{"id":"15d624bb-c4df-44a1-aaec-2a44a179613a","arxiv_id":"2605.21404","paper_version":1,"verdict":"ACCEPT","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"low","formal_verification":"none","parameter_count":0,"one_line_summary":"Pilot audit of twelve LLM benchmark papers finds mean disclosure score of 0.38/1.0 for agent benchmarks versus 0.66 for classical ones, with zero papers disclosing inference costs or full harness specs, and releases an open JSON schema plus scoring CSV.","lead":"This paper audits twelve LLM agent benchmark papers on how much they disclose about their evaluation setups using a five-field schema. A smart generalist might read it to understand why results on the same benchmark often disagree and how to push for better transparency in AI testing.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Single-auditor application of the codebook leaves the reported mean scores (0.38 vs 0.66) and gap rankings vulnerable to individual boundary-case judgments.","rationale":"The reader flagged schema sufficiency as the weakest assumption, which matters for the schema's future reuse. For the immediate claim about observed means and specific gaps, however, the single-auditor execution is the more proximate risk to the numbers themselves. This concern is consistent with the paper's own acknowledgment that a multi-rater version is the natural next step, but supplies an explicit test of whether the current pilot results are stable.","tokens_in":1843,"tokens_out":353,"duration_ms":43665,"concrete_test":"Recruit two additional auditors to independently score the identical twelve papers using the released JSON schema and Markdown codebook; compute field-level agreement (percentage exact match and Fleiss' kappa) and re-calculate the two mean scores and gap ordering; if means shift by more than 0.05 or the top two gaps change, the headline numbers require qualification.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central quantitative claim requires that the five-field scores reliably reflect disclosure quality across papers. The methods describe a single auditor performing one pass after iteratively refining the codebook on the same set of papers. Fields such as harness specification hinge on judgments about what constitutes 'fully disclose a content-addressed container image,' and cost reporting on what counts as 'any form' of disclosure. Without an independent second or third scoring pass, it is possible that a different rater applying the same codebook would assign different per-paper scores, altering the means or the identification of cost and harness as the largest gaps.","agreement_with_reader":"partial"},"referee_report":{"model":"grok-4.3","summary":"The paper conducts a pilot audit of disclosure practices in twelve LLM benchmark papers, consisting of eight agent benchmarks and four classical static benchmarks. Using a five-field schema (benchmark identity, harness specification, inference settings, cost reporting, failure breakdown) with a documented codebook, it finds mean scores of 0.38 for agent papers and 0.66 for classical ones. Key gaps identified include no disclosure of inference cost in any form for agent papers and no full disclosure of content-addressed container images for the evaluation harness. The authors release the JSON schema, Markdown codebook, and CSV raw scores, framing the work as descriptive of disclosure rather than an assessment of benchmark correctness, while acknowledging the single-auditor limitation.","tokens_in":1970,"tokens_out":474,"duration_ms":41239,"significance":"This pilot provides a practical, open tool for improving evaluation transparency in LLM agent research, where irreproducibility is a noted issue. The quantitative gaps, especially in cost and harness details, offer actionable insights, and the released artifacts enable extension by the community. Credit is due for the explicit release of the scoring schema, codebook, and data, as well as for the clear distinction between measuring disclosure and claiming benchmark validity.","major_comments":[{"comment":"The quantitative claims, including the mean audit scores of 0.38 versus 0.66 and the ranking of gaps on cost and harness specification, are based on a single auditor's application of the codebook after iterative refinement on the same papers. While the paper positions this as a pilot and calls for multi-rater follow-up, the boundary judgments (e.g., what constitutes 'fully disclose' for harness or 'any form' for cost) could vary, affecting the specific results reported in the abstract and results sections.","section":null}],"minor_comments":[{"comment":"The abstract refers to 'twelve well-known LLM agent benchmark papers' but the breakdown into eight agent and four classical is only clarified later; including this split in the abstract would improve immediate clarity.","section":null},{"comment":"A brief table or figure showing the per-field average scores across papers would help readers visualize the contributions