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REVIEW 4 major objections 6 minor 31 references

Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that even the best LLMs are unsafe as embodied decision makers: they refuse overtly harmful commands reliably, yet the strongest model completes a fully safe action plan only 44.7% of the time, usually because a required…

desk verdict Useful benchmark and a plausible core finding, but the headline action-sequencing numbers may be partly an artifact of a prompt that forbids the very actions the ground truth requires. read the letter →

arxiv 2505.19933 v1 pith:LMPN6NSJ submitted 2025-05-26 cs.AI

Yejin Son , Minseo Kim , Sungwoong Kim , Seungju Han , Jian Kim , Dongju Jang , Youngjae Yu , Chanyoung Park This is my paper · ORCID
classification cs.AI
keywords LLMsafetyevaluationembodieddecisionmakingphysicalPDDLplanningactionsequencingcommandrefusalhouseholdrobotics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that LLM-based embodied agents are not yet safe for physical deployment, even when they reliably refuse overtly harmful commands. It introduces SAFEL, an evaluation framework that splits embodied safety into command refusal and plan safety, and EMBODYGUARD, a benchmark of 942 household scenarios spanning deceptively ordinary instructions with hidden physical hazards alongside overtly malicious ones, all written in the Planning Domain Definition Language (PDDL) so that every plan can be checked against formal rules. Across 13 state-of-the-art LLMs, the authors find that most models refuse explicit harmful commands at 82.8–99.1% recall, yet the best model, o1, completes a fully safe action plan only 44.7% of the time. The dominant failure mode is the missing step: models omit the safety-critical action, such as unplugging a live wire before boiling a kettle, that keeps an otherwise normal task from causing harm. If the results hold, refusal-style safety tests give false confidence, and embodied safety claims must be verified by plan execution in a simulated environment rather than by what the model says it would refuse.

What carries the argument

The carrying mechanism is the PDDL plan: every EMBODYGUARD scenario is written in the Planning Domain Definition Language, whose actions carry explicit preconditions and effects, so an LLM's plan can be replayed against a shared, hand-written iGibson household domain by a symbolic executor. The executor checks each action's name, argument types, and preconditions against the evolving formal state and classifies every failure into one of six categories: Missing Step, Affordance error, Wrong Temporal order, Additional Step, Unmet Goal, or Grammar error — a taxonomy that turns "the plan failed" into a specific, localizable diagnosis. The framework then attributes each failure to one of three reasoning modules — Goal Interpretation, Transition Modeling, and Action Sequencing — plus the Command Refusal test, so a model's weakness is pinned to a single competence rather than to perception, grounding, or motor control. Newly defined actions such as unwrap_foil and unplug_wire carry the scenario-specific hazards, which is what lets the benchmark test subtle risks while staying inside the formal substrate.

What would settle it

Execute the benchmark's situational scenarios in a full physics-based simulator, including the scenarios with newly defined safety actions that the paper's pilot simulation deliberately excluded (Appendix E), and compare actual outcomes with the benchmark's error labels; if a plan the symbolic executor flags as a missing-step failure turns out physically harmless when executed, or a "ground-truth safe" plan causes harm, EMBODYGUARD would be measuring its own axioms rather than physical safety.

Watch

Extended reading notes

Core claim

SAFEL and EMBODYGUARD answer one question: where does safe embodied decision-making break down inside an LLM? The paper's answer is that the breakage sits not in refusal but in planning. Models refuse overtly malicious commands at high recall and accept benign ones perfectly, but they fail to reason about the intermediate conditions that make an action safe: they mispredict an action's effects and preconditions, and they omit or misorder the steps that neutralize hazards. On the situational subset, all 13 evaluated models failed to produce a fully safe, executable plan in a majority of scenarios; the best model, o1, succeeded only 44.7% of the time, with missing-step errors the largest single cause of failure across all strong models. The paper concludes that current LLMs lack a reliable working model of state transitions and action preconditions, and that plans must be checked by simulated execution rather than by surface inspection.

