{"id":"64734643-81ae-429b-8cef-4ca5259414e7","arxiv_id":"2606.04874","paper_version":2,"verdict":"CONDITIONAL","confidence":"LOW","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Introduces APB benchmark with 4209 cases across 22 domains to diagnose planning in 12 MLLMs and shows it improves downstream execution when used for refinement.","lead":"This paper introduces the Agent Planning Benchmark (APB) with 4,209 multimodal cases to diagnose planning strengths and weaknesses in LLM agents across domains and settings like feedback, broken tools, and unsolvable tasks. A smart generalist might read it to identify where current agents fail at decomposition and refusal before relying on them for complex workflows.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Benchmark case construction may confound planning isolation with authoring/filtering artifacts","rationale":"Reader's weakest assumption directly identifies the same isolation risk; full-text details on construction would be needed to move beyond CONDITIONAL, but no stronger internal inconsistency is visible from the provided claims.","tokens_in":1728,"tokens_out":271,"duration_ms":16029,"concrete_test":"Sample 50 cases per setting (250 total); have two independent annotators (blind to model outputs) score each for (a) whether success requires planning vs. execution and (b) presence of authoring artifacts; compute agreement and fraction flagged as confounded. If >15% flagged or agreement <0.7, rerun main experiments on the cleaned subset.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that the 4209 cases across five settings validly isolate planning (decomposition, tool selection, refusal) from execution quirks and from biases introduced during case authoring/filtering. The abstract and validation on 200 external tasks do not address how cases were generated, what filtering criteria were applied, or whether inter-rater checks confirmed that failures are planning-specific rather than prompt- or domain-specific artifacts. If case design inadvertently favors certain model behaviors, the reported systematic weaknesses and refinement gains could be overstated.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces the Agent Planning Benchmark (APB), a diagnostic framework consisting of 4,209 multimodal cases across 22 domains and five settings (holistic planning, feedback-conditioned step-wise planning, robustness under extraneous/broken tools, and unsolvable tasks). It evaluates 12 MLLMs to identify systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement, then validates APB-guided refinement on 200 ToolSandbox and 200 τ²-bench tasks, reporting consistent gains in plan correctness, plan grade, and downstream execution metrics. The benchmark and code are released publicly.","tokens_in":1815,"tokens_out":489,"duration_ms":32394,"significance":"If the cases validly isolate planning without confounding from authoring/filtering artifacts or execution quirks, APB would be a useful upstream diagnostic complement to end-to-end agent benchmarks. The public release of the benchmark and code is a clear strength supporting reproducibility. The cross-benchmark validation on 400 external tasks adds practical value, though the absence of statistical controls limits the strength of the refinement claims.","major_comments":[{"comment":"§3 (Benchmark Construction and Dataset): The manuscript provides no details on case generation process, filtering criteria, inter-annotator agreement, or controls for execution leakage. This directly undermines the central claim that the 4,209 cases isolate planning ability (decomposition, tool selection, refusal) from model-specific execution quirks or authoring biases, as the validation on external tasks does not address these construction issues.","section":"§3"},{"comment":"Validation experiments (§5): Improvements from APB-guided refinement on the 200+200 external tasks are reported as 'consistent' without error bars, ablation of the refinement method, or statistical significance tests. This weakens support for the claim that APB serves as an effective diagnostic for refinement gains.","section":"§5"}],"minor_comments":[{"comment":"The abstract states the benchmark covers 'multimodal cases' but does not clarify how visual inputs factor into the planning diagnostics across the five settings.","section":"Abstract"},{"comment":"Table or figure captions for the 12 MLLM results should explicitly note the number of runs or variance if any aggregation is used.","section":"Results tables"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their thoughtful review and constructive feedback on the Agent Planning Benchmark paper. We address each major comment point by point below, indicating where revisions will be made to strengthen the manuscript.","responses":[{"response":"We agree that the current §3 lacks sufficient detail on the case generation process, filtering criteria, inter-annotator agreement, and controls for execution leakage, which is necessary to fully support the claim that the cases isolate planning capabilities. In the revised manuscript, we will expand §3 with a dedicated subsection describing the generation pipeline across the 22 domains, specific filtering rules applied to the 4,209 cases, any inter-annotator agreement statistics, and explicit controls (e.g., execution sandboxing or leakage checks) used to minimize authoring biases and execution confounds. While the external validation on ToolSandbox and τ²-bench tasks demonstrates practical utility, we acknowledge it does not substitute for transparent construction documentation.","revision_made":"yes","referee_comment":"[§3] §3 (Benchmark Construction and Dataset): The manuscript provides no details on case generation process, filtering criteria, inter-annotator agreement, or controls for execution leakage. This directly undermines the central claim that the 4,209 cases isolate planning ability (decomposition, tool selection, refusal) from model-specific execution quirks or authoring biases, as the validation on external tasks does not address these construction issues."},{"response":"We agree that reporting improvements as 'consistent' without error bars, ablations, or statistical tests limits the strength of the refinement claims in §5. In the revised manuscript, we will update the validation experiments to include error bars (e.g., standard error across runs), ablation studies isolating components of the APB-guided refinement method, and statistical significance tests (such as paired t-tests) on the reported gains in plan correctness, plan grade, and execution metrics across the 400 external tasks. These additions will provide more rigorous support for APB as a diagnostic tool.","revision_made":"yes","referee_comment":"[§5] Validation experiments (§5): Improvements from APB-guided refinement on the 200+200 external tasks are reported as 'consistent' without error bars, ablation of the refinement method, or statistical significance tests. This weakens support for the claim that APB serves as an effective diagnostic for refinement gains."