{"id":"227792a0-36bd-4fc3-938e-fd8a8b7e3849","arxiv_id":"2607.28529","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Gating a security expert by its own confidence (max-prob or entropy) fixes inverted acceptance in co-decoding and improves joint functional-security code generation, especially on OOD CWEs.","lead":"CoGate adds a confidence gate to security co-decoding so a small expert steers code LLMs only when it is sure. That cuts noise on unseen vulnerability patterns and raises the rate of code that is both correct and secure.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"No significant objection identified beyond the reader's already-flagged τ-selection issue.","rationale":"I agree with the Reader that the paper's diagnosis and fix are sound and that the single most fragile point is family-specific τ chosen by maximizing the headline OOD metric. That is already correctly identified and does not require a harsher verdict: the theory (Props. 1–2), the gate construction (Eq. 4, Alg. 1), the multi-model tables, and the counterfactual utility analysis cohere. No independent load-bearing attack (mis-stated acceptance rule, confounded LoRA baseline, or non-reproducible claim) survives scrutiny. CONDITIONAL remains appropriate pending artifacts, variance, and a non-CWEval τ-robustness check; I do not move the needle further.","tokens_in":15173,"tokens_out":548,"duration_ms":11183,"concrete_test":"Re-select each family τ on a criterion that never sees CWEval labels (e.g., maximize Security-Suite ratio on a disjoint CWE split, or fix τ=0.6 for all families). Re-evaluate Table 1 Func-Sec@10; if the Qwen entropy gain falls below ~5 points or the Fig. 2 utility peak disappears, the OOD claim weakens. Otherwise the reader's concern is mitigated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is internally consistent: Prop. 1–2 correctly show that CoSec+'s acceptance statistic is nonincreasing in St(x̃) and collapses to base-driven noise under flat St; Algorithm 1's absolute gate (cmax or cent > τ) directly blocks that regime; Table 1 and the token-level utility curves in Fig. 2 show the predicted OOD gains and inverted-U sweet spot. The reader's weakest assumption (τ tuned on held-out CWEval Func-Sec@10) is already the softest empirical link, but it is not a hidden contradiction: §5.2 demonstrates a broad plateau (≈0.5–0.7) rather than a knife-edge, temperature sweeps in Table 2 preserve the ranking, and the gate strictly generalizes CoSec+ at τ≤1/|V|. No stronger load-bearing flaw (e.g., circular metric definition, inverted proposition, or confounded baseline) appears in the argument.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The paper diagnoses a failure mode in co-decoding for secure code generation (CoSec/CoSec+): the acceptance statistic min(1, B_t(x̃)/S_t(x̃)) is nonincreasing in the expert’s own probability on its proposal (Prop. 1), so an unconfident expert is accepted more readily and, in the near-uniform limit, injects tokens carrying no security content (Prop. 2). It proposes CoGate, which inserts an absolute confidence gate g_t = 1[c(S_t) > τ] (max-probability or normalized entropy) before the relative acceptance test, so the expert abstains when unconfident. The method is evaluated on six backends across four families (CodeGen, StarCoder, DeepSeek-Coder, Qwen2.5-Coder) on HumanEval, an in-distribution security suite, and the joint Func-Sec CWEval benchmark, with a token-level counterfactual intervention-utility study (Fig. 2) and a temperature sweep (Table 2). Reported gains reach up to +12.6 absolute Func-Sec@10 over CoSec+ on CWEval (Qwen2.5-Coder-14B, entropy gate).","tokens_in":15461,"tokens_out":1593,"duration_ms":42672,"significance":"If the result holds, the paper makes a clean and useful contribution to decoding-time security steering: it separates absolute expert reliability from relative preference, a distinction that prior co-decoding work conflates by construction. Propositions 1–2 are elementary but correctly identify an inverted acceptance incentive and a security-signal collapse under flat expert distributions—precisely the OOD regime that joint Func-Sec metrics expose. Algorithm 1 is simple, training-free, and strictly generalizes CoSec+ at low τ. The multi-backend evaluation, temperature study, and token-level utility