WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents

Lu Yan; Xiangyu Zhang; Xuan Chen

REVIEW 3 major objections 5 minor 6 references

WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A new diagnostic pipeline finds that conflicts between standing rules inside a single prompt policy are behaviorally live: 64.6% of jointly governed trials violate at least one source rule.

desk verdict A genuinely useful diagnostic pipeline for within-policy prompt conflicts; the headline 35.4% figure is honestly framed but needs a compatible-pair control before it can carry a conflict-specific reading. read the letter →

arxiv 2605.27784 v2 pith:KNGMWN47 submitted 2026-05-27 cs.AI

Lu Yan , Xuan Chen , Xiangyu Zhang This is my paper · ORCID

classification cs.AI

keywords LLMagentspromptpoliciesinstructionconflictsruleextractionsatisfiabilitytriagebehavioralevaluationresolutionprofileswitnessconstruction

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

WIRE asks a question existing benchmarks skip: when two standing rules inside one prompt policy both apply to the same state, how does an LLM agent actually resolve the pressure? It extracts source-grounded rules from six prompt policies, encodes them as logical clauses, and uses satisfiability checks only to nominate candidate collisions; concrete witnesses are then run through subject models and judged against the original rule text. Across 13,335 jointly governed, judgeable trials, only 35.4% satisfy both rules, so 64.6% violate at least one governed source rule. The paper is explicit that these are conditional diagnostics for WIRE-selected candidates, not deployment-frequency or causal excess-failure estimates, and that they reframe prompt reliability as an interaction problem: failures can emerge from the ordinary composition of reasonable standing rules.

What carries the argument

The load-bearing object is PyRule, a restricted normal form that rewrites each extracted source rule into atomic clauses of the form (activation condition, force sign, behavior primitive, argument tuple, decision surface). Decision surfaces are projected from behavior primitives and arguments, and a satisfiability check over conjoined activation conditions plus a surface-specific collision formula nominates same-surface hard-collision candidates. Critically, the solver only filters candidates; behavioral resolution is measured by executing subject models on concrete witnesses and judging outputs against the original quoted source-rule text. The four-cell resolution profile (q11, q10, q01, q0

What would settle it

Concrete check: have independent human annotators hand-write gold-standard logical rules for all six policies and compare WIRE's candidate set against gold-standard same-surface hard-collision pairs; if many genuine conflicts are absent or many WIRE candidates do not correspond to gold-standard collisions, the diagnostic claim fails. A second check: add rule-reordering and single-rule controls; if non-joint compliance stays near 64.6% even when only one rule governs or when source order is reversed, the reported pressure is not specifically about conflict.

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Extended reading notes

Core claim

The central discovery is that within-policy instruction collisions are sparse in symbolic clause space but behaviorally live once realized as concrete states. From 30,944 clause-pair comparisons across six policies, only 170 source-rule pairs survive satisfiability triage as encoded hard-collision candidates (0.55%), and 1,402 concrete witnesses realize them. In policy-only evaluation, those witnesses yield 13,335 trials in which both source rules govern after generation and both compliance labels are judgeable; only 35.4% land in joint compliance, and source-order asymmetry varies by policy and model. WIRE's four-cell resolution profile—satisfy both, earlier only, later only, neither—shows

Load-bearing premise

The whole pipeline rests on the assumption that WIRE's extraction and logical rewriting of each natural-language rule preserves what the rule actually means; if rules are missed, rewritten too broadly, or labeled with wrong semantics, every candidate pair and behavioral profile inherits that bias, which the paper's limitations section explicitly acknowledges.

