuracy and 13.9% macro F1 on Frozen V3, but is descriptive because the development set lacks positive Ignore examples.Errors are automatically categorized from metadata and predictions, with overlapping categories; parse failures count as incorrect and family-clustered tests use bootstrap and sign-flip permutation procedures.

Q Historical Development-Set Results … U Extended Related Work and Supplementary Discussion

Historical development-set gains were weakened by lexical separability, missing positive Ignore cases, and bundled prompt changes. Controlled and follow-up audits therefore emphasize isolated interventions, execution validity, family consistency, and the limits of intermediate evidence elicitation.

  • Q Historical Development-Set Results: Historical audits retained state confusion matrices, ablations, oracle-worded routing diagnostics, and token-cost comparisons for transparency.The historical oracle-worded routing diagnostic is not a neutral mapped-state intervention or deterministic oracle.
  • Q Historical Development-Set Results: The 480-example development set overstated gains: the state-structured bundle improved exact-match policy accuracy by 14.8 points for Llama and 10.2 points for Qwen.The set was lexically separable, lacked positive Ignore examples, and changed taxonomy, wording, state output, rationale output, and token budget.
  • R Policy Confusion Matrices: Development-set policy confusion matrices were reported separately for Llama-3.3-70B and Qwen3-32B under the full 480-example Groq protocol with seeds 101/102/103.Matrices include predicted policies and parse failures, while oracle-worded routing rows use the historical oracle-worded routing prompt.
  • S Execution Completeness, Failures, Tokens, and Cost: The frozen-V3 hosted execution completed 4,800 matched-ablation calls and 5,760 label-conditioning diagnostic calls, with one final parse failure counted as incorrect.Earlier 53 transient provider-failure records were later completed under the frozen protocol and excluded as separate valid predictions.
  • T Main-Text Design Materials: The representative controlled example held the task byte-identical across all four variants, exposing the synthetic four-way family structure rather than a naturalistic conversation.The task asked models to choose the preference option for Jordan’s flight seat choice.
  • U Extended Related Work and Supplementary Discussion: Long-horizon personalization introduces safety and privacy risks including training-data emission, private-attribute inference, and the need for user-level privacy protections.These risks are identified in the extended related-work discussion.
  • U Extended Related Work and Supplementary Discussion: Seed disagreement is a diagnostic warning rather than calibrated confidence, and unanimity does not guarantee correctness because unanimous predictions can remain wrong.The audit therefore reports per-call accuracy by unanimity, majority-vote accuracy for 2–1 splits, and full-disagreement counts.
  • U Extended Related Work and Supplementary Discussion: Decomposed semantic evidence failed to improve routing: Llama performed worse, while GPT-OSS could not be cleanly interpreted because the provider rejected explicit-evidence requests under JSON-mode validation.The follow-up avoided supplying the old benchmark-state label but still produced no positive result.
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