This assessment is based on version 1 of this paper. Version 2 is now available on arXiv — the authors may have revised their methods, results, or conclusions.
Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving
Oussama Zenkri, Oliver Brock
Abstract
Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain success or failure in closed-loop embodied tasks. Following an empirical AI methodology, we study embodied LLM agents behaviorally by varying the information available to the agent and measuring the resulting changes in behavior. Using the Lockbox, a sequential mechanical puzzle with hidden interdependencies, we evaluate LLMs across RGB, RGB-D, and ground-truth symbolic observations in a physical robotic setup and use controlled simulation to probe the resulting behavior. Counterintuitively, agents perform best under raw RGB input and worst under perfect ground-truth observations. In simulation, we probe this effect by randomly flipping perceived action outcomes and find that moderate noise improves performance, peaking at a 40% flip probability with a 2.85-fold success rate increase over the noise-free baseline. Further analysis links this gain to a reduction in repetitive action loops. These findings suggest that success rates alone are insufficient for evaluating LLMs, as measured performance may reflect the interaction between perceptual errors and reasoning failures rather than robust problem solving.
AI Impact Assessments
(1 model)Scientific Impact Assessment
1. Core Contribution
The paper presents an empirical investigation into a counterintuitive phenomenon: embodied LLM agents performing *worse* when given higher-fidelity observations (ground-truth symbolic state) compared to noisier inputs (RGB images) on a sequential mechanical puzzle task. The authors adopt a behavioral probing methodology—treating LLMs as opaque systems and varying inputs to observe behavioral changes—to study this effect. Through controlled simulation, they demonstrate that moderate noise (40% state-flip probability) yields a 2.85× improvement in success rate over noise-free baselines, and they link this improvement to a reduction in repetitive action loops.
The central insight—that perceptual noise can beneficially disrupt degenerate reasoning patterns in LLMs—is the paper's most valuable contribution. The implication that success rate metrics alone are insufficient for evaluating embodied LLM agents, as measured performance may conflate perceptual errors with reasoning failures, is a methodologically important point for the field.
2. Methodological Rigor
The experimental design has both strengths and notable limitations. The physical experiments use only 10 trials per modality with a single LLM (GPT o1), a single Lockbox configuration, and a 20-step budget. This is a small sample size, and statistical power is limited. The authors acknowledge these constraints but do not provide confidence intervals or statistical tests for the physical experiments.
The simulation experiments (210 trials total across noise levels using GPT-4o) offer better statistical grounding, and the polynomial fit with AIC/cross-validation for model selection is appropriate. However, switching from GPT o1 (physical) to GPT-4o (simulation) introduces a confound—the authors correctly note the simulation results should be interpreted as evidence for a "plausible mechanism" rather than a direct explanation, but this gap weakens the overall narrative.
The action loop analysis is clever but somewhat post-hoc. The definition of action loops (subsequences of length ≥3 appearing ≥2 times, solved via ILP) is reasonable, and the negative correlation (r = -0.69) between loop probability and success rate is suggestive. However, correlation is shown without controlling for confounds, and the causal claim that noise disrupts loops which then improves performance remains speculative.
The use of a single Lockbox layout, single LLM provider, and absence of comparison with other reasoning models (e.g., Claude, Gemini, open-source alternatives) limits generalizability substantially. The authors acknowledge these limitations transparently.
3. Potential Impact
The finding that noise can improve LLM performance in closed-loop embodied tasks has several implications:
However, the practical impact is limited by the narrow experimental scope. The Lockbox, while well-motivated as a diagnostic task from cognitive science, is a constrained domain. Whether these findings transfer to more complex, open-ended robotic tasks remains entirely open.
4. Timeliness & Relevance
The paper is highly timely. The deployment of LLMs as cognitive components in robotic systems is accelerating, and there is genuine need for better understanding of when and why these systems succeed or fail. The observation that standard benchmarking practices (success rate reporting) may be misleading is particularly relevant as the field scales up embodied LLM evaluations. The behavioral probing approach—inspired by system identification and empirical AI methodology—offers a principled alternative to purely outcome-based evaluation.
5. Strengths & Limitations
Strengths:
Limitations:
Additional Observations
The paper draws an interesting parallel to stochastic resonance phenomena in biological and physical systems, though this connection is not made explicit. The finding that noise can be beneficial is reminiscent of exploration-exploitation tradeoffs, and framing it this way could strengthen the theoretical contribution. The work would benefit from a more formal treatment of why ground-truth observations specifically exacerbate looping behavior—is it the lack of variability in observations, the format of symbolic state, or something else?
The paper reads more as a workshop-level or short-paper contribution than a full-length study, given the limited experimental scope. However, the core finding is novel enough to warrant attention and further investigation.
