{"id":"b57fd51a-08d5-47f9-9dc0-78926b836350","arxiv_id":"2605.27922","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Harness-Bench is a new diagnostic benchmark that demonstrates substantial performance variation across model-harness pairings in 5,194 trajectories and recommends reporting agent capability at the configuration level.","lead":"This paper introduces Harness-Bench, a benchmark with 106 tasks to measure how different harness configurations affect LLM agent performance across models in realistic workflows. A smart generalist should read it because it shows that agent success depends on the full execution system, not just the base model.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Representativeness of 106 tasks and chosen harness configs rests on manual review without external validation","rationale":"The reader's weakest_assumption matches the load-bearing point exactly. Full text availability does not remove the need for external validation of task/harness coverage; the abstract's description of construction method already flags the gap. This moves the verdict from UNVERDICTED to CONDITIONAL rather than full acceptance.","tokens_in":1764,"tokens_out":363,"duration_ms":36125,"concrete_test":"Sample 200 real-world agent execution traces from public repositories or production logs; compute and compare histograms of key observables (tool-call count per task, fraction of state-modifying actions, error-recovery depth, artifact complexity) against the 106 Harness-Bench tasks; if Kolmogorov-Smirnov distance exceeds 0.25 on any metric, re-run a 20-task subset of Harness-Bench on the new distribution and check whether model-harness interaction rankings change by >15%.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim—that results justify reporting capability at the model-harness configuration level—requires that observed variation across the 5,194 trajectories reflects execution-layer effects that generalize beyond the benchmark. The tasks were built from practical patterns and manually reviewed for realism/solvability/oracle-checkability, and harness configs are described as representative, yet the paper supplies no quantitative comparison (e.g., distributional statistics on tool-call sequences, state-transition complexity, or recovery patterns) to real deployed agent logs or usage corpora. If the selected tasks systematically under-sample long-horizon state drift, permission-edge cases, or live-environment nondeterminism, the measured harness effects could be artifacts of the construction process rather than intrinsic to realistic workflows.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces Harness-Bench, a diagnostic benchmark consisting of 106 sandboxed offline tasks constructed from practical agent-use patterns. It evaluates representative harness configurations across multiple model backends under shared environments and protocols, recording 5,194 trajectories that show substantial variation in completion, process quality, efficiency, and failure behavior. The central claim is that these results justify reporting agent capability at the model-harness configuration level rather than the base model alone, while also identifying recurring execution-alignment failures.","tokens_in":1891,"tokens_out":475,"duration_ms":28893,"significance":"If the tasks prove representative, the work would be significant for LLM agent evaluation by providing a reproducible foundation for isolating execution-layer effects and diagnosing alignment failures between reasoning and tool/workspace state. Explicit strengths include the sandboxed design, preservation of native harness behavior, detailed trace and validator recording, and focus on oracle-checkable tasks; these enable analyses beyond final success rates.","major_comments":[{"comment":"Task construction and validation (described in the methods section on the 106 tasks): the assertion of representativeness rests solely on construction from practical patterns plus manual review for realism/solvability/oracle-checkability, with no quantitative distributional statistics (tool-call sequences, state-transition complexity, recovery patterns, or nondeterminism) compared against real deployed agent logs or usage corpora. This is load-bearing for the generalization that observed harness effects reflect intrinsic execution-layer variation rather than benchmark artifacts.","section":"Task construction and validation"},{"comment":"Results and analysis section (reporting variation across 5,194 trajectories): the manuscript states 'substantial variation' and identifies recurring execution-alignment failures but supplies no details on the statistical tests, effect-size measures, or controls for multiple comparisons used to establish that differences across the five harness configurations are significant and not driven by task-specific artifacts.","section":"Results and analysis"}],"minor_comments":[{"comment":"The abstract and introduction could more explicitly quantify key metrics (e.g., range of completion-rate deltas across harnesses) rather than relying on the qualitative phrase 'substantial variation'.","section":"Abstract"},{"comment":"Notation for harness configurations and model backends should be introduced with a clear table or diagram early in the paper to aid readability of the experimental matrix.","section":"Introduction/Methods"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. We address each major comment below with honest responses and indicate where revisions will be made.","responses":[{"response":"We agree that quantitative distributional statistics compared to real deployed agent logs would strengthen the claim of representativeness. Such data is not available to us, as it would require access to private usage logs from deployed systems. Our tasks were constructed from practical patterns and underwent manual review. We will revise the manuscript to explicitly discuss this limitation in the methods or discussion section and outline plans for future validation against available corpora where possible. The benchmark remains valuable for its controlled, oracle-checkable design that isolates harness effects.","revision_made":"partial","referee_comment":"[Task construction and validation] Task construction and validation (described in the methods section on the 106 tasks): the assertion of representativeness rests solely on construction from practical patterns plus manual review for realism/solvability/oracle-checkability, with no quantitative distributional statistics (tool-call sequences, state-transition complexity, recovery patterns, or nondeterminism) compared against real deployed agent logs or usage corpora. This is load-bearing for the generalization that observed harness effects reflect intrinsic execution-layer variation rather than benchmark artifacts."