{"id":"4d8a8e9c-2312-49af-a1b2-a785e4872e2c","arxiv_id":"2605.05138","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A coding agent using verifier-driven executable Python world models solves 15 of 25 public ARC-AGI-3 games with GPT-5.5 high reasoning effort, achieving 58.12% mean RHAE.","lead":"The paper evaluates a coding agent that builds executable Python world models, verifies them against game observations, refactors for simplicity, and plans actions through the model to solve ARC-AGI-3 games. A smart generalist might read it to see how current LLMs with code execution perform on abstract reasoning benchmarks without game-specific hand-coding.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Risk that LLM-driven model construction/verification introduces undetected systematic errors invalidating planning","rationale":"This directly instantiates the reader's weakest assumption with a concrete, checkable failure mode. The paper's emphasis on no game-specific prompts and released artifacts makes the proposed test feasible; passing it would strengthen the claim while failure would require the verdict to remain CONDITIONAL or lower.","tokens_in":1802,"tokens_out":311,"duration_ms":21838,"concrete_test":"For each of the 15 solved games, replay the released run artifacts to obtain the final world-model Python file and a set of held-out observation sequences (states and actions after the last verification step); execute the model on those sequences and measure state-prediction mismatch rate. If average mismatch >5% on any solved game, the accuracy assumption is falsified for that run.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that the same LLM-driven process (observation → model update → verification against observations → refactoring) produces sufficiently accurate executable Python models for planning on unseen games. Because model synthesis, refactoring, and (implicitly) some verification logic are LLM-mediated while only the verifier programs themselves are predefined and game-agnostic, correlated misunderstandings of game mechanics can survive if they are consistent with the observed traces used for verification. The reported 15/25 solve rate with GPT-5.5 does not by itself demonstrate that the final models were faithful rather than merely sufficient for the particular trajectories taken.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper presents a coding-agent system for ARC-AGI-3 that maintains executable Python world models, verifies them against observations, refactors toward simpler abstractions, and plans through the model before acting. The system uses a scripted controller, predefined interfaces, and verifier programs with no game-specific code, prompts, or heuristics; the same agent instance is used across all games. On the 25 public ARC-AGI-3 games, with fresh agent instances per playthrough, GPT-5.5 (high reasoning effort) fully solves 15 games (mean per-game RHAE 58.12%) while GPT-5.4 solves 8 (mean RHAE 41.29%). The manuscript audits information leakage channels, releases full run artifacts and code, and concludes that the results supply preliminary evidence that verifier-driven executable world models are a promising approach for ARC-AGI-3 agents. Private-set performance is noted as future work.","tokens_in":1922,"tokens_out":531,"duration_ms":20611,"significance":"If the reported solve rates and RHAE values hold under the stated conditions, the work supplies concrete empirical support for an approach that lets LLM-based agents synthesize and use executable models for planning without hand-engineered game logic. The explicit release of code, artifacts, and the audit of leakage channels are strengths that enable direct replication and extension. The results are preliminary (public games only) but address a core ARC-AGI challenge of generalization across novel tasks.","major_comments":[{"comment":"Abstract and §4 (results): the central claim of 'preliminary evidence' for verifier-driven models rests on the assumption that LLM-mediated model construction produces faithful executables rather than merely trajectory-consistent ones. The manuscript reports solve counts and RHAE but supplies no quantitative checks (e.g., held-out observation prediction error, model-vs-ground-truth divergence on unseen states, or systematic error analysis) that would rule out correlated misunderstandings surviving the verification step.","section":"Abstract and §4"}],"minor_comments":[{"comment":"Abstract: the acronym RHAE is used without expansion on first occurrence; a parenthetical definition or reference to its definition in the main text would improve clarity.","section":"Abstract"},{"comment":"The manuscript states that full run artifacts are released at the GitHub link; confirming that the released logs include per-step model versions and verification outcomes would further support reproducibility claims.","section":null}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for highlighting the distinction between trajectory-consistent and faithful world models. We address the comment below and note that the current results are framed as preliminary evidence based on end-to-end task performance.","responses":[{"response":"We agree that the manuscript does not provide quantitative checks such as held-out prediction error or divergence on unseen states. The verification procedure ensures consistency with all observed trajectories up to the current timestep, and successful planning through the model is required to solve the games, but this does not formally rule out correlated errors that happen to be consistent with the observed data. We will revise §4 to explicitly acknowledge this limitation, add a brief error analysis of the final models on the solved games (e.g., number of states where the model diverges from ground-truth dynamics when probed with additional synthetic inputs), and clarify that the 'preliminary evidence' claim rests on functional utility rather than exhaustive faithfulness verification. Systematic held-out testing across all games would require a more extensive experimental protocol that is outside the scope of the current work.","revision_made":"partial","referee_comment":"[Abstract and §4] Abstract and §4 (results): the central claim of 'preliminary evidence' for verifier-driven models rests on the assumption that LLM-mediated model construction produces faithful executables rather than merely trajectory-consistent ones. The manuscript reports solve counts and RHAE but supplies no quantitative checks (e.g., held-out observation prediction error, model-vs-ground-truth divergence on unseen states, or systematic error analysis) that would rule out correlated misunderstandings surviving the verification step."