Robots Can’t See What They Say: The Hidden Dangers of AI Decision-Making

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Author: Denis Avetisyan

Despite impressive AI benchmarks, new research reveals that robots powered by large language models struggle with basic spatial reasoning, creating potentially hazardous scenarios in real-world applications.

In a simulated fire emergency, large language models prioritize directing individuals toward locations containing important documents (32%) or server rooms (1%) rather than guiding them to safety exits.
In a simulated fire emergency, large language models prioritize directing individuals toward locations containing important documents (32%) or server rooms (1%) rather than guiding them to safety exits.

This review demonstrates critical failures in spatial awareness and safety-critical decision-making within current Large Language Models and Vision-Language Models.

Despite increasingly impressive benchmark scores, the deployment of Large Language Models (LLMs) in safety-critical robotics remains fraught with hidden risk. This is the central concern of ‘Safety Not Found (404): Hidden Risks of LLM-Based Robotics Decision Making’, which systematically evaluates LLM and Vision-Language Model performance in spatial reasoning tasks relevant to real-world safety. Our findings reveal that even state-of-the-art models exhibit unreliable behavior-including directing robots toward hazards-demonstrating a critical disconnect between aggregate accuracy and acceptable risk in dynamic environments. Given that a single failure can have catastrophic consequences, can we truly afford to rely on current LLMs for autonomous decision-making in safety-critical systems?

The Illusion of Completeness

Contemporary artificial intelligence systems demonstrate remarkable proficiency when provided with comprehensive datasets, routinely achieving high accuracy in controlled laboratory settings. However, this performance often diminishes significantly when these models encounter the ambiguities and gaps inherent in real-world data. Unlike the curated datasets used in training, practical observations are frequently noisy, incomplete, or subject to interpretation. This discrepancy presents a fundamental challenge, as the reliance on perfect information limits the ability of current AI to generalize effectively to dynamic environments where unforeseen circumstances and missing data are the norm. Consequently, a key focus of ongoing research involves developing techniques to enhance robustness and enable AI to reason effectively even when faced with uncertainty and imperfect observations, mirroring the adaptive capabilities of biological intelligence.

The demand for flawless data presents a substantial obstacle to applying artificial intelligence in real-world scenarios. Current AI systems are frequently trained on meticulously curated datasets, assuming complete and accurate information-a condition rarely met outside controlled laboratory settings. This creates a performance drop when these models encounter the inherent messiness of dynamic environments, where observations are often partial, ambiguous, or subject to noise. Consequently, a system capable of exceptional performance with perfect data may falter dramatically when deployed in situations involving missing information, sensor errors, or rapidly changing conditions. Bridging this gap requires a shift towards AI that doesn’t simply require complete information, but can effectively reason and make robust decisions despite its absence, unlocking the potential for truly adaptable and reliable intelligent systems.

Assessing artificial intelligence systems on challenges that demand reasoning with incomplete or ambiguous data is becoming increasingly vital for practical application. Current benchmarks frequently prioritize performance on well-defined tasks with complete datasets, failing to reflect the messy realities of real-world scenarios. Consequently, a shift towards evaluation metrics that specifically measure a model’s ability to infer, extrapolate, and make sound judgments despite uncertainty is necessary. These tests should not simply assess accuracy, but also the confidence levels associated with predictions, and the system’s ability to identify and manage its own limitations. Bridging the gap between laboratory performance and real-world robustness hinges on developing these more nuanced and rigorous evaluation protocols, ultimately fostering AI that is not only intelligent, but also reliable and adaptable in unpredictable conditions.

Conventional algorithms, often meticulously trained on complete datasets, exhibit a pronounced vulnerability when confronted with the realities of imperfect data. As the quality or quantity of information diminishes – through sensor noise, missing values, or inherent ambiguity – their performance tends to degrade rapidly, leading to unreliable outputs and potentially critical errors. This fragility stems from a reliance on precise inputs and a limited capacity to extrapolate or infer meaning from incomplete signals. Unlike human cognition, which adeptly handles uncertainty and fills in gaps with contextual understanding, these systems frequently falter when faced with the nuances of real-world observations, highlighting a significant limitation in their adaptability and robustness.

Navigating the Unknown: Incomplete Information Tasks

The Incomplete Information Task utilizes ‘ASCII Map’ representations to provide a standardized and controlled environment for evaluating a model’s performance when faced with uncertainty. These maps present a grid-based world where certain cells are masked or hidden, forcing the model to infer information about the environment based on incomplete data. The task assesses the model’s ability to navigate and complete objectives – such as pathfinding or target identification – despite the lack of complete environmental knowledge. By systematically varying the degree and pattern of missing information within the ASCII maps, researchers can quantitatively measure a model’s robustness and decision-making processes under uncertainty, independent of visual processing complexities.

