{"id":"06bcc7f3-1c0e-4c6f-b8ff-8395b60f9d11","arxiv_id":"2603.23844","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"LLM-as-Higher-Order-Formalizer generates compact programs that expand into large PDDL, improving planning formalization where direct LLM formalizers fail on unraveling problems.","lead":"The paper argues that LLM formalizers for planning fail on \"unraveling\" problems where short natural-language specs expand into huge PDDL, and proposes higher-order formalizers that emit compact programs which then generate the PDDL. This could matter for anyone building LLM-plus-solver planning stacks that must handle real, combinatorially large domains rather than toy benchmarks.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"Central claim cannot be stress-tested: supplied full text is the wrong paper (3D-LLDM), so the unraveling-proxy premise and reported scaling gains remain uninspectable.","rationale":"The reader correctly diagnosed that only the abstract is usable and that the weakest assumption is the un-justified claim that standard benchmarks depart from real use cases while the authors’ unraveling variants are a faithful proxy. The supplied full text is the wrong paper, so no further experimental or definitional evidence can be inspected. My concern is identical to the reader’s; therefore the UNVERDICTED / LOW-confidence status is left unchanged. A full re-review against the correct PDF is required before any stronger verdict can be issued.","tokens_in":6361,"tokens_out":546,"duration_ms":18737,"concrete_test":"Retrieve the correct PDF of arXiv:2603.23844. Confirm that §§3–4 define the four unraveling domains with an explicit size parameter n (objects/actions growing with n) and report success rates (or plan validity) for direct formalizer vs. higher-order formalizer as n increases. If no clear scaling advantage appears (e.g., absolute success-rate gap <20 % at largest n) or if the domains remain essentially one-to-one, the central claim weakens.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim is that an LLM-as-Higher-Order-Formalizer (emitting a compact program that expands into large PDDL) decouples token budget from combinatorial growth and thereby outperforms direct LLM-as-Formalizer on complex problems. For that claim to hold, two linked conditions must be true: (1) standard benchmarks’ near one-to-one NL-to-PDDL mapping is unrepresentative of real formalization difficulty, and (2) the authors’ “unraveling variants of four standard planning domains” are a faithful enough proxy that failure of direct formalizers (and recovery via higher-order programs) on them generalizes. The abstract asserts both but supplies neither domain definitions, size-scaling laws, success-rate tables, nor any independent justification that the one-to-one mapping “departs from real use cases.” Because the CACHEABLE full-manuscript block is an unrelated medical-imaging paper (arXiv 2603.23845), none of the experimental constructions, baselines, or ablations can be examined. The load-bearing gap is therefore that both the ecological-validity premise and the claimed performance recovery are currently uncheckable.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The abstract of arXiv:2603.23844 argues that LLM-as-Formalizer success on planning is overstated because standard benchmarks often admit near one-to-one natural-language-to-PDDL mappings. It introduces “unraveling problems,” in which a succinct description expands into a very large PDDL representation, claims that direct formalizers fail to scale on unraveling variants of four standard domains, and proposes LLM-as-Higher-Order-Formalizer: the LLM emits a compact high-level program that encodes recurrent structure and then expands into the full PDDL, decoupling token budget from combinatorial growth and improving performance on complex instances.","tokens_in":6608,"tokens_out":705,"duration_ms":14280,"significance":"If the unraveling construction is a faithful stress test and the higher-order program approach demonstrably recovers solver success where direct formalization fails, the work would be a useful methodological contribution to LLM planning: it would separate formalization scalability from search scalability and give a concrete alternative to pure chain-of-thought planning. Those claims, however, cannot be assessed from the materials supplied for review.","major_comments":[{"comment":"The full manuscript text provided under the CACHEABLE PAPER SOURCE CONTEXT is an entirely different paper (3D-LLDM, arXiv:2603.23845, on label-guided 3D latent diffusion for hepatic MR synthesis). It contains no PDDL, no planning domains, no unraveling constructions, no formalizer baselines, and