to the overall means beyond the textual description.","section":null}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the positive assessment of the work as a practical contribution to evaluation transparency and for recommending minor revision. We address the major comment point by point below.","responses":[{"response":"We agree that single-auditor application introduces the possibility of variation in boundary judgments, as the referee notes. The manuscript already frames the work explicitly as a pilot, documents the iterative codebook development, and calls for multi-rater follow-up while discussing what such an audit might change. The released codebook and CSV make every scoring decision inspectable. The largest reported gaps—complete absence of any cost disclosure in the eight agent papers and lack of full content-addressed harness containers—are zero-disclosure cases with limited boundary ambiguity. To further address the concern, we will add a clarifying sentence in the abstract and results sections stating that the reported means and gap rankings reflect the documented single-auditor process and are subject to potential inter-rater variation. This is a partial revision.","revision_made":"partial","referee_comment":"The quantitative claims, including the mean audit scores of 0.38 versus 0.66 and the ranking of gaps on cost and harness specification, are based on a single auditor's application of the codebook after iterative refinement on the same papers. While the paper positions this as a pilot and calls for multi-rater follow-up, the boundary judgments (e.g., what constitutes 'fully disclose' for harness or 'any form' for cost) could vary, affecting the specific results reported in the abstract and results sections."}],"tokens_in":1495,"tokens_out":340,"duration_ms":25957,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"This paper shows that LLM agent benchmark papers disclose far less about their runs than classical static benchmark papers do. The mean scores come out to 0.38 for the eight agent papers and 0.66 for the four classical ones, with cost and harness specification as the clearest gaps. None of the agent papers report inference cost in any form, and none fully specify a content-addressed container for the evaluation environment. The authors built a five-field audit schema covering benchmark identity, harness specification, inference settings, cost reporting, and failure breakdown. They documented the codebook with the boundary cases they encountered, scored the twelve papers in one pass, and released the schema as JSON, the codebook as Markdown, and the scores as CSV. This turns a recurring complaint about non-comparable results into something that can be measured and addressed. They are explicit that the scores reflect disclosure only, not the quality of the underlying benchmarks. The single-auditor limitation is the main soft spot. Judgments on terms like full harness disclosure or any form of cost reporting could shift with another reader, which might adjust the exact averages or which gap appears largest. The broad pattern of lower disclosure in agent work would probably stay the same. The schema is intentionally small, so it leaves room for later expansion, but that is reasonable for a pilot. Researchers who evaluate LLM agents or who review papers in this area will get the most from this. Anyone tired of trying to replicate results without enough setup details can use the released schema right away. The work is clear and honest about its scope, so it deserves a serious referee. I would send it to peer review. The findings are useful as a pilot, and the open tooling adds real value that revisions can build on.","headline":"Agent benchmark papers disclose far less than classical ones, especially on cost and harness details, and the released open schema is the part worth using.","tokens_in":2466,"tokens_out":423,"would_cite":true,"duration_ms":27826,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":null,"paper_passage":"We designed a small audit schema (five fields: benchmark identity, harness specification, inference settings, cost reporting, failure breakdown)"},{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/RealityFromDistinction.lean","rs_theorem":"reality_from_one_distinction","paper_passage":"The mean audit score across the eight agent-benchmark papers is 0.38"}],"headline":"Audit schema for LLM benchmark disclosure is orthogonal to RS forcing chain","alignment":"orthogonal","rationale":"The paper's central machinery is a 5-field disclosure audit (benchmark identity, harness specification, inference settings, cost reporting, failure breakdown) plus codebook and scoring on reproducibility of agent runs. This is empirical meta-research in ML evaluation; it neither invokes nor parallels any RS structure such as J-cost, φ-ladder, 8-tick periodicity, ratio symmetry, or parameter-free constant derivation. RS modules (e.g., Cost.FunctionalEquation, Foundation.RealityFromDistinction) have