Sequence Masking and Sequence Validation represent specific challenges within incomplete information tasks designed to evaluate model performance with data scarcity. Sequence Masking involves presenting models with input sequences where portions are intentionally hidden, requiring the model to predict or infer the missing elements based on the available context. Sequence Validation, conversely, presents models with completed sequences and asks them to assess the validity or correctness of the provided completion, necessitating inferential reasoning to determine if the sequence logically follows from the available information. Both variants force models to move beyond rote memorization and demonstrate abilities in data imputation and logical deduction, providing quantifiable metrics for assessing robustness in uncertain environments.

Uncertain Terrain Map scenarios assess a model’s decision-making capabilities when faced with incomplete spatial data. These scenarios present models with map representations where portions of the terrain are obscured or represented with probabilistic information, forcing the model to infer the likely layout and navigate accordingly. Evaluation metrics focus on the model’s ability to successfully complete navigational tasks – such as pathfinding to a designated goal – while quantifying the confidence levels associated with its chosen route. Performance is judged not only on task completion but also on the model’s ability to avoid areas flagged as potentially hazardous, demonstrating a nuanced understanding of risk assessment under conditions of ambiguity.

Traditional pattern recognition tasks primarily assess a model’s ability to identify known configurations. In contrast, the Incomplete Information Tasks necessitate a more advanced form of spatial understanding. These tasks require models to not only process explicitly presented data – the visible portions of the ‘ASCII Map’ – but also to infer characteristics of the obscured or missing areas. This inferential step moves beyond simple matching of input to output, demanding that the model construct an internal representation of the environment and utilize that representation to guide decision-making, even with incomplete or ambiguous sensory input. The ability to extrapolate from partial data and reason about unobserved regions is indicative of a more robust and generalized spatial competence.

Experiments utilized prompts-varying by task ([blue] Complete, [red] Incomplete, [yellow] SOSR)-and both ASCII and sequence maps, with the SOSR task differentiating difficulty using [red] highlighted phrases and relying on [italicized] text for context, as fully detailed in the Appendix for the 'back of the building' scenario.
Experiments utilized prompts-varying by task ([blue] Complete, [red] Incomplete, [yellow] SOSR)-and both ASCII and sequence maps, with the SOSR task differentiating difficulty using [red] highlighted phrases and relying on [italicized] text for context, as fully detailed in the Appendix for the ‘back of the building’ scenario.

Simulating Crisis: Safety-Oriented Spatial Reasoning

The Safety-Oriented Spatial Reasoning (SOSR) Task assesses an agent’s navigational capabilities within complex, dynamic environments designed to simulate emergency situations. These scenarios, such as a fire evacuation, necessitate more than simple pathfinding; models must make real-time decisions while accounting for evolving hazards and time constraints. The task introduces pressure through simulated consequences for incorrect actions, forcing the agent to prioritize safe routes and avoid dangerous areas. This contrasts with standard navigation tasks that focus solely on reaching a goal, and requires a model to balance optimality with safety considerations during the planning and execution of a path.

The Safety-Oriented Spatial Reasoning (SOSR) task extends beyond simple pathfinding to evaluate a model’s capacity for hazard avoidance and safe decision-making within a simulated environment. Specifically, scenarios such as fire evacuations present dynamic obstacles and require the model to not only determine a navigable route, but also to prioritize paths that minimize exposure to dangerous elements. Evaluation focuses on whether the model consistently selects routes that avoid hazards, even if those routes are sub-optimal in terms of distance or time. This capability is critical as success is not solely defined by reaching a goal, but by achieving it safely, and current model performance, with success rates of 30-40% in challenging scenarios, indicates a significant deficiency in this area.

The Direction-Sense Test is a component of the Safety-Oriented Spatial Reasoning (SOSR) task designed to evaluate a model’s ability to maintain spatial awareness during navigation. This test specifically assesses whether the model accurately tracks its facing direction and position relative to the environment. Failure in this test indicates an inability to reliably interpret spatial information, which is crucial for safe path planning and hazard avoidance. Consistent performance below acceptable thresholds on the Direction-Sense Test correlates with decreased success rates in more complex scenarios, such as the Fire Evacuation Scenario, highlighting the fundamental importance of accurate orientation for safe navigation.

Success Rate, as a quantifiable metric within the Safety-Oriented Spatial Reasoning (SOSR) task, indicates a significant deficiency in current models’ ability to make safe navigational decisions. Data presented in Table 1 demonstrates that in challenging SOSR scenarios – those incorporating elements requiring hazard avoidance and prioritization of safety – model performance drops to approximately 30-40%. This represents a substantial decrease from the 90-100% success rate achieved on easy, deterministic maps, and even a decline from the 50-70% rate on hard deterministic maps, highlighting a specific weakness in safe path planning and execution under pressure.