curves with confidence intervals are stronger empirical practice than is typical in this line of work. The practical implication is that confidence-aware abstention is a low-cost way to keep co-decoding from injecting noise on unseen CWEs without retraining the base model.","major_comments":[{"comment":"§5.1 (Implementation) selects the family-specific gate threshold τ by maximizing CWEval Func-Sec@10 on a held-out development split, then freezes it for all reported results, including the headline CWEval numbers. This is ordinary hyperparameter selection, but it is also selection on the same joint metric and distribution family used for the main claim. The paper should state the size and construction of that split, whether any CWEval CWE/language leakage into the split is possible, and report the selected τ values per family. §5.2’s inverted-U and plateau (≈0.5–0.7) mitigate knife-edge concerns, but without those details the OOD gains remain harder to audit than the rest of the evaluation.","section":"§5.1 Implementation; §5.2"},{"comment":"Table 1 does not uniformly support the abstract/§5.1 claim that CoGate “outperforms existing co-decoding methods (CoSec+) across multiple benchmarks.” Counterexamples include StarCoder-1B Max-Prob Func-Sec@10 (29.6 vs CoSec+ 31.0), StarCoder-7B Max-Prob Func-Sec@10 (tied at 32.8), and several HumanEval Pass@1 drops (e.g., StarCoder-7B Entropy 15.2 vs CoSec+ 16.5; CodeGen-6.1B Max-Prob 13.9 vs Original 17.0). The largest gain (+12.6 Func-Sec@10) is concentrated on Qwen2.5-Coder-14B with the entropy gate. The results narrative should characterize when gating helps (OOD joint Func-Sec, higher T) versus when it is neutral or harmful, rather than summarizing as broad outperformance.","section":"Table 1; Abstract; §5.1 Overview of Results"},{"comment":"The token-level intervention-utility analysis that validates Prop. 2 and the τ sweet spot (§5.2, Fig. 2, Eqs. 5–9) is reported only for StarCoder-7B with a StarCoder-1B expert. Because the central mechanistic claim is that unconfident interventions are harmful and that a mid-range τ removes them, repeating the counterfactual fork (CoGate / unconfident CoSec+ / Base) on at least one other family (e.g., CodeGen or DeepSeek-Coder) would substantially strengthen the load-bearing empirical link between the propositions and the table-level gains.","section":"§5.2; Figure 2"}],"minor_comments":[{"comment":"§2.1: “critival” → “critical”; several other extraction/spacing artifacts appear in the related-work and method text (e.g., “co-decodingapproachesdoesnotconsider”). A full proofread pass is needed.","section":"§2.1"},{"comment":"§4.4 introduces false-accept / false-reject at the token level via an idealized oracle R_t but never reports empirical FA/FR rates or a proxy. Even a coarse breakdown (fraction of steps gated closed; fraction of closed-gate steps that would have failed Func-Sec under forced expert accept) would make the trade-off concrete.","section":"§4.4"},{"comment":"§5.3’s recommendation (max-prob for in-distribution, entropy for OOD) is reasonable given Table 1 but is post hoc. State it as an empirical guideline and note that practitioners still need a selection rule when the OOD status of the deployment mix is unknown.","section":"§5.3"},{"comment":"StarCoder-1B is both expert and target with knowledge distillation skipped (§5.1). Flag this configuration more visibly in Table 1 or the caption so readers do not treat it as a standard teacher–student co-decoding setup.","section":"§5.1 LLM Backends"},{"comment":"Default acceptance threshold a = 0.3 is inherited from CoSec+ and never ablated jointly with τ. A brief note on interaction (or a small grid) would clarify that the reported gains are not an artifact of a fixed a.","section":"§5.1; Algorithm 1"},{"comment":"Figure 1 and Algorithm 1 are clear; consider adding the entropy definition next to the max-prob gate in the figure so both signals are visible in the overview.","section":"Figure 1"}],"recommendation":"minor_revision","confidential_remarks":"The contribution is incremental over CoSec+ but cleanly motivated and better evaluated on joint Func-Sec than much of the secure-code-generation literature. I do not see a load-bearing mathematical error. The main risk is overstated generality in the abstract relative to Table 1’s mixed cells; that is fixable in revision. Fit for a solid SE/security venue is reasonable if the τ-selection and mixed-result narrative are tightened. No concerns about misconduct or scope mismatch."