{"id":"5a6eb22c-17d1-40c9-bc3d-7982c06f71c7","arxiv_id":"2607.02846","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"Object-Centric Environment Modeling (OCM) builds an online executable object-and-procedure code model that improves average rank and cuts invalid actions on ScienceWorld, ALFWorld, and PlanCraft.","lead":"OCM turns an LLM agent's past episodes into two linked Python codebases: object classes for environment entities and procedures that must use those classes. The design aims to make agent memory executable, checkable, and reusable instead of free-form text.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"Executability verification alone can commit semantically wrong but runnable object/procedure code, so the best average rank and invalid-action reductions do not cleanly demonstrate benefits of object-centric modeling.","rationale":"The reader correctly isolates the weakest link: executability is treated as a sufficient online filter even though the paper itself disclaims semantic guarantees (3.3, Limitations). That assumption is load-bearing for the strongest claim, because the performance edge and the ablation drop both depend on the verified knowledge base being useful, not merely runnable. No stronger internal inconsistency appears; the dual-code design, progressive disclosure, and three-benchmark results remain coherent systems contributions. Multi-seed/multi-model gaps and the mixed ALFWorld numbers are real but secondary to the verification gap already flagged. Hence the CONDITIONAL verdict stands; the concrete audit above would either confirm or further temper the causal language without requiring a harsher rejection.","tokens_in":16728,"tokens_out":582,"duration_ms":21972,"concrete_test":"Extract the final committed object and procedure files after a full PlanCraft or ScienceWorld online run (including the pink_terracotta / MELTSORT examples of Figs. 5–7). Against the environment’s ground-truth dynamics, label each public method and __verify__ assertion as factually correct, incomplete, or incorrect. If the fraction of incorrect/incomplete items exceeds ~20 % while the agent still records the published SR/invalid-action advantage, the verification-sufficiency assumption fails to underwrite the central claim.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim rests on the online loop (reflection → dual-code update → Verify) producing an environment model whose reuse yields best A.R. (Table 1) and lowest invalid actions (Fig. 3). Section 3.3 defines Verify(eKo, eKp) solely as conjunction of Exec(p | eKo) succeeding for every procedure file; the paper states this “does not prove that the learned model is semantically complete or always correct.” Limitations reiterate the same. Consequently an update may replace or append incorrect affordances, preconditions or recipes (e.g., wrong slot requirements or temperature thresholds) provided the Python still imports and the lightweight __verify__ assertions pass. Ablation Table 2 shows removing verification collapses PlanCraft SR from 49.6 to 39.3, proving the filter is load-bearing for the reported numbers, yet the filter is only syntactic. Progressive disclosure then surfaces that code to the policy LLM. The empirical gains can therefore be explained by any consistent, inspectable code memory plus extra post-episode LLM calls rather than by the object-centric coupling itself; the causal demonstration is under-supported.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The paper proposes Object-Centric Environment Modeling (OCM), an online framework that accumulates agent experience as two linked executable code bases: object knowledge (Python classes for entities, states, affordances, and mechanisms) and procedure knowledge (focused interaction patterns that must import and use the object model). After each episode, OCM reflects into an update plan, generates candidate code, and commits only if every procedure file executes successfully against the updated object module (Section 3.3). At decision time, progressive knowledge disclosure exposes signatures/indexes first and source only on demand. On ScienceWorld, ALFWorld, and PlanCraft with GPT-4.1-mini, OCM reports the best average rank (Table 1) and the lowest mean invalid-action counts on ScienceWorld and PlanCraft (Figure 3). Ablations on PlanCraft (Table 2) attribute gains to both knowledge bases and the verification gate; case studies illustrate recipe and melting-point reuse.","tokens_in":17012,"tokens_out":1416,"duration_ms":20105,"significance":"If the results hold under stronger controls, OCM occupies a useful middle ground between free-form textual memory and monolithic programmatic world models: shared object abstractions plus object-grounded procedures, with an auditable commit criterion. The dual-code design, progressive disclosure, and online verification loop are concrete systems contributions, and the paper supplies multi-benchmark online evaluation, ablations, invalid-action analysis, knowledge-growth curves, and detailed case studies rather than only end-task success. The work is relevant to memory-augmented and symbolic LLM agents. Strengths include an honest Limitations section on semantic incompleteness of verification and the explicit requirement that procedures import object knowledge rather than store isolated scripts.","major_comments":[{"comment":"Section 3.3 defines Verify solely as conjunction of Exec(p | eKo) over procedure files and states that this does not prove semantic completeness or correctness; Limitations repeats the point. Table 2 shows removing verification drops PlanCraft SR from 49.6 to 39.3, so the filter is load-bearing for the reported numbers, yet it only