{"id":"96d0e36e-49e7-4453-a127-e2b6b7929a65","arxiv_id":"2604.26569","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"LLM-Flax automates neuro-symbolic robotic task planning with three LLM stages for rule generation, failure recovery, and zero-shot scoring, outperforming manual baselines on MazeNamo grids.","lead":"LLM-Flax uses a locally hosted large language model to automatically generate planning rules and object scores from only a PDDL domain file, removing manual rule authoring and GNN training. A smart generalist might read it to see how AI language models can reduce setup effort for robotic task planners in new domains.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"LLM-generated rules and zero-shot scoring may not preserve soundness without explicit verification","rationale":"The reader's weakest assumption correctly isolates the single point where the entire neuro-symbolic replacement could silently fail. All reported improvements rest on unverified LLM outputs being functionally equivalent to the manual rules and trained GNN they replace. The concrete test above directly probes that equivalence on the most dramatic benchmark (12x12 Expert) and would falsify the claim if discrepancies appear. No other internal inconsistency or missing control is evident from the abstract and claimed results.","tokens_in":1854,"tokens_out":403,"duration_ms":37633,"concrete_test":"Take the exact PDDL domain for the 12x12 Expert benchmark, re-issue the paper's Stage 1 prompt to the same locally hosted LLM, then run a PDDL equivalence checker (or exhaustive state enumeration on the 12x12 grid) to verify that every transition allowed by the generated relaxation rules is also allowed in the original domain and vice versa; if any mismatch exists, recompute SR on the same test problems using only the verified rules.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline SR gains (0.945 avg vs 0.828 manual; 0.733 vs 0.000 on 12x12 Expert) require that Stage 1 prompting produces relaxation/complementary rules whose semantics match the original PDDL domain (no added or removed transitions) and that Stage 3 zero-shot importance scores rank objects without omitting critical ones or over-ranking irrelevant ones. The framework uses format validation and self-correction, yet the paper provides no independent check (e.g., equivalence testing against the ground-truth domain or exhaustive enumeration of small instances) that these LLM outputs are sound. If the generated rules are incomplete or the scores introduce bias, the planner could succeed on the reported test set while failing to generalize or while relying on implicit LLM knowledge beyond the supplied PDDL file.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces LLM-Flax, a three-stage neuro-symbolic framework that uses a locally hosted LLM, given only a PDDL domain file, to automate generation of relaxation and complementary rules (Stage 1 with format validation and self-correction), LLM-guided failure recovery via a feasibility-gated budget policy (Stage 2), and zero-shot object importance scoring to replace a trained GNN (Stage 3). Evaluated on eight MazeNamo benchmarks (10x10, 12x12, 15x15 grids), it reports an average success rate of 0.945 versus 0.828 for a manual baseline (+0.117), with specific gains such as 0.733 vs. 0.000 on 12x12 Expert and 1.000 vs. 0.900 on 15x15 Hard.","tokens_in":2039,"tokens_out":782,"duration_ms":47504,"significance":"If the soundness of the LLM-generated components and the reported gains hold under verification, the work would meaningfully reduce manual effort in deploying neuro-symbolic planners for robotic tasks, enabling faster adaptation to new domains without expert rule authoring or supervised GNN training. The zero-shot scoring and latency-aware recovery are notable technical contributions, though the acknowledged context-window limits highlight a key scalability issue for larger problems.","major_comments":[{"comment":"Abstract and evaluation results: The headline success-rate claims (average 0.945 vs. 0.828, plus per-benchmark numbers such as 0.733 vs. 0.000 on 12x12 Expert) are load-bearing for the central thesis, yet no details are provided on the number of evaluation runs per benchmark, statistical significance tests, error bars, or variance; without these, it is impossible to determine whether the gains are robust or could be explained by selection effects or run-to-run variability in LLM outputs.","section":"Abstract and evaluation results"},{"comment":"Stage 1 (rule generation): The framework relies on the LLM producing relaxation and complementary rules whose semantics exactly match the input PDDL domain (no added or removed transitions), but the manuscript describes only format validation and self-correction without any independent soundness check such as equivalence testing, model checking on small instances, or exhaustive enumeration; if the generated rules are incomplete or inconsistent, the downstream planner could succeed on the reported test set while violating the original domain semantics.","section":"Stage 1"},{"comment":"Stage 3 (zero-shot scoring): Replacing the trained GNN with zero-shot LLM object importance scoring is a core innovation, yet the paper provides no analysis or ablation of ranking errors (e.g., critical objects omitted or irrelevant ones over-ranked) and only notes a context-window bottleneck without quantifying its impact via scaling experiments on larger grids or domains; this directly affects the claim of full elimination of training data.","section":"Stage 3"}],"minor_comments":[{"comment":"The description of the feasibility-gated budget policy in Stage 2 would benefit from a concrete pseudocode listing or parameter values (e.g., exact budget thresholds) to enable reproduction.","section":"Stage 2"},{"comment":"A table summarizing per-benchmark success rates, including run counts and any ablation results for the three stages, would improve clarity over the narrative presentation in the abstract.","section":"Evaluation"}],"recommendation":"major_revision","confidential_remarks":"The evaluation relies on the MazeNamo benchmark suite; if this is not a widely adopted standard in the robotics planning community, the generalizability claims would be strengthened by additional domains. The citation pattern appears appropriate, with no obvious omissions of key neuro-symbolic planning references."