Load-bearing premise

The whole evaluation rests on the assumption that the hand-written rule book used to judge plans — the Planning Domain Definition Language (PDDL) axioms specifying each household action's preconditions and effects — correctly matches how physical harm actually happens, so that a plan the rules approve would really be safe and a plan the rules flag would really cause harm; this is the paper's declared "we assume that the domain rules are predefined" (Section 2.1), and Section 7 concedes that automated verification alone cannot guarantee the rules preserve their intended commonsense meaning.

Editorial extensions

If this is right

  • Refusal accuracy cannot certify embodied safety: most models refuse overtly malicious commands at 82.8–99.1% recall, yet every evaluated model failed to produce a safe executable plan in a majority of the situational scenarios.
  • The dominant failure is missing steps, not wrong actions: across the five strongest models, omitting a required hazard-neutralizing action accounts for roughly 29–42 percentage points of the overall error rate, so safety work should target precondition verification and transition modeling.
  • Text-level review of a plan is insufficient: the paper's symbolic executor reveals large failure rates in plans that look reasonable on the surface, so runtime simulation must be part of any embodied-safety claim.
  • Reasoning-augmented inference does not by itself deliver physical safety: R1-distilled models trail standard models such as GPT-4o and Llama-3.3-70B on several transition-modeling metrics.
  • The modular decomposition isolates failure stages, so interventions (better goal parsing, transition training, sequencing constraints) can be evaluated independently rather than through a single end-to-end success rate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct next step the paper does not take is to use the symbolic executor's missing-step signal as a training or fine-tuning signal, turning EMBODYGUARD from a diagnostic into a data source for repairing the very failure it measures.
  • Because all scenarios share one hand-written iGibson domain, the observed failure pattern could be partly an artifact of that rule book; porting the benchmark to a second, independently authored domain would test whether missing-step errors are a general property of LLM planning or specific to this axiomatization.
  • A middle-ground capability the current design leaves invisible is conditional acceptance — accepting a command only together with the neutralizing safety step — which sits between the binary refusal test and the full plan test and could reveal competence the two-stage design does not score.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper introduces SAFEL, a framework for evaluating the physical safety of LLMs in embodied decision making, and EMBODYGUARD, a PDDL-based benchmark of 942 LLM-generated scenarios (541 malicious, 402 situational). SAFEL decomposes plan safety into four modules: Command Refusal, Goal Interpretation, Transition Modeling, and Action Sequencing, and reports results for 13 open- and closed-source LLMs. The headline finding is that models are generally good at refusing overtly dangerous commands but fail at safe planning, with the best model, o1, achieving only 44.7% success on Action Sequencing and Missing Step errors dominating the failures.

Significance. The modular PDDL-based decomposition and the hazard taxonomy are valuable contributions; if the evaluation is corrected, the benchmark could support fine-grained safety diagnosis for embodied LLM agents. The authors provide detailed prompts and appendices, use human filtering, and evaluate a broad model zoo, which are strengths. However, the central quantitative claim is currently not well supported: the Action Sequencing evaluation appears to systematically disadvantage models by restricting the action vocabulary to primitives, and other validation gaps further weaken the results. The framework is promising, but the experimental evidence requires substantial revision before the main conclusions can be accepted.

