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 models)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 (22)
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.
Paper 1 has higher impact potential due to a novel, counterintuitive empirical finding (better performance with noisier, lower-fidelity observations) in embodied LLM robotics, with both physical and simulated validation and an explanatory mechanism (reduced action loops). It is timely for evaluating LLM agents in closed-loop settings and has broad applicability across robotics, embodied AI, benchmarking, and safety/robustness evaluation. Paper 2 is valuable as a reproducible case study, but its scope is narrower (one formalization attempt) and mainly diagnostic rather than delivering a broadly generalizable new method or capability.
Paper 2 reveals a counterintuitive and broadly important finding—that higher observation fidelity can actually hurt LLM agent performance in embodied tasks, with moderate noise improving success rates by 2.85x. This challenges fundamental assumptions about how LLMs interact with perception systems and has implications across robotics, AI evaluation methodology, and LLM reasoning research. The finding that noise masks reasoning failures rather than enabling robust problem-solving is a critical insight for the growing embodied AI field. Paper 1, while practically useful for e-commerce A/B testing, addresses a narrower application domain with less fundamental scientific contribution.
Paper 1 addresses a major grand challenge in AI by achieving gold-medal-level performance on IMO and IPhO benchmarks. Its unified recipe for test-time scaling and long-horizon reasoning directly contributes to the most active and impactful area of current AI research (LLM reasoning). While Paper 2 offers interesting counterintuitive insights into embodied AI, Paper 1 has vastly broader implications for automated scientific discovery, mathematics, and the fundamental scaling laws of reasoning models.
Paper 1 presents a counterintuitive and novel finding—that higher observation fidelity can hurt embodied LLM performance, and moderate perceptual noise actually improves outcomes by breaking repetitive action loops. This challenges fundamental assumptions about how LLMs function in embodied settings and has broad implications for evaluation methodology, robotics, and understanding LLM reasoning. Paper 2 proposes a useful engineering contribution (training-free memory module with modest benchmark improvements), but the gains are incremental. Paper 1's surprising insights are more likely to reshape thinking across multiple research communities.
Paper 1 reveals a counterintuitive and broadly important finding: embodied LLM agents perform better with noisier observations, challenging fundamental assumptions about perception quality in AI systems. This insight—that measured performance reflects interactions between perceptual errors and reasoning failures—has deep implications for how the entire community evaluates and designs LLM-based agents. Paper 2, while technically solid, is more incremental in its contribution to scene synthesis pipelines. Paper 1's finding is more surprising, generalizable across domains, and likely to influence evaluation methodology and system design philosophy broadly.
Paper 2 presents a comprehensive system for autonomous scientific discovery, a highly relevant and transformative area of AI. Its potential to accelerate research across all scientific domains, combined with robust methodological features like self-healing execution and human-AI collaboration, gives it a massive breadth of impact. While Paper 1 offers valuable and counterintuitive insights into embodied LLM behavior, its scope is primarily limited to robotics and AI evaluation, making Paper 2's cross-disciplinary potential significantly higher.
Paper 1 presents a novel, counterintuitive empirical finding—that higher observation fidelity actually hurts LLM performance in embodied tasks, and that moderate perceptual noise can improve success rates by reducing repetitive loops. This challenges fundamental assumptions in embodied AI and has broad implications for how we design and evaluate LLM-based robotic systems. Paper 2 is a well-executed systematic survey identifying reproducibility gaps in LLM trading agents, but its contribution is primarily diagnostic/methodological rather than introducing new scientific insights. Paper 1's surprising mechanistic finding is more likely to inspire follow-up research across robotics, cognitive science, and AI evaluation.
Paper 1 reveals a counterintuitive and broadly important finding: embodied LLM agents perform better with noisier observations, challenging fundamental assumptions about perception-action loops in AI. This has significant implications for how the community evaluates and deploys LLMs in robotics, questioning standard benchmarking practices. The finding that noise reduces repetitive action loops provides mechanistic insight. Paper 2, while technically sound, is more incremental—applying generative models as intermediary translators for EEG-to-MLLM alignment. Paper 1's findings are more likely to reshape evaluation methodologies across embodied AI.
Paper 1 offers a more novel and broadly relevant empirical finding: increasing observation fidelity can degrade embodied LLM performance, and controlled noise can improve success by reducing behavioral loops. It combines real-robot experiments with targeted simulation ablations, yielding a mechanistic hypothesis with implications for evaluation methodology, robustness, and agent design across robotics and embodied AI. Paper 2 addresses an important AV-planning gap, but reports largely null quantitative gains and relies on qualitative effects, which may limit immediate impact despite proposing an initial benchmark.
Paper 2 proposes a foundational architectural advancement for neural reasoning systems, moving beyond standard autoregressive generation to probabilistic multi-trajectory latent recursive reasoning. This addresses a critical bottleneck in AI scaling and reasoning capabilities, offering broad applicability across NLP and general AI. In contrast, Paper 1 provides valuable empirical insights into embodied LLM evaluation, but its impact is more confined to the specific niche of robotics and behavioral analysis. The general methodological shift proposed in Paper 2 has a much higher potential for widespread adoption and transformative impact across the broader AI community.
Paper 2 has higher likely impact: it introduces a broadly applicable algorithmic improvement (DMPO) with a principled objective shift (forward-KL approximation) addressing a known, general RL failure mode (mode collapse). It demonstrates consistent gains across multiple benchmarks/modalities and out-of-domain tasks, suggesting reusable methodology for training reasoning models. Paper 1 offers an interesting empirical finding about perception-noise effects in embodied LLM agents, but its scope is narrower and may be more diagnostic than enabling. DMPO is more likely to be adopted and to influence subsequent methods.
Paper 1 presents a highly counterintuitive empirical result in the rapidly expanding field of embodied AI, showing that moderate noise dramatically improves LLM agent performance. This challenges existing evaluation paradigms and has immediate implications for designing LLM-based robotic systems. While Paper 2 offers a valuable bridge to behavioral economics, Paper 1 addresses a critical bottleneck in a highly active area, likely sparking significant follow-up research and methodological shifts in how embodied LLMs are evaluated.