enforces runnability (plus lightweight __verify__ assertions). The central claim that gains demonstrate benefits of object-centric environment modeling therefore over-attributes: runnable but wrong affordances, recipes, or thresholds can still be committed and later disclosed. Please either (i) add environment-grounded or counterexample-based checks, or (ii) substantially qualify claims and report how often committed code is later contradicted by environment feedback.","section":null},{"comment":"Table 1 and the abstract credit object-centric coupling, but the experimental design does not isolate that design choice from generic structured code memory plus extra post-episode LLM calls. Closest controls are w/o Ko and w/o Kp (Table 2) and symbolic baselines (ASI, WorldCoder, Wall-E), yet there is no matched baseline that stores executable procedures without the import-from-Ko constraint, or non-object-centric code with the same reflection/update/verify budget. Without that control, best average rank and lower invalid actions (Figure 3) are consistent with several mechanisms. A targeted ablation or reimplementation control is needed for the causal claim in the abstract and Section 4.2.","section":null},{"comment":"All main results use a single backbone (GPT-4.1-mini) with no multi-seed variance, confidence intervals, or significance tests (Section 4.1; Table 1). ScienceWorld uses a custom sample of 149 tasks (Appendix A.1). Given modest absolute margins (e.g., ScienceWorld SR 34.8 vs. AWM 33.6; PlanCraft overall 49.6 vs. Reflexion 47.0) and mixed ALFWorld results where OCM is not best, the 'best average rank' headline is sensitive to sampling and ranking aggregation. Report multi-run variability, at least one additional model family/size, and justify or release the ScienceWorld sample so the ranking claim can be assessed.","section":null},{"comment":"Section 4.2 notes ALFWorld is mixed and attributes weaker gains to household routines where textual lessons suffice, but does not analyze failure modes, knowledge reuse rates, or whether progressive disclosure helps or hurts there. Because average rank pools all three benchmarks, ALFWorld is load-bearing for the cross-benchmark claim. Provide a short failure analysis (invalid actions, inspection frequency, procedure applicability) or down-weight the universality of the conclusion when transfer is routine- rather than mechanism-driven.","section":null}],"minor_comments":[{"comment":"Figure 1 is conceptually clear but the 'overcomplicated machine' metaphor is informal for a methods paper; a more precise schematic of skill-only vs. monolithic WM vs. OCM would help.","section":null},{"comment":"Appendix A.2: WorldCoder is substantially adapted (post-episode transition/reward synthesis rather than the original formulation). Flag this more prominently in the main baseline discussion so readers do not treat it as an off-the-shelf comparison.","section":null},{"comment":"Table 1 formatting: PlanCraft columns are dense; separating difficulty levels into a subtable would improve readability. Also clarify whether 'impossible' tasks enter the overall SR and average rank equally.","section":null},{"comment":"Notation: K^k_o / K^k_p vs. eKo / eKp in Verify is slightly inconsistent; unify candidate vs. committed notation in Section 3.3.","section":null},{"comment":"Typos/style: 'V oyager' spacing (Related Work); 'Expel' vs. 'ExpeL' inconsistency in Table 1 vs. text; arXiv-style line breaks in the abstract are fine for preprint but should be cleaned for journal production.","section":null},{"comment":"Prompts in A.3 are valuable for reproducibility; consider releasing the full code and learned knowledge dumps as stated in the header link so verification behavior can be audited independently.","section":null}],"recommendation":"major_revision","confidential_remarks":"Fit is appropriate for a systems/AI-agent venue. Novelty is real but incremental relative to Voyager/ASI/WorldCoder/AWM; the dual-code + verify loop is the main differentiator. I would not reject on novelty alone. The skeptic concern about syntactic verification is valid and should be addressed in revision; if the authors only add prose caveats without a control or stronger check, I would remain at major revision rather than accept. No integrity concerns from the manuscript as provided."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The useful takeaway is simple: OCM is a clean online loop that forces procedural experience to import a shared Python object model, then only commits updates that still execute. That design sits in a genuine gap between Voyager-style skill piles and WorldCoder-style transition models, and the paper ships the full recipe—reflection plan, dual-code update, Verify as conjunction of Exec, progressive signature-first disclosure—plus three-benchmark online results, PlanCraft ablations, invalid-action distributions, and concrete code case studies.\n\nWhat is new is the packaging and the invariant, not a new theory of objects. Object classes for entities/affordances plus procedures that must import them, with post-episode repair until everything runs, is a practical substrate other agent-memory papers have not put together this way. The experiments are honest enough for a systems claim: best average rank, strongest ScienceWorld/PlanCraft numbers, lowest mean invalid actions, and ablations that show removing Ko, Kp, or verification each hurts. The case studies (pink terracotta recipe, melting-point procedure reuse) show the intended division of labor rather than pure narrative.