Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight

Computer Science > Artificial Intelligence [Submitted on 7 May 2026] Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight Christopher Z. Cui, Taylor W. Killian, Prithviraj Ammanabrolu Reasoning in Large Language Models (LLMs) poses a challenge for oversight as many misaligned behaviors do not surface until reasoning concludes. To address this, we introduce Behavior Cue Reasoning for making LLM reasoning more controllable and monitorable. Behavior Cues are special token sequences that a model is trained to emit immediately before specific implicit and explicit behaviors, acting as dual purpose signal and control levers. When fine-tuning a weaker external monitor with Reinforcement Learning for reasoning oversight, a compressed view of only information surfaced by Behavior Cues is sufficient signal for the monitor to prune up to 50% of otherwise wasted reasoning tokens in complex math problem solving. When leveraged by an almost optimal rule-based monitor in an environment where excessive constraint violations results in failure, \ours allows for the recovery of safe actions from 80% of reasoning traces that would otherwise end with the proposal of an unsafe action, more than doubling the success rate from 46% to 96%. Through evaluation across two model families and three domains, we show that \bcreasoning improves reasoning monitorability and controllability with no cost to performance. More broadly, our work progresses scalable oversight by demonstrating how the monitored model itself can be trained to reason more tractably to oversight. Code to be released at this https URL Comments: Code currently being cleaned, and prepared for public release. This comment will be updated once completed Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.07021 [cs.AI]   (or arXiv:2605.07021v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.07021 Focus to learn more Submission history From: Christopher Cui [view email] [v1] Thu, 7 May 2026 23:05:50 UTC (4,804 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)