},{"response":"We concur that including statistical details would enhance the analysis. In the revised version, we will incorporate a statistical analysis subsection detailing the tests used (such as appropriate non-parametric tests for completion rates and efficiency metrics), effect sizes, and adjustments for multiple comparisons. We will also address potential task-specific artifacts by reporting variance across tasks and overall patterns. This addition will be made without altering the core findings.","revision_made":"yes","referee_comment":"[Results and analysis] Results and analysis section (reporting variation across 5,194 trajectories): the manuscript states 'substantial variation' and identifies recurring execution-alignment failures but supplies no details on the statistical tests, effect-size measures, or controls for multiple comparisons used to establish that differences across the five harness configurations are significant and not driven by task-specific artifacts."}],"tokens_in":1457,"tokens_out":443,"duration_ms":36404,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core finding is that the same models produce noticeably different completion rates, process quality, and failure modes when paired with different harnesses, based on 5194 trajectories. That observation is the paper's main contribution and it lands cleanly.\n\nWhat works is the experimental design: fixed tasks, budgets, and protocols across harness configurations, with each harness keeping its native behavior. Recording traces, artifacts, and validator outputs beyond binary success is also a step up from many agent benchmarks. The note on execution-alignment failures (reasoning decoupling from tool feedback or state) is a useful diagnostic angle.\n\nThe soft spot is representativeness. The tasks were built from practical patterns and manually reviewed, but the paper gives no distributional comparison to real agent usage logs on metrics like tool-call sequences, state drift, or recovery patterns. If the selected tasks under-sample long-horizon nondeterminism or permission edges, the measured harness effects could be narrower than claimed. The central recommendation to report at the model-harness level follows from the observed variation, yet it would be more convincing with some external check on task coverage.\n\nThis is aimed at researchers who evaluate or deploy LLM agents and want to separate model capability from execution stack. It is coherent on its own terms and shows clear thinking about the evaluation gap. A serious editor should send it to peer review; the empirical scale and the question are worth referee time even if the task validation needs tightening.","headline":"Harness-Bench shows real variation across harnesses on shared tasks but the 106-task set lacks external grounding against real logs.","tokens_in":2398,"tokens_out":360,"would_cite":false,"duration_ms":21042,"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":"Agent capability must be evaluated at the model-harness configuration level rather than by base model alone.","keywords":["Harness-Bench","LLM agents","agent workflows","harness effects","execution variation","benchmark evaluation","agent harness"],"falsifier":"A replication study using a larger or differently constructed set of tasks that finds no substantial performance variation across harness configurations for the same models.","tokens_in":2671,"feed_emoji":"📋","tokens_out":558,"duration_ms":32337,"temperature":0.7,"pith_summary":"Existing benchmarks for LLM agents either ignore the execution harness or hold it fixed, making it hard to see how the system layer influences outcomes. Harness-Bench creates 106 realistic, sandboxed tasks and runs them across multiple models and harness configurations under controlled conditions. The results show substantial differences in completion rates, process quality, and failure modes depending on the specific model-harness pairing. A sympathetic reader would care because this implies that claims about model superiority in agent tasks are incomplete without specifying the harness. The benchmark records traces and artifacts to allow deeper analysis of execution alignment.","feed_headline":"Harness setup changes agent outcomes as much as the model","feed_subtitle":"Benchmark runs show completion rates, efficiency, and failures vary widely by model-harness pair on realistic tasks.","key_machinery":"The harness, the system layer managing context, tools, state, constraints, permissions, tracing, and recovery in agent workflows.","core_discovery":"Harness-Bench measures harness effects by evaluating representative configurations across model backends on shared tasks while preserving native execution behavior. Across 5,194 trajectories, it finds substantial variation in completion, efficiency, and failure behavior, supporting the claim that agent capability depends on the model-harness pair.","pith_inferences":["Current public leaderboards for agents may overstate or understate model differences due to harness variation.","Developers might need to optimize harnesses specifically for certain models to achieve best results.","Standardizing harness interfaces could reduce the observed configuration-dependent effects."],"forward_implications":["Agent performance reports should specify the full model-harness configuration.","Comparisons between models require testing under multiple harness setups to be valid.","Execution traces from the benchmark enable diagnosis of alignment failures between reasoning and tool feedback.","Future agent systems can use the benchmark to improve reliability by addressing recurring failure patterns."],"fun_headline_variants":["Harness affects agent outcomes like the model","Agent results vary by harness across models","Harness changes alter agent completion rates","Model and harness jointly set agent performance","Harness effects measured on shared agent tasks"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The 106 tasks, drawn from practical patterns and reviewed for realism, adequately represent the range of real-world agent workflows.","fun_headline_variants_meta":{"raw":{"variants":["Harness affects agent outcomes like the model","Agent results vary by harness across models","Harness changes alter agent completion rates","Model and harness jointly set agent performance","Harness effects measured on shared agent tasks"]},"model":"grok-4.3","cost_usd":0.005471,"raw_usage":{"total_tokens":2647,"prompt_tokens":702,"num_sources_used":0,"completion_tokens":58,"cost_in_usd_ticks":54712000,"prompt_tokens_details":{"text_tokens":702,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1887,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":702,"tokens_out":58,"duration_ms":24184,"temperature":1.0,"reasoning_tokens":1887,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T12:49:09.357243+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A replication study using a larger or differently constructed set of tasks that finds no substantial performance variation across harness configurations for the same models.","supporting_citations":[],"review_version":1}