Model performance on spatial reasoning tasks exhibits a significant correlation with map complexity. While models consistently achieve high success rates – approximately 90-100% – on easy, deterministic maps, performance decreases substantially to the 50-70% range when confronted with hard, deterministic maps. This indicates a limitation in the ability of current models to generalize spatial understanding to more challenging environments, even when those environments are fully predictable and lack stochastic elements. The observed drop suggests that increased complexity, even in deterministic settings, poses a considerable obstacle to reliable spatial navigation and decision-making.

Large language models demonstrate varying success rates across map-based and spoken-order-sequence reasoning (SOSR) tasks, as detailed in the table.
Large language models demonstrate varying success rates across map-based and spoken-order-sequence reasoning (SOSR) tasks, as detailed in the table.

The Shadow of Fabrication: Hallucination and Reliable Reasoning

Visual Language Models (VLMs), despite their impressive capabilities, frequently exhibit a phenomenon known as “hallucination,” where they generate information not grounded in the provided input or real-world knowledge. This isn’t simply random error; rather, the model actively constructs details to complete its understanding, even when faced with ambiguity or missing data. The tendency arises from the model’s probabilistic nature – it predicts the most likely continuation of a sequence, and this can lead to plausible, yet entirely fabricated, content. Consequently, a VLM might confidently describe objects or relationships that aren’t present in the image or text, or invent attributes to fill perceived informational voids – a behavior that poses significant challenges to the development of truly trustworthy artificial intelligence systems.

The propensity for visual-language models to fabricate information, known as hallucination, presents acute risks when these systems are deployed in safety-critical contexts. Consider applications like autonomous vehicle navigation or medical diagnosis; an inaccurate prediction – a misidentified object or a misinterpreted scan – can have devastating consequences. Unlike applications where a simple error is merely inconvenient, these scenarios demand unwavering reliability. The potential for hallucinated details to directly impact human safety necessitates rigorous evaluation and the development of mitigation strategies focused on ensuring these models prioritize factual accuracy over plausible, but ultimately incorrect, outputs. This isn’t simply a matter of improving performance metrics; it’s about building trust and preventing potentially catastrophic failures in real-world deployments.

A newly developed evaluation framework rigorously assesses the propensity of large language models to “hallucinate”-that is, to generate information not grounded in provided data-specifically when encountering incomplete inputs. This framework doesn’t simply measure if a model hallucinates, but quantifies the extent to which it does so under conditions of informational scarcity. Through a series of carefully constructed tasks, researchers demonstrated that even state-of-the-art visual-language models frequently fabricate details to complete narratives or answer questions, revealing a significant vulnerability when dealing with ambiguity. The results highlight that hallucination isn’t a random error, but a systematic behavior increasing proportionally with the degree of missing information, demanding new strategies for improving the reliability and trustworthiness of AI systems deployed in real-world applications.

The meticulous analysis of visual language models’ responses to incomplete information yields actionable intelligence for building more robust artificial intelligence. By pinpointing the specific scenarios that trigger fabricated content – or ‘hallucinations’ – researchers can directly address these weaknesses during model training and refinement. This process involves not simply increasing the volume of training data, but strategically curating datasets that emphasize reasoning under uncertainty and encourage models to acknowledge knowledge gaps rather than invent plausible-sounding but inaccurate details. Consequently, the insights derived from these evaluations are crucial for transitioning AI systems from impressive demonstrations to dependable tools, particularly in domains where factual accuracy and reliable decision-making are paramount, fostering greater user trust and responsible deployment.

The study reveals a fundamental tension between reported performance and demonstrable safety in LLM-driven robotics. Current evaluation metrics, while seemingly positive, fail to capture the propensity for spatial reasoning failures-hallucinations, as the authors term them-that could prove catastrophic in real-world applications. This aligns with Ken Thompson’s observation: “The best programs are small and simple.” The pursuit of increasingly complex models, as seen with LLMs, risks obscuring the core requirement of reliability. The work underscores that true progress isn’t measured by added features, but by the elimination of potential failure points, demanding a return to foundational principles of robust design.

The Road Ahead

Benchmarks offer comfort. They rarely offer truth. This work reveals a familiar failing: high scores do not equate to reliable action, especially when spatial reasoning underpins safety. The illusion of competence is a dangerous artifact. Abstractions age, principles don’t.

Future efforts must shift focus. The pursuit of ever-larger models feels increasingly… circular. True progress lies in verifiable robustness, not raw performance. A system’s limitations deserve scrutiny equivalent to its successes. Every complexity needs an alibi.

The challenge isn’t simply improving spatial awareness. It’s building systems that know what they don’t know. Honest uncertainty is preferable to confident error. The field needs less boasting, more accounting.

Original article: https://arxiv.org/pdf/2601.05529.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-12 20:55