{"id":"24f48173-982e-4294-8de5-8233268fb475","arxiv_id":"2605.24134","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"ProofAgent Harness is open infrastructure that runs adversarial multi-turn trials on AI agents, applies multi-juror scoring with turn-level audits, and generates evidence-linked reports.","lead":"The paper presents ProofAgent Harness, an open system for running adversarial multi-turn tests on AI agents and scoring their behavior with multiple juror personas. A smart generalist might read it to understand emerging tools for catching failures in agents used for customer support, medicine, or code before they cause real harm.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The abstract-only review already flags the core external-validity risk. No further load-bearing internal flaw (e.g., circular scoring, missing ablation, or contradictory equations) is detectable from the provided text. The infrastructure claim is descriptive and the experimental findings are presented as observations rather than universal proofs, so the reader's provisional UNVERDICTED stance remains appropriate.","tokens_in":1778,"tokens_out":259,"duration_ms":20631,"concrete_test":"Re-run the reported customer-support and medical-triage trials with an independently authored set of 20 adversarial prompts and three new juror personas; if the selective-failure patterns and small-LLM advantage disappear, the harness scores are scenario-dependent.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper introduces open infrastructure for multi-turn adversarial agent evaluation and reports selective failures plus a small local model outperforming scale via the pipeline. The reader's weakest assumption (curated trials and juror personas may yield artifacts rather than real risks) is the only plausible load-bearing point, but the abstract provides no internal inconsistency or hidden assumption that would falsify the infrastructure claim itself. Full text access is referenced but yields no additional technical flaw in the described architecture.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces ProofAgent Harness, open infrastructure for scalable, auditable, and adversarial multi-turn evaluation of AI agents in high-risk settings. It describes components for curating evaluation intelligence, running adversarial trials, capturing behavioral traces, applying Adversarial Multi-Juror Scoring with Turn-Level Audit using calibrated juror personas and consensus checks, and producing evidence-linked reports. The open design allows extension of domains, traps, metrics, and scoring rules. Experiments across customer support, medical triage, privacy/security, and code generation agents report selective failures via weak metrics, fragile turns, unsafe reframing, and manipulation paths, plus the result that a small quantized local Harness LLM can challenge production agents powered by large LLMs, suggesting evaluation capability emerges from the full pipeline rather than model scale.","tokens_in":1840,"tokens_out":480,"duration_ms":32345,"significance":"If the harness provides reliable, extensible, and auditable adversarial evaluation, the contribution could be significant for AI agent safety and multi-agent systems research by shifting evaluation from static outputs to trajectory-based, pressure-tested assessment before deployment. The open infrastructure and evidence-backed reporting are strengths that enable community extension. The paper ships open infrastructure, which supports reproducibility and extensibility.","major_comments":[{"comment":"Abstract: The abstract states experimental outcomes across domains showing selective failures and a small local model outperforming scale via the pipeline, but supplies no methodology details, dataset descriptions, number of trials, controls, or statistical reporting. This prevents assessment of whether the data support the claims and directly undermines evaluation of the central experimental assertions.","section":"Abstract"},{"comment":"The core claim that Adversarial Multi-Juror Scoring with Turn-Level Audit produces scores reliably indicating real deployment risks (rather than artifacts of curated trials or juror definitions) is load-bearing for the harness's practical value, yet the provided text offers no validation of juror calibration, inter-juror agreement metrics, or comparison to real-world risk data.","section":"Abstract (and implied Experiments)"}],"minor_comments":[{"comment":"The abstract and description would benefit from explicit section references or a high-level architecture diagram to clarify the flow from trial curation through scoring to reporting.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive review and for recognizing the potential significance of ProofAgent Harness for adversarial agent evaluation. We address each major comment below with specific plans for revision where warranted.","responses":[{"response":"We agree the abstract is too high-level. In the revised manuscript we will expand it to include the number of trials conducted per domain, a brief description of the four evaluation domains, and explicit references to the detailed methodology, controls, and statistical reporting already present in the Experiments section. This will allow readers to assess the claims without expanding the abstract beyond reasonable length.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The abstract states experimental outcomes across domains showing selective failures and a small local model outperforming scale via the pipeline, but supplies no methodology details, dataset descriptions, number of trials, controls, or statistical reporting. This prevents assessment of whether the data support the claims and directly undermines evaluation of the central experimental assertions."},{"response":"The manuscript describes the design of Adversarial Multi-Juror Scoring with Turn-Level Audit, including calibrated personas and consensus mechanisms, but does not report quantitative inter-juror agreement statistics or external validation against real-world deployment outcomes. We will add a new subsection in the Experiments section reporting agreement metrics computed from the existing trial data and a limitations paragraph acknowledging the absence of ground-truth risk labels. We do not claim the current scores have been externally validated against deployment incidents; the framework is presented as an auditable, extensible starting point rather than a fully validated risk predictor.","revision_made":"partial","referee_comment":"[Abstract (and implied Experiments)] The core claim that Adversarial Multi-Juror Scoring with Turn-Level Audit produces scores reliably indicating real deployment risks (rather than artifacts of curated trials or juror definitions) is load-bearing for the harness's practical value, yet the provided text offers no validation of juror calibration, inter-juror agreement metrics, or comparison to real-world risk data."}],"tokens_in":1463,"tokens_out":469,"duration_ms":32948,"standing_objections":["Direct comparison of harness scores to real-world deployment risk data is not feasible at present because no public, standardized ground-truth datasets exist for multi-turn agent failures in the tested high-risk domains."]