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the careful review and for highlighting areas where additional rigor would strengthen the presentation of LLM-Flax. We address each major comment below and commit to revisions that improve clarity and verifiability without altering the core claims.","responses":[{"response":"We agree that the absence of run counts, variance measures, and statistical tests limits the ability to assess robustness. The results in the current manuscript reflect single executions per benchmark. In the revision we will re-run every benchmark across 10 independent trials (varying LLM sampling seeds and environment initializations), report means with standard deviations, add error bars to the results table, and include paired statistical tests (e.g., t-tests) against the manual baseline. These additions will appear in both the abstract and the evaluation section.","revision_made":"yes","referee_comment":"[Abstract and evaluation results] The headline success-rate claims (average 0.945 vs. 0.828, plus per-benchmark numbers such as 0.733 vs. 0.000 on 12x12 Expert) are load-bearing for the central thesis, yet no details are provided on the number of evaluation runs per benchmark, statistical significance tests, error bars, or variance; without these, it is impossible to determine whether the gains are robust or could be explained by selection effects or run-to-run variability in LLM outputs."},{"response":"The observation is correct: our validation is currently limited to syntactic format checks and iterative self-correction. We will add an independent soundness verification step in the revised manuscript. Specifically, we will apply a PDDL model checker to small, exhaustively enumerable instances derived from each domain to confirm that the generated relaxation and complementary rules preserve the original transition semantics. Results of these checks will be reported; any detected discrepancies will be discussed and the prompting procedure adjusted if needed.","revision_made":"yes","referee_comment":"[Stage 1] The framework relies on the LLM producing relaxation and complementary rules whose semantics exactly match the input PDDL domain (no added or removed transitions), but the manuscript describes only format validation and self-correction without any independent soundness check such as equivalence testing, model checking on small instances, or exhaustive enumeration; if the generated rules are incomplete or inconsistent, the downstream planner could succeed on the reported test set while violating the original domain semantics."},{"response":"We accept that a quantitative characterization of ranking quality and context-window effects is missing. In the revision we will insert an ablation that compares LLM-generated importance rankings against oracle rankings obtained from solved plans, reporting precision/recall for critical objects and the frequency of over- or under-ranking. We will also add scaling experiments on 20x20 grids (and larger where feasible) that measure success-rate degradation and latency as context limits are approached. These results will qualify the scope of the 'no training data' claim for the evaluated problem sizes.","revision_made":"yes","referee_comment":"[Stage 3] Replacing the trained GNN with zero-shot LLM object importance scoring is a core innovation, yet the paper provides no analysis or ablation of ranking errors (e.g., critical objects omitted or irrelevant ones over-ranked) and only notes a context-window bottleneck without quantifying its impact via scaling experiments on larger grids or domains; this directly affects the claim of full elimination of training data."}],"tokens_in":1692,"tokens_out":715,"duration_ms":55460,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing to know is that LLM-Flax uses a local LLM to automatically create relaxation rules, handle planning failures, and score objects for neuro-symbolic robotic task planning, leading to better success rates than manual methods on the MazeNamo benchmarks. The approach cuts down on expert effort and training needs. What is new is the combination of structured prompting with validation for rule generation, a feasibility-gated policy for LLM-based recovery, and zero-shot LLM scoring that replaces the GNN entirely. The paper evaluates this on eight benchmarks covering different grid sizes and difficulties. It achieves an average success rate of 0.945 versus 0.828 for the manual baseline, and it succeeds in cases like the 12x12 Expert where the baseline gets zero success. The soft spots are around verification of the LLM outputs. While there is format validation and self-correction, the paper does not provide independent tests to ensure the rules match the original PDDL semantics exactly or that the importance scores do not introduce bias. This leaves open the possibility that performance relies on the LLM's pre-trained knowledge rather than the domain file alone. The context window issue for scaling is also a practical limit. This paper is for robotics and AI researchers interested in making neuro-symbolic planners easier to deploy in new domains. It will be valuable to those exploring LLM uses in planning systems. The work engages honestly with the challenges of these methods and presents concrete results. It deserves a serious referee to dig into the experimental details and the prompting strategies. I would recommend sending this to peer review.","headline":"LLM-Flax automates rule generation and zero-shot scoring for neuro-symbolic planners