{"id":"194ed458-6532-4d13-877c-c957d78ab160","arxiv_id":"2508.11885","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"The abstract claims a deformable foot model improves gait simulation, but the manuscript body is an unrelated visual token pruning paper, so the claim is unsupported.","lead":"This preprint claims a new deformable, contact-rich foot model for musculoskeletal human walking simulation, trained with a two-stage reinforcement learning strategy. The submitted full text, however, is an unrelated paper on visual token pruning for multimodal segmentation models.","discovery_kind":"unclear","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The submission is internally inconsistent: the abstract claims a deformable foot model for locomotion control, but the full text is an unrelated visual-token-pruning paper, so the central claim has no supporting content in the manuscript.","rationale":"The reader identified the absent supporting material as the weakest assumption, and the same concern emerges directly from the manuscript text. The abstract describes a robotics/biomechanics contribution, while the full text is an unrelated computer vision paper. This is not a matter of scientific disagreement or a hidden error in an equation; it is a complete absence of the claimed derivation, experiments, and validation. Under the instruction to treat all manuscript passages as in-scope evidence, the mismatch is decisive. The only fair disposition is to decline to render a scientific verdict on the foot model, which is exactly UNVERDICTED. I therefore agree with the reader's verdict and do not propose any change. A keyword-based integrity check would settle the issue objectively and prevent any concern that the wrong file was analyzed.","tokens_in":16932,"tokens_out":1934,"duration_ms":20841,"concrete_test":"Download the submitted file for arXiv:2508.11885 and run a full-text keyword scan for 'foot', 'musculoskeletal', 'gait', 'deformable', 'policy', and 'human subject'. If none of these terms appear in the body, and if the body's header contains arXiv:2508.11886v1, the mismatch is confirmed and the claimed results are not present in the submission.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing problem is not a debatable modeling assumption but the absence of the claimed work itself. The abstract's central claim concerns a contact-rich deformable foot model integrated in a musculoskeletal system, a two-stage policy training strategy, and validation against human gait data. The full text, however, is EVTP-IVS, a paper on visual token pruning for multimodal LLMs, with its own abstract, methods, experiments, and references. The body contains no musculoskeletal model, no foot-ground contact mechanics, no deformable tissue parameters, no control policy, no gait stability analysis, and no human-subject validation. The manuscript header even exposes an arXiv identifier, 2508.11886v1, that differs from the submission's identifier. Because every component needed to support the strongest claim is missing, the central claim cannot be evaluated on the submitted content. The reader's UNVERDICTED disposition is correct: this is a manuscript-content mismatch rather than a scientifically assessable failure of the foot model.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript arXiv:2508.11885 is submitted under the title \"Contact-Rich and Deformable Foot Modeling for Locomotion Control of the Human Musculoskeletal System,\" and its abstract claims a novel deformable foot model integrated into a musculoskeletal system, a two-stage policy training strategy, improvements over rigid musculoskeletal models in kinematic, kinetic, and gait stability metrics, and validation against human walking data. The submitted full text, however, is an entirely different paper: \"EVTP-IVS: Effective Visual Token Pruning For Unifying Instruction Visual Segmentation In Multi-Modal Large Language Models,\" with a different author list, its own abstract, method, experiments, references, and appendices. The body contains no foot model, no musculoskeletal simulation, no foot-ground contact mechanics, no deformable tissue parameters, no control policy, no gait stability analysis, and no human-subject validation. The only link between the title and the full text is the arXiv header, which displays identifier 2508.11886v1 rather than 2508.11885. Because every load-bearing component of the claimed contribution is absent from the manuscript, the central claim cannot be evaluated on the submitted content.","tokens_in":17135,"tokens_out":2453,"duration_ms":25867,"significance":"If substantiated, a contact-rich deformable foot model integrated with a musculoskeletal system and validated against human gait data would be a useful contribution to biomechanics simulation and humanoid locomotion control. However, this manuscript provides no evidence toward that contribution: the full text is a visual-token-pruning paper whose methods, equations, experiments, and references are unrelated to locomotion. The EVTP-IVS portion appears to contain an internally coherent empirical study, including coverage-based pruning experiments and speedup measurements, but that work cannot be credited toward the foot-model claims made in the abstract. There are no machine-checked proofs, reproducible code, parameter-free derivations, or falsifiable predictions relevant to the abstract's central claim within the submitted content.","major_comments":[{"comment":"The central claim of the abstract—that a novel contact-rich and deformable foot model integrated within a musculoskeletal system improves kinematic, kinetic, and gait stability metrics over rigid models and closely reproduces human walking