no bearing on audit schemas or disclosure quality.","tokens_in":51704,"confidence":"high","tokens_out":295,"duration_ms":10367,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"An audit of twelve LLM agent benchmark papers finds they disclose an average of only 38 percent of key evaluation details.","keywords":["LLM agent benchmarks","evaluation disclosure","reproducibility audit","benchmark papers","inference cost reporting","harness specification","audit schema","pilot study"],"falsifier":"Re-running the audit with three or more independent scorers and measuring inter-rater agreement on the same twelve papers would reveal whether the reported scores are stable or sensitive to individual interpretation.","tokens_in":2749,"feed_emoji":"📋","tokens_out":690,"duration_ms":39263,"temperature":0.7,"pith_summary":"This paper audits how much information twelve well-known LLM benchmark papers actually provide about their evaluation setups. It introduces a five-field scoring schema covering benchmark identity, harness specification, inference settings, cost reporting, and failure breakdown. The authors score eight agent-focused papers at a mean of 0.38 out of 1.0 and four classical static benchmarks at 0.66. The gaps highlight missing details on costs and exact evaluation environments, which prevent reproducing or explaining differing results across papers. By releasing the schema, codebook, and scores, the work aims to encourage more transparent reporting in future benchmarks.","feed_headline":"LLM agent benchmarks disclose only 38 percent of evaluation details","feed_subtitle":"Pilot audit reveals missing costs and harness specs prevent reproducing or comparing results across papers.","key_machinery":"The five-field disclosure audit schema consisting of benchmark identity, harness specification, inference settings, cost reporting, and failure breakdown, with explicit scoring rules defined in an accompanying codebook.","core_discovery":"By applying a custom five-field audit schema to twelve LLM benchmark papers, the authors establish that agent-oriented benchmarks provide significantly less information about their evaluation procedures than classical static benchmarks. Specifically, the mean score for the eight agent papers is 0.38 compared to 0.66 for the four static ones. The largest deficiencies appear in cost reporting, where none of the agent papers disclose any inference costs, and in harness specification, where none provide a content-addressed container image for the evaluation environment. The audit focuses solely on disclosure quality rather than result validity, and the schema is made available as an open JSON.","pith_inferences":["Poor disclosure may explain many conflicting results reported on the same benchmarks.","Implementing content-addressed harnesses could become a standard practice if this schema gains adoption.","The gap between agent and static benchmarks indicates that dynamic agent evaluations require more detailed documentation protocols.","This work could extend to auditing benchmarks in other AI domains beyond LLMs."],"forward_implications":["Reproducibility of LLM agent results remains limited until disclosure practices improve on cost and environment details.","Classical static benchmarks serve as a higher standard for documentation that agent papers could emulate.","Releasing the scoring schema allows other researchers to apply consistent audits to new papers.","Single-pass auditing by one rater provides a baseline that multi-rater studies can build upon."],"fun_headline_variants":["Agent benchmarks score 0.38 on disclosure audit","None of eight papers disclose inference costs","Harness specs absent from all agent benchmarks","Classical benchmarks score 0.66 vs 0.38 for agents"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The five-field audit schema and its boundary cases in the codebook are sufficient to capture the key dimensions of evaluation disclosure quality.","fun_headline_variants_meta":{"raw":{"variants":["Agent benchmarks score 0.38 on disclosure audit","None of eight papers disclose inference costs","Harness specs absent from all agent benchmarks","Classical benchmarks score 0.66 vs 0.38 for agents"]},"model":"grok-4.3","cost_usd":0.006804,"raw_usage":{"total_tokens":3223,"prompt_tokens":788,"num_sources_used":0,"completion_tokens":59,"cost_in_usd_ticks":68037000,"prompt_tokens_details":{"text_tokens":788,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2376,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":788,"tokens_out":59,"duration_ms":18832,"temperature":1.0,"reasoning_tokens":2376,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-21T05:16:34.948810+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Re-running the audit with three or more independent scorers and measuring inter-rater agreement on the same twelve papers would reveal whether the reported scores are stable or sensitive to individual interpretation.","supporting_citations":[],"review_version":1}