Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness
This addresses the problem of unstable reasoning in non-interactive puzzle settings for AI systems, representing a novel method for a known bottleneck.
The paper tackles the problem of logical inertia in large language models by proposing SABA, a reasoning framework that introduces self-awareness of missing premises before final decisions. SABA achieves best performance on all difficulty splits of the non-interactive Detective Puzzle benchmark and maintains leading results on multiple public benchmarks.
Large language models perform well on many reasoning tasks, yet they often lack awareness of whether their current knowledge or reasoning state is complete. In non-interactive puzzle settings, the narrative is fixed and the underlying structure is hidden; once a model forms an early hypothesis under incomplete premises, it can propagate that error throughout the reasoning process, leading to unstable conclusions. To address this issue, we propose SABA, a reasoning framework that explicitly introduces self-awareness of missing premises before making the final decision. SABA formulates reasoning as a recursive process that alternates between structured state construction and obstacle resolution: it first applies Information Fusion to consolidate the narrative into a verifiable base state, and then uses Query-driven Structured Reasoning to identify and resolve missing or underspecified premises by turning them into queries and progressively completing the reasoning state through hypothesis construction and state refinement. Across multiple evaluation metrics, SABA achieves the best performance on all three difficulty splits of the non-interactive Detective Puzzle benchmark, and it also maintains leading results on multiple public benchmarks.