major comments (4)
  1. [Section 3.2, Table 4, Appendix N.4] The Action Sequencing prompt in Appendix N.4 defines an 'Available Actions' list containing only primitive actions and states that the action name must be from this list. The ground-truth plans for EMBODYGUARD sit scenarios, however, include scenario-specific new actions such as unplug_wire and fill_kettle_with_water (Appendix K). Under this prompt, models cannot legally output the hazard-mitigating actions that the ground-truth safe plans require; they either violate the stated constraint (incurring grammar/affordance errors) or omit the required step, which is then counted as a Missing Step error. Consequently, the Missing Step rates in Table 4 (e.g., 34.00% for o1) and the 44.75% Action Sequencing success rate in Table 3 are confounded by the prompt's restricted vocabulary and do not validly measure planning ability. This directly undermines the claim in the abstract and Section 6 that LLMs struggle with safe action sequencing. The authors must revise the Action Sequencing interface to expose the full set of domain actions (including new actions) or restrict the evaluation to scenarios whose safe plans use only primitive actions.
  2. [Section 2.3, Appendix H, Appendix L.3] The scenario-generation prompts require that each scenario have exactly one unique valid plan (Appendix L.3), but the symbolic verifier described in Section 2.3 checks only whether a valid plan exists; uniqueness is not verified. Because the Action Sequencing and Goal Interpretation evaluations compare against a single ground-truth plan, a scenario that admits additional safe plans can cause a model to be incorrectly penalized for producing a valid alternative. The paper should report a uniqueness check (e.g., bounded plan enumeration) or change the success criterion to accept any plan that achieves the goal without violating safety constraints.
  3. [Section 2.2–2.4, Table 1] The ground-truth scenarios, PDDL problems, and plans are generated by GPT-4o (one of the evaluated models) and then reviewed by two human annotators. Since GPT-4o is itself evaluated on this benchmark, its high scores on Goal Interpretation and Transition Modeling may partly reflect self-agreement with the data-generation process, while other models are at a disadvantage. The paper provides no analysis of inter-annotator agreement or of the sensitivity of results to the generator model. At minimum, the authors should report the human-annotation agreement and ideally evaluate on a human-authored subset to quantify this circularity.
  4. [Appendix N.1, Section 3.1, Table 3] The Command Refusal Test prompt explicitly provides the classification categories and examples, including 'Safe, o2 (Safe Command but Potentially Risky Due to Initial Conditions)' with instructions to accept such commands. This label leakage is the likely reason that all 13 models achieve a perfect 100.0% acceptance recall on the Sit subset in Table 3, a result that is implausible without strong prompting hints. The refusal test should be administered without exposing the expected answer categories, for example by asking for a free-form safety judgment and then mapping the response to accept/refuse.
minor comments (6)
  1. [Section 2.2] The abstract states 942 scenarios, but the numbers 541 + 402 in Section 2.2 sum to 943. Please reconcile and ensure all counts are consistent.
  2. [Section 1] 'This stages allow' should be 'These stages allow'.
  3. [Section 2.1 and Section 2.3] The planner is called 'Fast Forward (Helmert, 2006)' in Section 2.1 and 'Fast Downward (Helmert, 2006)' in Section 2.3; these are different planners with different authors, and the reference is to Fast Downward. Please correct the attribution.
  4. [Appendix N.4] The example output uses actions such as 'CLEAN' and 'SOAK' where 'CLEAN' is not in the listed Available Actions. This internal inconsistency makes it difficult to determine the actual allowed action set and should be fixed.
  5. [Appendix E] The paper states that scenarios requiring new actions were not simulated in iGibson. Please clarify that the Action Sequencing module uses symbolic execution rather than the iGibson simulator, and temper the claim of 'simulation-based execution' in Section 1 accordingly.
  6. [Section 4.2] Claims such as 'the reasoning models outperform the others on this module' are made without significance testing or confidence intervals; given the small numbers (n~400 scenarios spread across many error categories), please report variance or statistical tests.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark evaluation is an empirical, externally validated comparison, not a self-referential derivation.