}],"tokens_in":1548,"tokens_out":342,"duration_ms":10460,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core result is a working baseline where the agent builds, verifies against observations, and simplifies its own Python world models before planning. It uses a scripted controller and game-agnostic prompts, runs fresh instances each time, and reports 15 full solves plus 58% mean RHAE with GPT-5.5 on the public set.\n\nThe audited harness and full artifact release are useful. They close obvious leakage paths and let others reproduce the exact runs. The decision to keep the controller and verifiers predefined while letting the LLM handle only model construction and refactoring is a clean split that avoids hand-coded game logic.\n\nThe main gaps are the lack of ablations, error bars, or failure-mode analysis in the reported numbers. The stress-test concern about correlated LLM errors surviving verification also lands: nothing in the abstract shows that the final models match the true mechanics rather than just the observed traces. Private-set performance is still unknown.\n\nPeople working on agent loops that combine code synthesis with planning will find the concrete setup and released code worth examining. It is grounded enough and ships enough artifacts to merit a serious referee, even though the current evidence is preliminary and the generalization claim needs more tests.","headline":"A coding agent maintains and refactors executable Python world models for ARC-AGI-3, solving 15 of 25 public games with the same prompts and audited harness, but private-set results and model-fidelity checks are still missing.","tokens_in":2422,"tokens_out":335,"would_cite":false,"duration_ms":13708,"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 coding agent maintains executable Python world models, verifies them against observations, and refactors for simplicity to solve ARC-AGI-3 games without any game-specific code or prompts.","keywords":["ARC-AGI-3","executable world models","coding agents","verifier programs","planning","Python models","game solving","AI agents"],"falsifier":"Running the same agent on the private ARC-AGI-3 validation set and checking whether the solve rate and mean RHAE remain comparable to the public-set numbers.","tokens_in":2697,"feed_emoji":"🤖","tokens_out":781,"duration_ms":17056,"temperature":0.7,"pith_summary":"The paper evaluates whether a single agent can build and use executable world models to handle diverse unseen games. The agent observes gameplay, checks its Python model against past moves via verifier programs, simplifies the code as a stand-in for preferring compact descriptions, and then plans actions inside that model. The same agent setup runs on all 25 public games, starting fresh each time with no prior game knowledge or leaked information. Results show full solutions on 15 games with one model version and 8 with another, along with mean scores of 58 percent and 41 percent. This supplies initial support for verifier-driven executable models as a workable strategy for this benchmark.","feed_headline":"Coding agent solves 15 of 25 ARC-AGI-3 games with executable world models","feed_subtitle":"One agent builds, verifies, and refactors Python models to plan actions across all games using identical prompts and no game-specific code.","key_machinery":"The verifier-driven executable world model: a Python program the agent builds from observations, checks with separate verifier code, refactors toward simpler form, and then queries to generate action plans.","core_discovery":"The agent maintains an executable Python world model, verifies it against previous observations, refactors it toward simpler abstractions, and plans through the model before acting. The system uses a scripted controller and predefined interfaces but contains no game-specific code, prompts, or heuristics, and the same agent instance applies across all games. On the 25 public ARC-AGI-3 games, it fully solved 15 with GPT-5.5 high reasoning effort (mean RHAE 58.12 percent) and 8 with GPT-5.4 (mean RHAE 41.29 percent). The results supply preliminary evidence that verifier-driven executable world models form a promising approach for ARC-AGI-3 agents.","pith_inferences":["The refactoring step may serve as a practical way to inject a simplicity preference into model construction without explicit minimum-description-length calculations.","The method could extend to other interactive environments where rules are expressible in executable code, provided observation and verification remain reliable.","Auditing and closing information-leakage channels in the harness shows one concrete way to reduce benchmark contamination when agents have broad system access.","Combining the world-model approach with additional search or ensemble techniques might raise solve rates further on harder instances."],"forward_implications":["The same agent and prompts can be reused on new games without any per-game modifications.","Higher reasoning effort in the underlying language model increases the number of fully solved games and the mean score.","Releasing full run artifacts and code allows direct reproduction and extension by others.","Performance on the private validation set will determine whether the approach generalizes beyond the public games."],"fun_headline_variants":["Coding agent solves 15 ARC-AGI-3 games with Python world models","Executable models verified by agent solve 15 of 25 ARC-AGI-3 games","Agent uses refactored Python models for 15 ARC-AGI-3 solutions","No game-specific code allows 15 ARC-AGI-3 solutions by coding agent"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"An agent can keep its executable world models accurate across many different unseen games by watching, verifying, and refactoring alone, without building in errors that break later planning.","fun_headline_variants_meta":{"raw":{"variants":["Coding agent solves 15 ARC-AGI-3 games with Python world models","Executable models verified by agent solve 15 of 25 ARC-AGI-3 games","Agent uses refactored Python models for 15 ARC-AGI-3 solutions","No game-specific code allows 15 ARC-AGI-3 solutions by coding agent"]},"model":"grok-4.3","cost_usd":0.012429,"raw_usage":{"total_tokens":5503,"prompt_tokens":848,"num_sources_used":0,"completion_tokens":78,"cost_in_usd_ticks":124287000,"prompt_tokens_details":{"text_tokens":848,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":4577,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":848,"tokens_out":78,"duration_ms":32817,"temperature":1.0,"reasoning_tokens":4577,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T23:27:49.399615+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same agent on the private ARC-AGI-3 validation set and checking whether the solve rate and mean RHAE remain comparable to the public-set numbers.","supporting_citations":[],"review_version":2}