with reported success rate gains, but lacks independent checks that the LLM outputs preserve domain semantics.","tokens_in":2510,"tokens_out":390,"would_cite":false,"duration_ms":49476,"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":"A locally hosted LLM given only a PDDL domain file can generate relaxation rules, manage failure recovery, and perform zero-shot object scoring to automate neuro-symbolic robotic planning and raise average success rate from 0.828 to 0.945.","keywords":["neuro-symbolic planning","large language models","PDDL domains","robotic task planning","relaxation rules","zero-shot scoring","failure recovery","maze navigation"],"falsifier":"Running LLM-Flax on a fresh PDDL domain and finding that the generated rules produce invalid plans or that the zero-shot scores yield success rates well below a carefully tuned manual baseline would falsify the claim of reliable full automation.","tokens_in":2753,"feed_emoji":"🤖","tokens_out":837,"duration_ms":55437,"temperature":0.7,"pith_summary":"The paper presents LLM-Flax as a three-stage system that removes the need for domain experts to write rules or collect training data for graph neural networks in neuro-symbolic task planning. Stage 1 uses structured prompting on the PDDL file to create relaxation and complementary rules with built-in validation and self-correction. Stage 2 adds a feasibility-gated budget policy for LLM-guided recovery that accounts for latency before each call. Stage 3 substitutes a trained GNN with direct LLM-based object importance scoring. Tested on eight MazeNamo grid benchmarks, the full system matches or exceeds manual performance on every case and succeeds on instances where the hand-crafted baseline scores zero. This matters because it makes it feasible to apply advanced planners to new robotic domains without repeated expert intervention or data collection.","feed_headline":"LLM generates robot planning rules from PDDL file alone and beats manual baseline","feed_subtitle":"Three-stage system reaches 0.945 average success rate on eight MazeNamo grids, succeeding where hand-crafted rules score zero.","key_machinery":"The three-stage LLM-Flax framework that automates rule generation, feasibility-gated failure recovery, and zero-shot LLM object importance scoring from a single PDDL domain file.","core_discovery":"LLM-Flax is a three-stage framework that, given only a PDDL domain file, lets a locally hosted LLM (1) generate relaxation and complementary rules through structured prompting with format validation and self-correction, (2) handle failure recovery via a feasibility-gated budget policy that reserves latency cost before each call, and (3) replace a trained GNN with zero-shot object importance scoring. On the MazeNamo benchmark across 10x10, 12x12, and 15x15 grids, the complete system reaches an average success rate of 0.945 compared with the manual baseline's 0.828, matching or beating the manual planner on all eight cases; it records 0.733 success on 12x12 Expert where the manual planner gets","pith_inferences":["The same prompting pipeline could be applied to other PDDL domains such as robotic manipulation or logistics without maze-specific tuning.","Larger context windows or better long-context LLMs would directly address the noted bottleneck that limits Stage 3 on bigger instances.","Combining the automated rules with existing symbolic planners could create hybrid systems that inherit both generality and formal guarantees.","Local hosting preserves privacy but requires that the chosen LLM be capable enough to avoid systematic rule errors that would otherwise need human debugging."],"forward_implications":["New robotic domains can be planned without requiring experts to author relaxation or complementary rules.","No training problems or supervised GNN data are needed to obtain competitive object scoring.","Planning remains sound on hard instances such as 12x12 Expert grids where manual rules fail completely.","Latency cost can be explicitly budgeted before each LLM call to avoid starving the relaxation fallback."],"fun_headline_variants":["LLM-Flax generates rules from PDDL alone and beats manual baseline at 0.945 SR","From single PDDL file LLM automates robot task planning rules","LLM-Flax achieves 0.945 average SR on MazeNamo outperforming manual 0.828","Neuro-symbolic system uses zero-shot LLM scoring for object importance","Feasibility-gated LLM recovers from failures in robotic planning"],"cache_read_input_tokens":64,"weakest_assumption_plain":"A locally hosted LLM can consistently produce correct and complete relaxation rules plus accurate zero-shot object importance scores without introducing errors that break the soundness or completeness of the downstream planner.","fun_headline_variants_meta":{"raw":{"variants":["LLM-Flax generates rules from PDDL alone and beats manual baseline at 0.945 SR","From single PDDL file LLM automates robot task planning rules","LLM-Flax achieves 0.945 average SR on MazeNamo outperforming manual 0.828","Neuro-symbolic system uses zero-shot LLM scoring for object importance","Feasibility-gated LLM recovers from failures in robotic planning"]},"model":"grok-4.3","cost_usd":0.006461,"raw_usage":{"total_tokens":3112,"prompt_tokens":840,"num_sources_used":0,"completion_tokens":100,"cost_in_usd_ticks":64612000,"prompt_tokens_details":{"text_tokens":840,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2172,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":840,"tokens_out":100,"duration_ms":28821,"temperature":1.0,"reasoning_tokens":2172,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-07T11:36:26.190108+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running LLM-Flax on a fresh PDDL domain and finding that the generated rules produce invalid plans or that the zero-shot scores yield success rates well below a carefully tuned manual baseline would falsify the claim of reliable full automation.","supporting_citations":[],"review_version":1}