no higher-order program results. The central claims of 2603.23844 are therefore uninspectable.","section":null},{"comment":"Because the correct manuscript is absent, load-bearing elements cannot be checked: definitions and size-scaling of the four unraveling domains; quantitative success rates of LLM-as-Formalizer vs. LLM-as-Higher-Order-Formalizer; ablations isolating the program-generation step; and any independent justification that one-to-one NL–PDDL benchmarks “depart from real use cases.” Without these, the abstract’s performance and ecological-validity claims remain unsupported.","section":null},{"comment":"The review cannot proceed on abstract alone. A formal journal evaluation requires the actual methods, experimental design, tables, and discussion of 2603.23844. The present package is not a reviewable submission for that paper.","section":null}],"minor_comments":[{"comment":"Abstract only: the term “unraveling problems” and the four domain variants are undefined; even a short formal sketch would be needed in any resubmission.","section":null},{"comment":"Abstract only: “improved performance for complex problems” is stated without metrics, baselines, or problem-size axes; any future manuscript should report solver success rates and token/program length scaling.","section":null}],"recommendation":"reject","confidential_remarks":"The supplied full text is the wrong arXiv paper (2603.23845 medical imaging vs. claimed 2603.23844 planning formalizers). This looks like a packaging or cache error rather than author misconduct, but it makes a substantive technical review impossible. Recommend returning the submission and requesting the correct PDF before any further review cycle. Do not treat the 3D-LLDM content as evidence for or against the planning claims."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"We cannot actually review this paper yet. The target is arXiv 2603.23844 on LLMs as higher-order planning formalizers. What got attached as the full manuscript is a completely different work (3D-LLDM, hepatic MR synthesis). So everything below is from the abstract alone, and confidence has to stay low.\n\nFrom that abstract, the move is clear and worth naming. They argue that LLM-as-formalizer success on standard planning suites may be inflated because those NL descriptions often map almost one-to-one onto PDDL. They introduce “unraveling” variants of four domains—succinct natural language that expands into a large PDDL encoding—and show direct formalizers fail to scale. The proposed fix is LLM-as-Higher-Order-Formalizer: the model emits a compact program that captures the recurrent structure and then expands into the full PDDL. That decouples token budget from combinatorial growth. If the experiments hold, that is a clean architectural contribution inside the LLM-for-planning niche.\n\nWhat we cannot assess: whether the unraveling domains are a fair proxy for real formalization difficulty, how large the PDDL blow-up actually is, what baselines and solvers were used, success rates, ablations, or error analysis. The ecological-validity premise (“departs from real use cases”) is asserted, not justified in the text we have. No code, tables, or domain definitions are available here.\n\nWho it is for: people already working on LLM formalizers and PDDL generation. Not a theory paper; a methods/benchmark stress-test paper. On the abstract’s face it is coherent and non-circular, so a serious editor should send the real PDF to referees rather than desk-reject. I would not cite or bring it to reading group until we have the correct manuscript. Get the right PDF and re-read; until then this is an idea sketch, not a finished result.","headline":"Useful idea on paper, but we only have the abstract—the supplied full text is a different medical-imaging paper, so none of the claims can be checked.","tokens_in":7268,"tokens_out":494,"would_cite":false,"duration_ms":47257,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"Language models scale better as planning formalizers when they emit compact programs that expand into large PDDL, not the PDDL itself.","keywords":["large language models","planning","PDDL","formalization","higher-order formalizer","unraveling problems","program synthesis"],"falsifier":"Measure whether, on the same unraveling instances, a direct LLM formalizer (or a stronger long-context formalizer) matches or exceeds the higher-order program approach in correct PDDL generation and final plan success once output-length limits are removed or controlled for.","tokens_in":7208,"feed_emoji":"🧩","tokens_out":822,"duration_ms":12916,"temperature":0.7,"pith_summary":"Standard LLM-as-formalizer pipelines translate natural-language