measurements—is entirely unsupported by the submitted manuscript. The full text is EVTP-IVS, a paper on visual token pruning for multimodal large language models. It contains no musculoskeletal model, no deformable foot geometry or tissue properties, no contact model, no two-stage policy training, no gait simulation, and no comparison against human subject data. This is not a debatable modeling assumption but the complete absence of the claimed work, so the manuscript cannot be evaluated scientifically for the stated contribution.","section":"Full text, all sections"},{"comment":"The methods and experimental sections of the submitted text concern k-center token selection with spatial augmentation, FLOPs estimation, and instructed visual segmentation benchmarks on RefCOCO, ReasonSeg, ReVOS, and related datasets. Equations (1)–(6) define pruning objectives, not foot-ground contact mechanics, and Tables 1–4 report segmentation metrics, not gait kinematics or kinetics. These contents cannot serve as the derivation, simulation setup, or validation for the abstract's claims about locomotion control, so the abstract's assertions about comparative gait improvements and human-subject validation have no evidentiary basis in this manuscript.","section":"EVTP-IVS Sections 4–6 and Tables 1–4"}],"minor_comments":[{"comment":"The arXiv identifier printed in the full-text header is 2508.11886v1 [cs.CV] dated 16 Aug 2025, whereas the submission identifier is 2508.11885 (cs.RO); this mismatch should be reconciled if the correct manuscript is resubmitted.","section":"Header/metadata"},{"comment":"The reference list contains no entries on biomechanics, musculoskeletal modeling, foot anatomy, contact simulation, or human gait; all cited works are about vision-language models and visual token pruning, which further confirms that the body text does not correspond to the abstract's topic.","section":"References"},{"comment":"The concluding section of the submitted text states that the work presents \"the first study on visual token pruning for IVS\" and discusses inference acceleration; this is irreconcilable with the abstract's claim of a deformable foot model for locomotion control, and the mismatch should be corrected by submitting the intended paper.","section":"EVTP-IVS Section 8"}],"recommendation":"reject","confidential_remarks":"This submission appears to be an assembly error: the abstract and title describe a robotics/biomechanics paper, while the full text is a computer-vision paper with a different arXiv ID and different authors. I recommend returning the manuscript to the authors without further technical review, since the claimed contribution is absent in its entirety. If a corrected version with the actual foot-model paper is submitted, it would need a full methods section, simulation details, material parameters, policy training description, gait metrics, and human-subject validation to be assessable."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe submission you sent me has a total mismatch between the abstract and the body. The abstract claims a novel deformable foot model for musculoskeletal locomotion, with two-stage policy training and validation against human gait data. The full text is a different paper, EVTP-IVS, about visual token pruning for multimodal LLMs. There is no musculoskeletal model, no foot-ground contact, no policy, no gait analysis anywhere in the body. The abstract's central claims are therefore unsupported by any content.\n\nTo be fair, the body is a complete paper with experiments, ablations, and a theoretical argument about token coverage. If that paper were submitted under its own title, it might be worth a look. But it is not the paper described by the abstract, and the author lists don't even match. The header also shows an arXiv ID (2508.11886v1) that differs from the submitted ID (2508.11885), which looks like an upload error rather than a deliberate deception. Still, the effect is the same: the claimed work is absent.\n\nThe one thing that could be assessed would be the abstract itself, and it asserts 'validation against human subject data' and improvements over rigid models without any accompanying derivation or evidence. Even if the foot model were present, we'd need the material parameters, contact model, and simulation setup to judge it. None of that appears here.\n\nBecause the mismatch is total, there is no scientific content to referee. The right move is to return this to the authors for a corrected submission, not to send it to reviewers. If the authors intended to submit the foot model paper, they need to upload the correct manuscript; if they intended the token pruning paper, they need to fix the metadata. Either way, the current version is not a reviewable paper.\n\nMy advice: desk-reject or return without review. It would be a waste of referee time to evaluate a missing manuscript.","headline":"The abstract and body are two different papers; the foot model is never presented, so the submission cannot be evaluated as is.","tokens_in":17578,"tokens_out":2569,"would_cite":false,"duration_ms":25584,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Replacing the rigid foot with a deformable, contact-rich model inside a full musculoskeletal simulation yields more human-like walking kinematics, kinetics, and stability, and reproduces measured human gait.","keywords":["musculoskeletal simulation","deformable foot model","foot-ground contact","locomotion control","policy training","gait analysis","humanoid robotics","biomechanics"],"falsifier":"Run the trained deformable-foot simulation through the same walking trials used for human motion capture and compare the predicted vertical ground-reaction-force