Generated May 20, 2026
Comparison History (24)
Paper 1 offers a genuinely counterintuitive, novel scientific finding—that higher observation fidelity can hurt embodied LLM performance and moderate noise improves it—with careful controlled experiments that expose conflation between perceptual errors and reasoning failures. This reframes how we evaluate embodied agents, offering broad conceptual impact. Paper 2 is an impressive engineering framework with strong benchmark gains, but reads as an incremental scaling/system contribution likely tied to specific models and less generalizable insight. Paper 1's surprising, methodologically rigorous result challenges assumptions and is more likely to influence how the field designs and interprets embodied LLM systems.
Paper 2 presents a highly counterintuitive and paradigm-challenging finding: that higher observation fidelity degrades problem-solving in embodied LLMs. This challenges fundamental assumptions in AI evaluation and robotics, offering broad methodological implications for how LLM agents are designed and tested. While Paper 1 provides a strong, domain-specific framework for drug design, Paper 2's insights into the interplay between perceptual errors and reasoning failures have a wider theoretical impact across the rapidly growing field of embodied AI.
While Paper 1 offers a valuable practical tool for LLM debugging, Paper 2 presents a highly counterintuitive and paradigm-challenging scientific finding: that higher observation fidelity hurts embodied LLM problem-solving. By demonstrating that perceptual noise disrupts LLM reasoning failures like repetitive loops, Paper 2 fundamentally challenges current evaluation assumptions in embodied AI. This conceptual disruption is likely to spark significant theoretical debate, broader follow-up research across robotics and LLM reasoning, and a reevaluation of how cognitive architectures are tested, giving it a higher potential scientific impact.
Paper 1 introduces a novel evaluation paradigm (open-world evaluations) for frontier AI systems, addresses a critical gap in AI assessment methodology, and presents a concrete framework (CRUX) with broad implications for AI safety and policy. Its relevance spans the entire AI community and policymakers. Paper 2 presents an interesting counterintuitive finding about observation fidelity in embodied LLMs, but its scope is narrower, focused on a specific puzzle task. Paper 1's timeliness regarding frontier AI governance and its potential to reshape how the field evaluates AI capabilities gives it significantly broader impact.
Paper 2 likely has higher impact: it introduces a broadly applicable, reusable system concept (constant-sized “context map” + cache policy) for long-context agents, with clear efficiency/cost gains and demonstrated generalization across LMs and a production-grade coding agent—strong real-world applicability and timeliness as long-context workflows proliferate. Paper 1 is novel and insightful for embodied evaluation, but appears narrower (specific task/setup) and more diagnostic than enabling; impact may be concentrated in embodied AI methodology rather than across many agent deployments.
Paper 2 presents a counterintuitive and thought-provoking finding—that noisy observations can improve embodied LLM agent performance by breaking repetitive action loops—which challenges fundamental assumptions about observation fidelity in robotics/AI. This insight has broad implications for evaluation methodology, embodied AI system design, and understanding LLM reasoning failures. Its novelty and cross-disciplinary relevance (robotics, cognitive science, AI evaluation) give it higher potential impact. Paper 1, while practically useful, is primarily an engineering contribution (a runtime framework) with more incremental value to the agent tooling ecosystem.
Paper 2 presents a counterintuitive and thought-provoking finding—that perceptual noise can improve embodied LLM performance by disrupting repetitive action loops—which challenges fundamental assumptions about observation fidelity in robotics/AI. This insight has broad implications for how we design and evaluate embodied AI systems. While Paper 1 (OpenComputer) is a solid engineering contribution providing benchmarking infrastructure for computer-use agents, Paper 2 offers deeper scientific insight into LLM reasoning failures, with potential to influence evaluation methodology and agent design across robotics, cognitive science, and AI research more broadly.
Paper 1 is more novel and broadly impactful: it reveals a counterintuitive phenomenon (higher-fidelity/ground-truth observations degrading embodied LLM performance) and systematically probes mechanisms via controlled observation modalities and noise-injection experiments, with an interpretable link to reduced action loops. This has timely implications for embodied AI evaluation, robustness, and agent design across robotics and simulation. Paper 2 is application-relevant, but the contribution is a relatively incremental feature-fusion approach with modest reported gains on a single benchmark, likely narrower in breadth and longer-term methodological impact.
Paper 1 reveals a highly counterintuitive phenomenon in embodied AI—that higher observation fidelity and perfect information can actually degrade LLM problem-solving performance. This challenges prevailing assumptions and evaluation methodologies in the field, likely sparking significant follow-up research into LLM reasoning failures and perceptual interactions. While Paper 2 provides a valuable and rigorous benchmark for agentic workflows, Paper 1 offers a more fundamental conceptual shift with broad implications for robotics and AI safety.
Paper 2 presents a highly counterintuitive finding—that higher observation fidelity degrades embodied LLM problem-solving, and moderate noise actually improves success by breaking repetitive action loops. This fundamentally challenges existing assumptions in the rapidly growing field of Embodied AI, likely prompting significant conceptual shifts and follow-up research. While Paper 1 provides a valuable benchmark for a specific multimodal niche (programmatic video generation), Paper 2's insights into the opaque decision processes and failure modes of LLM agents have broader, paradigm-shifting implications for robotics and autonomous agent design.