planning problems into PDDL and hand them to a classical solver. That approach looks strong on common benchmarks, but those benchmarks usually have a near one-to-one mapping from the description to the PDDL, which is not how real problems are stated. The paper introduces “unraveling” variants of four classic domains in which a short natural description must expand into a combinatorially large formalization; direct formalizers fail to scale on them. The proposed fix is a higher-order formalizer: the model writes a short program that encodes the recurrent structure and then expands that program into the full PDDL. Because the model’s token budget is no longer tied to the size of the expanded representation, performance on complex unraveling instances improves.","feed_headline":"LLMs formalize hard planning by writing expander programs","feed_subtitle":"A short program that grows into large PDDL beats direct formalization on unraveling domains","key_machinery":"LLM-as-Higher-Order-Formalizer: the model emits a compact program that encodes the repeated structure of an unraveling problem; executing that program yields the large PDDL instance that a classical solver then uses.","core_discovery":"Direct LLM-to-PDDL formalization does not reliably scale once a succinct natural-language description must be expanded into a very large structured representation. Generating an intermediate high-level program that captures the recurrent logic, then letting that program produce the PDDL, decouples the model’s output length from the combinatorial size of the formalization and restores better planning performance on those hard instances.","pith_inferences":["If the higher-order program is itself buggy, the expanded PDDL will be systematically wrong; verification of the generator becomes as important as verification of the expanded instance.","The approach suggests a general recipe for any domain where natural language is succinct but the underlying symbolic encoding is large: have the model write the expander, not the expansion.","Real-world planning tools that already accept programmatic domain generators could absorb this paradigm with little change to the solver stack."],"forward_implications":["Unraveling-style benchmarks become a necessary stress test for any claim that LLMs can formalize planning problems at scale.","Token budget for formalization can be spent on a short generative program rather than on enumerating the expanded domain, reducing output length for large instances.","Classical PDDL solvers remain the search engine; the LLM’s job is only to produce a correct, compact generator of the formalization.","The same higher-order pattern may apply to other structured formalisms that expand combinatorially from a short natural description."],"fun_headline_variants":["LLMs scale planning by writing programs that expand into large PDDL","Higher-order formalizers: LLMs generate expanders for complex PDDL","Unraveling domains show direct formalization fails; expanders succeed","LLM expander programs beat direct PDDL on hard planning instances","Capturing recurrent logic in code decouples LLMs from PDDL explosion"],"cache_read_input_tokens":128,"weakest_assumption_plain":"The claim rests on the idea that standard planning benchmarks’ near one-to-one natural-language-to-PDDL mappings are unrealistic, and that the authors’ hand-constructed unraveling variants are a fair enough proxy for real formalization difficulty.","fun_headline_variants_meta":{"raw":{"variants":["LLMs scale planning by writing programs that expand into large PDDL","Higher-order formalizers: LLMs generate expanders for complex PDDL","Unraveling domains show direct formalization fails; expanders succeed","LLM expander programs beat direct PDDL on hard planning instances","Capturing recurrent logic in code decouples LLMs from PDDL explosion"]},"model":"grok-4.5","effort":"low","cost_usd":0.00579,"raw_usage":{"total_tokens":1525,"prompt_tokens":744,"num_sources_used":0,"completion_tokens":94,"cost_in_usd_ticks":57900000,"prompt_tokens_details":{"text_tokens":744,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":687,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":744,"tokens_out":94,"duration_ms":6907,"temperature":1.0,"reasoning_tokens":687,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-13T19:18:01.717034+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Measure whether, on the same unraveling instances, a direct LLM formalizer (or a stronger long-context formalizer) matches or exceeds the higher-order program approach in correct PDDL generation and final plan success once output-length limits are removed or controlled for.","supporting_citations":[],"review_version":1}