arXiv:2603.29025v3 Announce Type: replace Abstract: Large language models fail when a salient surface cue conflicts with an unstated feasibility constraint. We introduce the Heuristic Override Benchmark (HOB): 500 instances spanning 4 heuristic families and 5 constraint families, with minimal pairs and explicitness gradients. We pair HOB with a falsifiable behavioral characterization following a diagnose-measure-bridge-treat arc. Causal-behavioral analysis of the car wash problem across six models reveals context-independent sigmoid heuristics: the distance cue has 8.7 to 38 times more influen
This research provides a new benchmark and behavioral characterization for understanding a critical limitation of large language models, indicating a maturing field focused on diagnostic tools.
Understanding and addressing the 'surface cue override' problem in LLMs is crucial for developing more reliable and trustworthy AI agents and systems, particularly in sensitive applications.
The introduction of the Heuristic Override Benchmark (HOB) provides a standardized tool to diagnose and potentially mitigate a key failure mode in LLM reasoning, allowing for more targeted development efforts.
- · AI researchers
- · AI safety engineers
- · Companies developing AI agents
- · Untrustworthy AI systems
- · Black-box AI development approaches
New methods will emerge to prevent LLMs from being misled by surface heuristics over implicit constraints.
Improved LLM reliability will accelerate the deployment of autonomous AI agents in various sectors.
More robust AI systems could lead to a re-evaluation of ethical guidelines and regulatory frameworks as AI capabilities advance.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL