{"id":"7205984d-b91e-48a5-8434-69084e9f8304","arxiv_id":"2605.27784","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A new profiler, WIRE, tests within-policy rule collisions in LLM agents and finds models violate at least one governed rule in 64.6% of jointly governed trials.","lead":"WIRE finds pairs of standing rules inside a single LLM prompt policy that can collide on the same request, then measures how different models resolve the clash. On six public prompt policies, only 35.4% of jointly governed trials satisfied both rules, showing collisions are sparse in clause space but behaviorally live.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Missing matched compatible-pair control leaves 'behaviorally live' underdetermined: 35.4% joint compliance may reflect generic multi-rule difficulty, not conflict.","rationale":"The reader's weakest assumption—PyRule encoding faithfulness—is real and upstream, but the paper provides a reasonable (if small) audit and mitigates by judging against original source text. The more decisive gap is experimental design: the paper never compares WIRE-selected hard-collision pairs against matched co-governance pairs without a hard collision. The headline statistic (35.4% joint compliance) therefore cannot distinguish 'conflict-specific pressure' from 'generic difficulty of simultaneously satisfying two rules' or 'judge strictness.' The authors are transparent about the absence of controls, and their conditional-diagnostic framing is a legitimate partial defense. However, the contribution list says the pipeline exposes 'distinct behavioral signatures' and the conclusion says conflicts are 'behaviorally live'; those phrasings assert more than the design can establish. A compatible-pair control is the natural, inexpensive check and would settle the interpretation. The concrete test should be run on the same witness set, with the same K rollouts and judging, and analyzed both pooled and per policy, because Table 2 shows large policy heterogeneity (OpenHands q11=52.3% vs Lovable 23.3%); a control that only shifts small policies is less informative. If the control shows similar violation rates, the paper should soften the 'behaviorally live' claim to 'co-governance pressure is observable,' which is still a useful finding but not a conflict-specific one. If the control shows a clear separation, the central claim is supported and the remaining concerns (judge agreement, encoding audit) become minor. Since the paper already received CONDITIONAL for related reasons, my recommendation is to retain that verdict.","tokens_in":20418,"tokens_out":5948,"duration_ms":65022,"concrete_test":"For each policy and each WIRE-selected candidate pair (ρi,ρj), construct a matched control pair (ρi,ρ'j) that shares ρi's activation surface and co-governs the same witnesses, but where the encoded clauses are satisfiable (e.g., change FORBID to REQUIRE of a compatible action or alter the forbidden argument to a different surface value). Run the identical witness realization, K=5 rollouts, and post-generation judging pipeline on these controls. Compare pooled q11 and 1−q11 with the candidate-pair results, using a bootstrap or chi-square test. If compatible controls show joint compliance near the same 35.4%, the violation rate is not conflict-specific and the 'behaviorally live' claim should be weakened. If controls show substantially higher q11, the claim is supported. Also report per-policy results because Table 2 shows large policy heterogeneity.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that within-policy conflicts are behaviorally live: only 35.4% of 13,335 jointly governed, judgeable POT trials satisfy both WIRE-selected rules. The paper explicitly disclaims causal excess-failure estimates and says it does not include rule-removal, rule-reordering, priority-insertion, matched compatible-pair, or single-rule controls (Section 5 Setup). This is the load-bearing gap. Without a matched compatible-pair baseline, the 64.6% violation rate can be explained by ordinary difficulty of satisfying any two standing rules under the witness distribution, by judge strictness, or by witness artifacts—not by the conflict itself. The paper's own language ('behaviorally live', 'expose distinct behavioral signatures') invites a conflict-specific reading, and the current design cannot support it. Encoding fidelity and judge agreement are secondary: even with perfect encoding and judging, the missing control leaves the interpretation underdetermined because there is no reference point showing that same-surface co-governance without a hard collision behaves differently. The audit in Section 5.5 addresses upstream and downstream measurement error but not this experimental-design gap.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":20685,"tokens_out":7347,"duration_ms":79690,"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":[{"comment":"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":"Section 5, 'Setup'; Section 5.2, Table 2"},{"comment":"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":"Section 5.2, Eq. (10)"},{"comment":"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.","section":"Section 5.5, Table 5; Eq. (13)"}],"minor_comments":[{"comment":"'LoveableAgent' is a typo; the policy name is 'Lovable' elsewhere. Use one spelling consistently.","section":"Figure 2"},{"comment":"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":"Figure 1"},{"comment":"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":"Section 5.4, Table 4"},{"comment":"The paper does not state whether WIRE code and the constructed witnesses will be released. An availability statement would support reproducibility claims.","section":"Section 5, Setup"},{"comment":"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.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The paper is methodologically strong and unusually careful in its disclaimers; the central concern is that the headline 64.6% non-joint-compliance statistic needs a matched compatible-pair control and better denominator reporting to support the 'behaviorally live' framing. If the authors add these, I would be willing to accept. No circularity concern: the behavioral measurements are on fresh model outputs judged against the original source text."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague —\n\nIf 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.\n\nThe 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.\n\nThe 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.\n\nSecondary 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.\n\nBottom 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.","headline":"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.","tokens_in":21118,"tokens_out":4469,"would_cite":true,"duration_ms":46404,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"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.","keywords":["LLM agents","prompt policies","instruction conflicts","rule extraction","satisfiability triage","behavioral evaluation","resolution profiles","witness construction"],"falsifier":"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.","tokens_in":20317,"feed_emoji":"⚖️","tokens_out":7529,"duration_ms":70205,"temperature":0.7,"pith_summary":"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.","feed_headline":"In six LLM policies, 64.6% of jointly governed trials break a rule","feed_subtitle":"A new profiler measures how models resolve clashes between rules inside one prompt policy—not just whether each rule is followed alone.","key_machinery":"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","core_discovery":"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","pith_inferences":["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."],"forward_implications":["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."],"fun_headline_variants":["In LLM policies, 0.55% of rule pairs cause 64.6% failures","When LLM rules truly collide, models obey both only 35.4% of the time","WIRE profiles: within-policy collisions are rare but behaviorally costly","Only 35.4% of jointly governed trials satisfy both LLM rules","Rare symbolic clashes in prompt policies yield frequent real-world violations"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["In LLM policies, 0.55% of rule pairs cause 64.6% failures","When LLM rules truly collide, models obey both only 35.4% of the time","WIRE profiles: within-policy collisions are rare but behaviorally costly","Only 35.4% of jointly governed trials satisfy both LLM rules","Rare symbolic clashes in prompt policies yield frequent real-world violations"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001126,"raw_usage":{"total_tokens":4564,"prompt_tokens":835,"completion_tokens":3729,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":579,"completion_tokens_details":{"reasoning_tokens":3623}},"tokens_in":579,"tokens_out":3729,"duration_ms":26785,"temperature":1.0,"reasoning_tokens":3623,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-02T12:55:24.839813+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[],"review_version":2}