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The useful bit here is not another security expert—it is the diagnosis. Props. 1–2 show that CoSec+’s accept statistic min(1, B/S) is nonincreasing in the expert’s own probability on the token it just proposed, so a flat expert gets accepted more readily and, in the limit, injects base-driven noise with no security content. That failure mode is real, elementary, and easy to miss if you only score in-distribution CWEs separately from correctness.\n\nCoGate’s fix is correspondingly small: an absolute confidence gate (max-prob or normalized entropy) before the usual ratio test. Algorithm 1 is clear, strictly generalizes CoSec+ at low τ, and the empirics are the right shape for the claim—six backends, HumanEval + SVEN suite + CWEval, temperature sweeps, and a token-level counterfactual utility study with CIs that shows the predicted inverted-U (harmful at low confidence, neutral when the expert is silenced). Gains concentrate where they should: OOD joint Func-Sec, up to about +12.6 absolute on Qwen-14B entropy vs CoSec+.\n\nSoft spots are ordinary, not load-bearing. τ is chosen per family on held-out CWEval Func-Sec@10, so there is some risk of tuning to the headline OOD score; §5.2’s plateau around 0.5–0.7 and the temperature table soften that, but variance on the main table and released code would make the result easier to trust. Novelty is incremental on CoSec/CoSec+ and standard confidence proxies—the absolute-vs-relative split and the utility analysis are the real additions, not a new paradigm.\n\nThis is for people doing decoding-time steering or secure code gen who already care about CoSec-style methods and joint functional-security metrics. Math is sound, citations are on-point, circularity is low. I would send it to referees and I would read the camera-ready if they ship artifacts and tighten the τ story. Worth engaging if you work in this lane; skip if you only care about training-time defenses.","headline":"Clean diagnosis of inverted confidence in CoSec+-style acceptance, plus a simple gate that actually helps on joint Func-Sec OOD eval.","tokens_in":16057,"tokens_out":537,"would_cite":true,"duration_ms":12519,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"Co-decoding for secure code accepts unconfident expert tokens more readily; gating on absolute confidence stops that noise and raises joint functional-security scores.","keywords":["secure code generation","co-decoding","confidence gating","decoding-time steering","large language models","out-of-distribution","Func-Sec"],"falsifier":"Retune or freeze the gate threshold on one benchmark family, then measure whether token-level intervention utility stays positive and Func-Sec@10 still beats ungated co-decoding on held-out OOD CWEs and at temperatures the threshold was not chosen for; a flat or negative utility curve would falsify the claim.","tokens_in":16057,"feed_emoji":"🔒","tokens_out":1005,"duration_ms":21324,"temperature":0.7,"pith_summary":"Large code models often emit insecure programs because their training data contains unsafe patterns. A popular fix is co-decoding: a small security expert proposes the next token alongside the base model, and a ratio test decides whether to accept it. This paper shows that the usual ratio test is inverted in the expert’s own confidence—the less sure the expert is of its proposal, the more likely that proposal is accepted—so on out-of-distribution or ambiguous steps the method injects tokens with no security content. CoGate adds a simple gate that first checks the expert’s absolute confidence (maximum token probability or normalized entropy) and only then runs the ratio test; when confidence is low the expert abstains and the base model decodes alone. Across six code models and three benchmarks the gated method beats prior co-decoding, with the largest gains on CWEval, where functionality and security must hold together and the weaknesses are unseen. A sympathetic reader cares because the failure mode is structural, the fix is training-free and cheap at inference, and the gains concentrate exactly where