Editorial extensions

If this is right

Prompt policies become testable control artifacts: measuring single-rule compliance is insufficient, and interactions among standing rules must be part of evaluation.
Symbolic triage is a selective filter: only 0.55% of clause-pair comparisons survive as encoded hard-collision candidates, yet those candidates produce non-joint compliance in 64.6% of jointly governed trials.
Resolution profiles are policy-, model-, and interface-specific; the tool-action harness shifts profiles without uniformly improving compliance, so policy-only and tool-action profiles should be reported separately.
The four-cell profile reveals one-sided rule prioritization and joint failure that scalar pass/fail metrics hide.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference — If matched compatible-pair and single-rule controls were added, the 64.6% non-joint-compliance could be partitioned into conflict-driven excess failure versus ordinary instruction-following difficulty; the paper's current design intentionally leaves that causal quantity unestimated.
Editorial inference — The same PyRule encoding could be extended from pairwise hard REQUIRE/FORBID collisions to soft signs (preferences and aversions) and multi-rule interactions, which the paper lists as out of scope.
Editorial inference — Rule-reordering ablations would test whether the observed q10/q01 asymmetry is causally driven by source order; if so, reordering a policy should flip the asymmetry within a fixed model and witness set.
Editorial inference — Since witnesses deliberately maximize activation of latent conflicts, a deployment-frequency estimate would require sampling real traffic; the profiles should not be read as rates in production.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague —

If you care about how LLM agents handle conflicts inside a single standing policy, this is worth a read. The real contribution is the pipeline: WIRE extracts source-grounded rules from a prompt policy, encodes them into a restricted clause language, uses SAT only to nominate same-surface hard-collision candidates, then realizes those candidates as concrete witnesses and measures models' actual resolution behavior against the original English rule text. That separation of symbolic triage from behavioral measurement is the right instinct, and it avoids the usual trap of treating an encoded collision as a predicted failure.

The paper does several things well. It is unusually honest: the authors repeatedly state that their percentages are conditional diagnostics for WIRE-selected candidates, not deployment-frequency or causal excess-failure estimates. The human audits—95% extraction faithfulness, 93.8% encoding faithfulness, 84.2% judge agreement on 466 samples—show real effort, even if sample sizes are modest. The per-policy, per-model, and POT-vs-TAH profiles are a genuinely new kind of data for prompt-policy evaluation.

The soft spots are proportional. The biggest is the missing matched compatible-pair (or single-rule) control. The paper explicitly says in Section 5 that it does not include such controls, and it disowns causal claims—so the authors know. But the headline 64.6% non-joint-compliance rate, and the word "behaviorally live" in the conclusion, invite a conflict-specific reading. Without a baseline showing that ordinary pairs of rules under the same witness distribution do better, you cannot distinguish "these two rules collide and cause failures" from "satisfying any two rules in these policies under adversarial witnesses is hard." That is a real gap. I would not call it fatal, because the paper's stated purpose is a measurement tool rather than a causal estimate, but it should be fixed before anyone cites the 35.4% number as evidence about conflict.

Secondary concerns: the whole pipeline rests on LLM extraction, encoding, and judging, and the audits are small; 84.2% per-label agreement is not high. No code or data are released, so independent reproduction is currently impossible. The SAT stage's candidate set inherits any encoding bias, a point the Limitations section acknowledges honestly.

Bottom line: this is a serious, careful piece of work with a novel artifact and a mostly defensible central claim, once you read the qualifications. It deserves a proper peer review. I'd send it back asking for a compatible-pair control and, ideally, code/data release, but I would not desk-reject it.

Referee Report

3 major / 5 minor

Summary. The paper introduces WIRE, a neuro-symbolic pipeline for finding and behaviorally profiling within-policy rule collisions in LLM prompt policies. WIRE extracts source-grounded rules, encodes them as PyRule clauses, uses SAT checks to nominate same-surface hard-collision candidate pairs, realizes them as concrete co-governance witnesses, and measures resolution profiles (joint compliance, earlier-rule-only, later-rule-only, both-violated) by judging model outputs against the original source-rule text. Across six public prompt policies the pipeline extracts 276 rules and 560 clauses, retains 170 candidate source-rule pairs, and produces 13,335 jointly governed, judgeable policy-only trials; only 35.4% satisfy both governed rules. The paper repeatedly and explicitly frames these numbers as conditional diagnostics for WIRE-selected candidates, not as deployment-frequency or causal excess-failure estimates.