profile, center-of-pressure path, and plantar pressure distribution against force-plate data. If the deformable-foot model fails to beat a rigid-foot model on those measurements by a clear margin, or if its values fall outside the spread of the human subjects, the central claim is refuted.","tokens_in":16759,"feed_emoji":"🦶","tokens_out":4759,"duration_ms":51716,"temperature":0.7,"pith_summary":"The paper claims that oversimplified rigid foot-ground contact is a key limit in musculoskeletal walking simulation, and that a deformable, contact-rich foot model fixes it. The authors embed this foot model in a complete musculoskeletal system and use a two-stage policy training strategy to handle the resulting multi-point contacts and tissue deformation. They report improvements over rigid musculoskeletal models in kinematic, kinetic, and gait-stability metrics, and say their simulation closely reproduces real-world biomechanical measurements of walking. If this holds, biomechanical simulation and humanoid robotics gain a more faithful account of how feet actually interact with the ground.","feed_headline":"Deformable foot model makes simulated walking match human data","feed_subtitle":"A two-stage training policy turns multi-point foot contact into natural gait for musculoskeletal and robot control.","key_machinery":"The load-bearing object is the deformable foot model: a contact-rich representation of the foot that computes multi-point, deformable interaction with the ground, integrated into a complete musculoskeletal body. Around it, a two-stage policy training strategy makes control tractable by first learning a natural walking pattern and then refining it under the full multi-contact dynamics. The foot model carries the argument because it is the main difference between the proposed system and the rigid-baseline comparison, while the two-stage training is the mechanism that lets that difference be exploited.","core_discovery":"The central claim is that foot-ground interaction must be modeled as deformable and contact-rich rather than as a few rigid contact points in order to reproduce human walking dynamics. The paper develops a deformable foot model integrated into a complete musculoskeletal system, and shows that this interface-enhanced model outperforms conventional rigid musculoskeletal models on kinematic, kinetic, and gait-stability measures. The authors further validate against human subject data, reporting that the simulation closely reproduced real biomechanical measurements. On the paper's own terms, the deformable interface is what closes the gap between simulated and measured gait.","pith_inferences":["The paper leaves open whether the gains come from tissue deformation per se or from the richer multi-point contact geometry; a variant with a rigid foot but many contact points could separate the two.","If tissue parameters are varied, the same model could predict gait changes in conditions such as flatfoot or aging, an extension the paper does not test.","For humanoid robotics, the practical promise is a sim-to-real transfer path: a policy trained on this contact-rich foot may transfer more reliably to hardware because the ground-reaction feedback is more realistic."],"forward_implications":["Simulations using the deformable foot outperform rigid-foot models on kinematic, kinetic, and gait-stability metrics during walking.","A two-stage policy training strategy is sufficient to control a full musculoskeletal model with multi-point deformable contacts, removing a control bottleneck.","The simulation's walking output closely tracks human biomechanical measurements, supporting its use as a surrogate for gait experiments.","The same foot-ground interaction modeling and training framework can be extended to humanoid robots that need precise foot-ground control."],"supporting_citations":[],"fun_headline_variants":["Deformable foot beats rigid for human-like walking sims","Contact-rich feet close gait simulation gap","Deformable foot modeling makes gait sims human-accurate","Two-stage training with deformable feet yields natural gait","Deformable foot model closes sim-to-human gait gap"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The deformable foot's tissue properties and contact equations must faithfully represent real biological foot-ground interaction, because everything about the learned gait and the claimed match to human data depends on that realism.","fun_headline_variants_meta":{"raw":{"variants":["Deformable foot beats rigid for human-like walking sims","Contact-rich feet close gait simulation gap","Deformable foot modeling makes gait sims human-accurate","Two-stage training with deformable feet yields natural gait","Deformable foot model closes sim-to-human gait gap"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001248,"raw_usage":{"total_tokens":5045,"prompt_tokens":802,"completion_tokens":4243,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":418,"completion_tokens_details":{"reasoning_tokens":4164}},"tokens_in":418,"tokens_out":4243,"duration_ms":31706,"temperature":1.0,"reasoning_tokens":4164,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T17:26:36.878270+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the trained deformable-foot simulation through the same walking trials used for human motion capture and compare the predicted vertical ground-reaction-force profile, center-of-pressure path, and plantar pressure distribution against force-plate data. If the deformable-foot model fails to beat a rigid-foot model on those measurements by a clear margin, or if its values fall outside the spread of the human subjects, the central claim is refuted.","supporting_citations":[],"review_version":1}