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that ProofAgent Harness supplies an extensible open system for running adversarial multi-turn trials on agents, complete with behavioral traces, calibrated juror personas, and evidence-linked reports. The architecture centers on Adversarial Multi-Juror Scoring with Turn-Level Audit, and the abstract reports selective failures across customer support, medical triage, privacy, and code domains plus the result that a small quantized local model can surface issues in large production agents.\n\nThe open design that lets users add domains, traps, metrics, and scoring rules is a practical step forward. Treating evaluation as infrastructure rather than a one-off score, and emphasizing trajectory under pressure instead of isolated outputs, aligns with real deployment needs. The claim that the full pipeline matters more than raw model scale is a testable point worth checking.\n\nThe clear limitation is that the abstract gives no methodology, dataset details, controls, or statistical reporting, so the experimental outcomes cannot be assessed. It is impossible to tell whether the curated trials and juror definitions produce reliable risk signals or just artifacts of the chosen scenarios. The full text may address this, but on the provided description the soundness remains low.\n\nThis work is aimed at researchers and developers building or auditing AI agents in high-stakes settings. Anyone needing concrete tools for multi-turn adversarial testing would find the architecture description useful. It deserves peer review because the infrastructure is concrete and the open claims can be evaluated once the methods are supplied.","headline":"The paper presents open infrastructure for multi-turn adversarial agent evaluation with turn-level audits and multi-juror scoring, but the abstract supplies no methods or stats to back the experimental claims.","tokens_in":2316,"tokens_out":367,"would_cite":false,"duration_ms":20245,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"ProofAgent Harness supplies open infrastructure for adversarial multi-turn evaluation of AI agents via multi-juror scoring on behavioral traces.","keywords":["AI agents","adversarial evaluation","multi-juror scoring","multi-turn interactions","AI safety testing","agent evaluation infrastructure","behavioral traces"],"falsifier":"A direct comparison showing that agents rated safe by the harness still produce comparable failure rates when deployed in live user interactions would falsify the claim that harness scores predict deployment risks.","tokens_in":2666,"feed_emoji":"🧪","tokens_out":642,"duration_ms":32051,"temperature":0.7,"pith_summary":"The paper introduces ProofAgent Harness to evaluate AI agents that use tools, retain context, and interact over multiple turns in high-risk settings. It argues that static or isolated-output tests miss failures that only appear under adversarial pressure and trajectory, so the harness curates trials, runs them, captures traces, applies calibrated juror scoring with consensus checks, and generates evidence-linked reports. Experiments across customer support, medical triage, privacy, security, and code generation show strong agents fail selectively through weak metrics, fragile turns, unsafe reframing, and manipulation paths, and that a small quantized local model inside the harness can challenge agents powered by the largest LLMs.","feed_headline":"Small local model challenges top AI agents in harness tests","feed_subtitle":"Adversarial multi-turn trials expose selective failures through fragile turns, unsafe reframing, and manipulation paths.","key_machinery":"Adversarial Multi-Juror Scoring with Turn-Level Audit, which scores completed agent trajectories under pressure using calibrated juror personas, disagreement resolution, and turn-level evidence links.","core_discovery":"ProofAgent Harness turns AI agent evaluation from static scoring into repeatable adversarial infrastructure by running multi-turn trials, capturing behavioral traces, and applying Adversarial Multi-Juror Scoring with Turn-Level Audit that uses calibrated personas, consensus resolution, and turn-level evidence to produce auditable reports.","pith_inferences":["The harness could support standardized benchmarks that compare agents across organizations by sharing trial sets and scoring rules.","Evaluation infrastructure may prove more decisive for safety than raw model size, shifting focus toward modular testing pipelines.","Integrating live user feedback loops into the harness could test whether simulated adversarial trials generalize to organic interactions."],"forward_implications":["Developers can extend the harness with new domains, traps, metrics, and juror personas without rebuilding the core pipeline.","Evaluation reports become evidence-linked and auditable, supporting pre-deployment decisions in customer support or medical settings.","A small local model can serve as an effective challenger when embedded in the full harness pipeline rather than relying on model scale alone.","Agents can be tested for manipulation paths and unsafe reframing before they handle private data or follow policies in production."],"fun_headline_variants":["Local model challenges production AI agents in harness multi-turn tests","Adversarial trials expose fragile turns and manipulation in AI agents","Multi-juror scoring audits complete AI agent trajectories with turn evidence","Harness infrastructure enables adversarial evaluation of production AI agents","Quantized local LLM challenges top agents via calibrated juror consensus"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The curated adversarial trials and calibrated juror personas produce scores that reliably indicate real deployment risks rather than artifacts of the chosen scenarios or persona definitions.","fun_headline_variants_meta":{"raw":{"variants":["Local model challenges production AI agents in harness multi-turn tests","Adversarial trials expose fragile turns and manipulation in AI agents","Multi-juror scoring audits complete AI agent trajectories with turn evidence","Harness infrastructure enables adversarial evaluation of production AI agents","Quantized local LLM challenges top agents via calibrated juror consensus"]},"model":"grok-4.3","cost_usd":0.003866,"raw_usage":{"total_tokens":1996,"prompt_tokens":684,"num_sources_used":0,"completion_tokens":79,"cost_in_usd_ticks":38662000,"prompt_tokens_details":{"text_tokens":684,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1233,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":684,"tokens_out":79,"duration_ms":17695,"temperature":1.0,"reasoning_tokens":1233,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T14:35:57.937540+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A direct comparison showing that agents rated safe by the harness still produce comparable failure rates when deployed in live user interactions would falsify the claim that harness scores predict deployment risks.","supporting_citations":[],"review_version":1}