\n\nSoft spots, in proportion. The stress-test is right that Verify is only executability plus lightweight __verify__ assertions; the paper says so itself. So the filter can admit runnable but wrong affordances, and some of the gain could come from “any consistent inspectable code memory + extra LLM calls” rather than object-centric coupling alone. ALFWorld is mixed, there is one backbone, no error bars, and ScienceWorld is a sampled 149-task slice. Those are real limits on how hard you should lean on the strongest claim, not reasons to dismiss the work. Citation pattern is appropriate; free parameters (repair attempts, inspection budget, baseline hyperparams) are normal for this genre.\n\nThis is for people building LLM agents who care about maintainable symbolic memory. It deserves a serious referee. I would engage with it, cite the dual-codebase + verification design when discussing agent memory substrates, and treat the empirical ranking as provisional until multi-model/multi-seed releases land. Send it to peer review.","headline":"Solid systems paper that packages a real middle ground between skill libraries and world models; gains are real but the causal story for “object-centric” is only partly locked down.","tokens_in":17650,"tokens_out":538,"would_cite":true,"duration_ms":6073,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"LLM agents improve by building a shared, executable object model of the environment and grounding procedures in it.","keywords":["LLM agents","object-centric modeling","executable knowledge","online learning","procedural knowledge","world models","ScienceWorld","ALFWorld"],"falsifier":"On the same online ScienceWorld, ALFWorld, and PlanCraft splits with the same backbone, a strong textual-memory or skill-only baseline that matches OCM’s average rank and invalid-action counts would falsify the claim that the object-centric dual-code design is what drives the gains.","tokens_in":17573,"feed_emoji":"🧩","tokens_out":590,"duration_ms":5404,"temperature":0.7,"pith_summary":"LLM agents get better by reusing past experience, but free-form text memories grow messy and hard to check, while pure skill libraries or full world models each miss half the structure. This paper proposes Object-Centric Environment Modeling (OCM): after every episode the agent writes two linked code bases—Python classes for environment objects and their mechanisms, plus procedure files that must import and use those classes—then commits the update only if every procedure still runs against the object module. During later play it first shows compact signatures and lets the agent open source only when needed. Across ScienceWorld, ALFWorld, and PlanCraft the method earns the best average rank and cuts invalid actions, arguing that agents benefit from treating experience as an auditable object-centric environment model rather than scattered notes or isolated scripts.","feed_headline":"Agents learn better with an executable object model of the world","feed_subtitle":"Shared Python classes plus grounded procedures cut invalid actions and rank first across three benchmarks","key_machinery":"Object-Centric Environment Modeling (OCM): two connected code bases—object knowledge as Python classes and procedure knowledge as files that must import them—joined by post-episode reflection, update, and an executability check that all procedures run against the updated object module, plus progressive disclosure of signatures before source.","core_discovery":"Organizing online experience into a shared executable object model (Python classes for entities and mechanisms) plus procedures that are forced to import and use that model, then verifying that every procedure still executes after each update, yields better average performance and fewer invalid actions than free-form memory, skill libraries, or monolithic programmatic world models on three interactive benchmarks.","pith_inferences":[],"forward_implications":[],"fun_headline_variants":["Object-centric models turn experience into executable Python worlds","Shared object classes force procedures to stay grounded and valid","Verified object models cut invalid actions and rank first overall","Online object updates beat free-form memory on agent benchmarks","Agents reuse executable entity code instead of growing text piles"],"cache_read_input_tokens":128,"weakest_assumption_plain":"That checking only whether every procedure file still runs against the updated object module is enough of a filter to keep online knowledge useful, even though the paper states this does not prove the mechanisms are complete or correct.","fun_headline_variants_meta":{"raw":{"variants":["Object-centric models turn experience into executable Python worlds","Shared object classes force procedures to stay grounded and valid","Verified object models cut invalid actions and rank first overall","Online object updates beat free-form memory on agent benchmarks","Agents reuse executable entity code instead of growing text piles"]},"model":"grok-4.5","effort":"low","cost_usd":0.004758,"raw_usage":{"total_tokens":1333,"prompt_tokens":714,"num_sources_used":0,"completion_tokens":60,"cost_in_usd_ticks":47580000,"prompt_tokens_details":{"text_tokens":714,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":559,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":714,"tokens_out":60,"duration_ms":4655,"temperature":1.0,"reasoning_tokens":559,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-12T06:38:12.286591+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"On the same online ScienceWorld, ALFWorld, and PlanCraft splits with the same backbone, a strong textual-memory or skill-only baseline that matches OCM’s average rank and invalid-action counts would falsify the claim that the object-centric dual-code design is what drives the gains.","supporting_citations":[],"review_version":1}