Significance. WIRE addresses a real and under-studied failure mode: long-lived prompt policies whose standing rules can jointly govern a single state. The paper is unusually careful about scope: it preserves source provenance, separates symbolic collision from behavioral resolution, disclaims causal and frequency interpretations, and audits extraction, encoding, triage recall, and response judging. The four-cell resolution profile is a useful descriptive representation, and the observation that WIRE-selected candidates do not yield uniform joint compliance across policies, models, and interfaces is valuable for regression testing and policy repair. The main limitation is that the headline 64.6% non-joint-compliance number lacks a compatible-pair or single-rule control, leaving the conflict-specific interpretation underdetermined; this is a fixable experimental-design gap rather than a fatal flaw.

major comments (3)

[Section 5, 'Setup'; Section 5.2, Table 2] The pooled 1−q11 = 64.6% is the principal empirical evidence that WIRE-selected collisions are behaviorally live, but the design includes no matched compatible-pair, single-rule, or rule-removal control. The manuscript explicitly states this and disclaims causal excess failure, which is appropriate. Nevertheless, the headline statistic and the word 'live' in the abstract and conclusion invite a conflict-specific reading. As it stands, 35.4% joint compliance could reflect generic multi-rule difficulty, judge strictness, or witness-selection artifacts. I request either a matched compatible-pair control—sampling same-policy pairs that share a decision surface but pass neither SAT collision gate and realizing them with the same tier cascade—or a consistent rewording of the abstract/conclusion so that 'behaviorally live' means only 'observable' and 64.6% is not presented as conflict-induced f
[Section 5.2, Eq. (10)] The support set S is, by design, narrower than the set of submitted rollouts, but the paper never reports the number of submitted rollouts per cell or the fraction excluded by each criterion (activation drift, provider refusal, malformed trace, unjudgeable label). The reported 41,983 subject-model calls cannot be reconciled with G=13,335 from the tables. Without this denominator, the aggregate q cells may be dominated by a selected subset of witnesses or cells, and the policy/model comparisons in Tables 3–4 are harder to interpret. Please report per-policy and per-model submitted-versus-support counts and the distribution of exclusion categories.
[Section 5.5, Table 5; Eq. (13)] Response judging shows 84.2% per-label agreement over 1,864 labels, but the central 35.4% versus 64.6% split and the individual q-cell values are reported without uncertainty bounds. Since each support trial contributes four labels and the headline aggregate pools 13,335 trials, a 15.8% per-label error rate could materially shift the profile, especially if errors are correlated with the source rule or witness type. Please provide confidence intervals, bootstrap estimates, or a sensitivity analysis under label noise for the pooled q cells.

minor comments (5)

[Figure 2] 'LoveableAgent' is a typo; the policy name is 'Lovable' elsewhere. Use one spelling consistently.
[Figure 1] The panel text 'R3 and r9 are likely conflicts!' is informal and conflicts with the paper's careful caveats. Consider rewording to 'R3 and r9 are candidates for behavioral testing.'
[Section 5.4, Table 4] mini-SWE cells have very small support, yet percentages are shown to one decimal. Consider suppressing decimal precision or annotating each cell with its support count to avoid overstating precision.
[Section 5, Setup] The paper does not state whether WIRE code and the constructed witnesses will be released. An availability statement would support reproducibility claims.
[References] Several references are very recent or appear as preprints (e.g., Mason 2026; He et al. 2026). Please verify publication status and update citations where applicable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SAT stage only nominates candidates; behavioral profiles are measured on fresh model outputs against the original source text.

full rationale

The derivation chain is not circular. WIRE's symbolic stage defines candidate pairs A(P) via encoded clause satisfiability (Eqs. 6-7), but the paper explicitly states this is only a candidate filter: "The solver is only a candidate filter: behavioral resolution is measured on concrete model outputs, not inferred from the symbolic abstraction alone." The behavioral profiles (Eqs. 10-13) are computed from completed trials in which both rules still govern after generation and both compliance labels are judgeable, with judging done against "the quoted text and gist of both source rules." Thus the headline 35.4% joint compliance is an independently measured outcome, not a quantity inserted into the candidate definition. There is no fitted parameter renamed as a prediction, no load-bearing self-citation (all cited prior work is external and used only as related work), no imported uniqueness theorem, and no ansatz smuggled in via citation. The paper's own limitations—"missed rules, over-broad clauses, conservative decision-surface projections, and imperfect semantic labels can affect which conflicts are tested"—concern upstream validity rather than definitional circularity. The absence of rule-removal/matched compatible-pair/single-rule controls is an experimental-design limitation, and the paper explicitly disclaims causal excess-failure estimates: "we do not include rule-removal, rule-reordering, priority-insertion, matched compatible-pair, or single-rule controls. Thus the reported profiles are conditional diagnostics..." A missing control weakens interpretation; it does not make the output equal to the input. Extraction and encoding are audited against human raters (95.0% and 93.8%), and response judging has finite but reported agreement (84.2%), all of which are measurement-error concerns, not circularity. Every load-bearing step either is externally grounded in the original policy text or is an audited measurement, so the central claim has independent empirical content.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The central empirical result rests on the faithfulness of the extraction/encoding/judging chain. The paper audits each stage on small samples but does not exhaustively certify the transformation from natural language to PyRule. The behavioral profiles are conditional on the candidate set produced by that transformation, so all reported percentages inherit any bias in rule extraction or clause encoding.