}],"tokens_in":1389,"tokens_out":509,"duration_ms":27689,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's core move is releasing APB, a benchmark of 4209 multimodal cases across 22 domains and five settings that explicitly tests planning separate from execution. The settings include holistic planning, feedback-conditioned step-wise planning, extraneous tools, broken tools, and unsolvable tasks. This split is new relative to the end-to-end success metrics in most prior agent benchmarks.\n\nIt does a few things right. Testing 12 MLLMs surfaces consistent patterns around long-horizon planning, tool noise, and refusal calibration. The external validation on 200 ToolSandbox and 200 τ²-bench tasks shows that APB-guided refinement lifts plan correctness and downstream execution for three models. Releasing the benchmark and code is straightforward and practical.\n\nThe soft spot is exactly the one the stress test flags. The abstract and validation sections give no information on how the 4209 cases were authored, what filtering rules were used, or whether any inter-rater checks confirmed that failures are planning-specific rather than prompt or domain artifacts. Without that, it is hard to know whether the reported weaknesses are robust or partly tied to construction choices. The external-task results are encouraging but come without error bars or ablation of the refinement method.\n\nThis paper is for people who build or evaluate LLM agents and want an upstream planning test. A reader working on agent benchmarks or refinement techniques will find the design and the reported patterns worth discussing. It is coherent enough and the idea is grounded enough to deserve peer review, though the methods section on case creation will need expansion.","headline":"APB gives a useful planning diagnostic split but the case construction process is not described enough to confirm it isolates planning from artifacts.","tokens_in":2339,"tokens_out":382,"would_cite":false,"duration_ms":21366,"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 Planning Benchmark isolates planning skills from execution in LLM agents using 4,209 cases.","keywords":["agent planning","LLM agents","planning benchmark","diagnostic evaluation","multimodal cases","tool robustness","plan refinement","unsolvable tasks"],"falsifier":"Re-authoring the cases with different methods or changing the execution environments so that the reported weaknesses and refinement benefits disappear would falsify the claim that APB isolates planning.","tokens_in":2613,"feed_emoji":"🤖","tokens_out":667,"duration_ms":42947,"temperature":0.7,"pith_summary":"This paper introduces the Agent Planning Benchmark to measure planning capabilities in LLM agents without mixing them with execution results. The benchmark includes 4,209 multimodal cases in 22 domains and five settings that test holistic planning, step-wise planning with feedback, and robustness to issues like extra or broken tools and impossible tasks. When applied to 12 models, it identifies consistent problems with long-horizon planning, handling tool noise, knowing when to refuse, and improving plans at inference time. Refining plans using insights from the benchmark leads to better performance on other agent benchmarks, suggesting planning is a key separable skill.","feed_headline":"Benchmark isolates planning from execution in LLM agents","feed_subtitle":"4,209 cases across 22 domains reveal long-horizon and tool issues in 12 models and guide refinements that improve other tasks.","key_machinery":"The Agent Planning Benchmark consisting of 4,209 multimodal cases across 22 domains and five settings that test planning in isolation from execution.","core_discovery":"The Agent Planning Benchmark (APB) is a planning-specific diagnostic with 4,209 multimodal cases across 22 domains and five settings covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs it reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics on 200 ToolSandbox tasks and 200 τ²-bench tasks.","pith_inferences":["Developers of agent systems could run similar planning diagnostics early to target reasoning improvements before full execution testing.","Reporting planning metrics separately might shift how agent papers present results and prioritize model changes.","If the five settings capture general planning demands, the benchmark could guide training objectives focused on decomposition and refusal."],"forward_implications":["LLM agents exhibit systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement.","APB-guided refinement improves plan correctness, plan grade, and downstream execution metrics on validation tasks.","APB serves as an upstream diagnostic complement to execution benchmarks.","The benchmark validates across ToolSandbox and τ²-bench where refinements transfer to better execution."],"fun_headline_variants":["APB benchmark separates planning from execution in LLM agents","4209 multimodal cases test planning across 22 domains in 12 MLLMs","Diagnostic reveals long-horizon and tool-noise weaknesses in agents","APB-guided refinement improves plan correctness on other tasks"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The 4,209 cases and five settings isolate planning ability without being confounded by model-specific execution quirks or by the way the cases were authored and filtered.","fun_headline_variants_meta":{"raw":{"variants":["APB benchmark separates planning from execution in LLM agents","4209 multimodal cases test planning across 22 domains in 12 MLLMs","Diagnostic reveals long-horizon and tool-noise weaknesses in agents","APB-guided refinement improves plan correctness on other tasks"]},"model":"grok-4.3","cost_usd":0.004589,"raw_usage":{"total_tokens":2205,"prompt_tokens":685,"num_sources_used":0,"completion_tokens":68,"cost_in_usd_ticks":45890500,"prompt_tokens_details":{"text_tokens":685,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1452,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":685,"tokens_out":68,"duration_ms":15610,"temperature":1.0,"reasoning_tokens":1452,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T06:02:19.557731+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Re-authoring the cases with different methods or changing the execution environments so that the reported weaknesses and refinement benefits disappear would falsify the claim that APB isolates planning.","supporting_citations":[],"review_version":1}