secure generation matters most.","feed_headline":"Unconfident security experts make code worse—gate them out","feed_subtitle":"A confidence check before co-decoding cuts noisy interventions and lifts joint functional-security scores on unseen CWEs.","key_machinery":"The confidence gate gt = 1[c(St) > τ], with c either max-probability or normalized entropy of the expert’s next-token distribution. It sits in front of the usual base/expert ratio test so the expert may steer only when certified confident; otherwise decoding defers to the base model alone.","core_discovery":"The acceptance statistic min(1, B(x̃)/S(x̃)) used by prior co-decoding is nonincreasing in the expert’s probability on its own proposal, so an unconfident expert is accepted more readily and, when its distribution flattens, the emitted token carries no security-relevant information. Conditioning expert intervention on an absolute confidence signal (max probability or normalized entropy) separates relative preference from absolute reliability, suppresses those harmful steps, and improves joint Func-Sec@k—up to a 12.6-point gain of Func-Sec@10 on CWEval—especially on out-of-distribution CWEs.","pith_inferences":["The same absolute-confidence gate could be dropped in front of other relative steering methods (expert/anti-expert reweighting, speculative accept/reject) wherever the auxiliary model can be unconfident.","If the expert’s confidence is systematically miscalibrated, a learned or temperature-aware threshold may be needed; the paper’s fixed τ assumes calibration is good enough after distillation and security fine-tuning.","The inverted-U utility curve versus τ suggests an operating regime that could be monitored online to detect when the expert has gone OOD mid-generation."],"forward_implications":["Ungated co-decoding should be treated as unsafe by default on long-tail or unseen vulnerability patterns.","Any decoding-time steering method whose control signal is only a relative ratio or difference can inherit the same confidence-inflation failure.","Joint functional-security metrics (not separate security ratios) become the right evaluation target once the gate removes noisy expert steps.","Practitioners can keep a frozen base model and a small security expert, adding only a cheap confidence check at each step.","Normalized entropy is the preferred gate when the evaluation is OOD; max-probability is enough for in-distribution security suites."],"fun_headline_variants":["Gate unconfident security experts out of co-decoding","CoGate: confidence gates stop noisy secure-code steering","Unconfident experts mislead co-decoding—confidence gates fix it","Confidence-gated co-decoding lifts Func-Sec@10 on CWEval","When security experts flatten, gate them before they emit tokens"],"cache_read_input_tokens":128,"weakest_assumption_plain":"A single family-specific confidence threshold, tuned once on a held-out development split to maximize joint functional-security score, cleanly separates helpful from harmful expert steps at test time across benchmarks and temperatures.","fun_headline_variants_meta":{"raw":{"variants":["Gate unconfident security experts out of co-decoding","CoGate: confidence gates stop noisy secure-code steering","Unconfident experts mislead co-decoding—confidence gates fix it","Confidence-gated co-decoding lifts Func-Sec@10 on CWEval","When security experts flatten, gate them before they emit tokens"]},"model":"grok-4.5","effort":"low","cost_usd":0.003385,"raw_usage":{"total_tokens":1178,"prompt_tokens":812,"num_sources_used":0,"completion_tokens":69,"cost_in_usd_ticks":33848000,"prompt_tokens_details":{"text_tokens":812,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":297,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":812,"tokens_out":69,"duration_ms":5013,"temperature":1.0,"reasoning_tokens":297,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-31T04:30:41.175087+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Retune or freeze the gate threshold on one benchmark family, then measure whether token-level intervention utility stays positive and Func-Sec@10 still beats ungated co-decoding on held-out OOD CWEs and at temperatures the threshold was not chosen for; a flat or negative utility curve would falsify the claim.","supporting_citations":[],"review_version":1}