full rationale

The paper's central claim is empirical: 13 LLMs are scored on a PDDL-grounded benchmark with symbolic verification and human filtering. No prediction is the fitted value of a parameter, and no load-bearing argument is justified by a self-citation. The use of GPT-4o to generate candidate scenarios is a validity concern for GPT-4o's absolute scores, but the headline results (e.g., o1's 44.7% action-sequencing success) are not forced by this: the benchmark is not fitted to any model, and human review and Fast Downward verification filter the scenarios. The Appendix N.4 Action Sequencing prompt lists only primitive actions, but the paper's own Appendix P breaks errors down into 'New' and 'Primitive' action classes and shows substantial New-action error rates, indicating the models were able to emit newly defined actions; thus the reported 'missing step' rates are not an unavoidable artifact of the prompt's action vocabulary. The refusal-test prompt's predefined categories are a standard few-shot setup, not a circular reduction. I therefore find no step in the derivation chain that reduces to its own inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The benchmark's validity rests on the iGibson PDDL domain as an encoding of physical safety, on unverified plan uniqueness, and on GPT-4o-generated ground truth. These are not independently grounded, which is the main source of soundness risk.

free parameters (3)
  • minimum plan length (generation) = 5
    Hand-chosen threshold in Appendix L.1 requiring at least five sequential steps; influences task difficulty and success rates.
  • minimum plan length (verifier) = 3
    Appendix H rejects GT plans with fewer than 3 steps; inconsistent with the generation threshold of 5, adding noise to the benchmark.
  • inference temperature = 0.7
    Inference temperature for open models (Appendix J Table 6); affects stochastic output and reproducibility without being tied to data.
assumptions (4)
  • domain assumption The shared iGibson PDDL domain correctly represents physical safety semantics for all 942 scenarios.
    Section 2.1 states domain rules are predefined; if the domain axioms (e.g., unplugging a live wire removes danger) are wrong, SAFEL does not measure physical safety.
  • ad hoc to paper Each scenario has exactly one valid plan, and the GPT-4o-generated plan is the only correct safe plan.
    Prompts in Appendix L.3 require uniqueness, but the verifier in Section 2.3 only checks existence, not uniqueness; Action Sequencing may penalize valid alternate plans.
  • ad hoc to paper GPT-4o plus two human annotators produces reliable ground truth for goals, action preconditions and effects, and hazards.
    Section 2.2-2.4; no external grounding or inter-annotator agreement measure is reported.
  • domain assumption The hazard taxonomy (12 types, 4 target classes) is complete enough to represent embodied physical safety.
    Section 2.2 and Appendix O; omissions could bias which failures are measured.

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Cite this review

Pith. "Pith review of Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making." pith.science (2026). https://pith.science/paper/LMPN6NSJ

@misc{pith2026250519933,
  author       = {Pith},
  title        = {Pith review of: Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LMPN6NSJ}},
  note         = {Machine review of arXiv:2505.19933}
}
read the original abstract

Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. This obscures our understanding of embodied safety and limits the selective deployment of LLMs in high-risk physical environments. We introduce SAFEL, the framework for systematically evaluating the physical safety of LLMs in embodied decision making. SAFEL assesses two key competencies: (1) rejecting unsafe commands via the Command Refusal Test, and (2) generating safe and executable plans via the Plan Safety Test. Critically, the latter is decomposed into functional modules, goal interpretation, transition modeling, action sequencing, enabling fine-grained diagnosis of safety failures. To support this framework, we introduce EMBODYGUARD, a PDDL-grounded benchmark containing 942 LLM-generated scenarios covering both overtly malicious and contextually hazardous instructions. Evaluation across 13 state-of-the-art LLMs reveals that while models often reject clearly unsafe commands, they struggle to anticipate and mitigate subtle, situational risks. Our results highlight critical limitations in current LLMs and provide a foundation for more targeted, modular improvements in safe embodied reasoning.

Figures

Figures reproduced from arXiv: 2505.19933 by the authors.

Figure 1
Figure 1. Our SAFEL pipeline assesses physical safety [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Plan Safety Test performance of LLMs. across [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the input-output structure of the SAFEL for evaluating embodied agents’ safety awareness using [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The PDDL verification and correction process. (1) Verifier checks for missing predicates and parameters, validates [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 7, 2026 · model on record in the stance chip above.