assumptions (5)

domain assumption Extracted source rules faithfully represent the policy's normative content.
WIRE starts with R(P) from LLM-based extraction of prescriptive spans. If the extractor omits rules or includes non-normative text, the candidate set and profiles are biased. The audit reports 95.0% faithfulness on 80 rules, which is strong but not exhaustive.
ad hoc to paper PyRule encoding and decision-surface projections preserve hard-collision semantics.
The SAT triage in §4.3 uses only encoded clauses and hand-designed surface metadata. The paper notes in Limitations that 'over-broad clauses, conservative decision-surface projections, and imperfect semantic labels can affect which conflicts are tested.' The invariance claims in Appendix C.5 are internal to the encoding, not to the original language.
domain assumption Post-generation compliance judging is accurate enough for aggregate profiles.
Response judging uses claude-sonnet-4-6 and agrees with a human rater at 84.2% per label over 466 samples. The authors therefore treat profiles as aggregate estimates, but label error could shift cell percentages by several points.
domain assumption The realized witnesses are realistic pre-generation states that genuinely co-govern both rules.
Witnesses are retrieved or synthesized and accepted by an LLM verifier checking concreteness and co-governance (Eq. 8). If the verifier is over-permissive, some trials may not actually present both rules, although post-generation governance checks partially mitigate this.
standard math SAT solver correctness and first-order logic semantics.
Candidate triage relies on standard SAT solving; the paper does not prove the solver's correctness, but this is a conventional background assumption.

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Cite this review

Pith. "Pith review of WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents." pith.science (2026). https://pith.science/paper/KNGMWN47

@misc{pith2026260527784,
  author       = {Pith},
  title        = {Pith review of: WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KNGMWN47}},
  note         = {Machine review of arXiv:2605.27784}
}

read the original abstract

LLM agents are governed by long-lived prompt policies, where individually reasonable stand- ing rules can jointly govern the same pre- generation state. Existing instruction-following evaluations usually ask whether a model satis- fies explicit constraints, but they do not show how a model resolves pressure among rules inside one standing policy. We introduce WIRE, a witnessed resolu- tion profiler for prompt policies. WIRE ex- tracts source-grounded rules, encodes them as PYRULE clauses, uses satisfiability checks only to nominate same-surface hard-collision can- didates, realizes those candidates as concrete co-governance witnesses, and executes subject models to produce a four-cell resolution profile: satisfy both rules, only the earlier rule, only the later rule, or neither. Across six public prompt policies, WIRE ex- tracts 276 source rules and 560 clauses, clas- sifies 30,944 within-policy clause-pair com- parisons, retains 170 encoded hard-collision source-rule pairs, and realizes 1,402 concrete witnesses. In policy-only evaluation, these wit- nesses yield 13,335 jointly governed, judgeable trials; only 35.4% satisfy both governed rules. The resulting profiles reveal policy-specific, model-specific, and tool-interface-specific res- olution patterns. WIRE is not a proof of natural-language contra- diction, a deployment-frequency estimator, or a root-cause diagnosis. It is a measurement tool that returns reproducible witnesses and aggre- gate profiles for inspection, regression testing, and repair.

Figures

Figures reproduced from arXiv: 2605.27784 by the authors.

**Figure 2.** Witness provenance by policy. Each stacked [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Mean cumulative witness-construction time [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

Review history (2 revisions) →

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