Chandan K. Reddy

Recent advances in large reasoning models (LRMs) have enabled remarkable performance by generating long Chain-of-Thought (CoT) reasoning traces. In this paper, we identify and systematically analyze a critical vulnerability we term reasoning distraction, where LRMs are diverted from their primary objective by irrelevant yet complex tasks embedded in the prompt. While such attacks can be instantiated via prompt injection, we show that they expose a distinct reasoning-level failure mode that is not captured by existing work, which primarily focuses on output manipulation rather than corruption of intermediate reasoning. Reasoning distraction can induce covert compliance, where the model silently follows adversarial objectives within its hidden reasoning trace while maintaining well-formed, seemingly correct outputs. This poses a significant risk to automated agentic pipelines that rely on final outputs without access to internal reasoning. We introduce DistractBench, a comprehensive benchmark for evaluating robustness to reasoning distraction, and show that even state-of-the-art LRMs are highly vulnerable, with accuracy drops of up to 60%. To mitigate this vulnerability, we propose a training-based defense using Supervised Fine-Tuning (SFT) and preference optimization on Distract-SFT and Distract-DPO, two synthetically generated datasets designed to improve robustness to reasoning-level corruption, achieving gains of over 50 points. Together, these components form the Distraction Suite, a standardized toolkit for evaluating and improving reasoning robustness in LRMs.

Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex reasoning tasks through specialized reasoning-based fine-tuning procedures. While these methods reliably improve pass@1 accuracy, prior works have observed that they face with a coverage shrinkage phenomenon, where pass@k degrades relative to the base model. In this paper, we investigate the reasoning shrinkage arise under SFT-based post-training. We hypothesize that this behavior is driven by properties of the fine-tuning data, specifically related to decision points or ``forks in the road'' scenarios where model faces with indecipherable patterns with multiple valid reasoning paths. To test this hypothesis, we design controlled case studies that simulate such decision-point settings, spanning indecipherable nodes in graph branching, and reasoning styles to solve a problem. By tracking post-training dynamics in these settings, we find that the shrinkage phenomenon is tightly correlated with the prevalence of decision-point scenarios in the training data. We also demonstrate that this shrinkage behavior can be partially mitigated through targeted data synthesis design of decision-points, and a more systematic diversity-encouraging decoding mechanism. Our findings identify data-centric factors as a key driver of shrinkage in reasoning models and highlight diversity-aware designs as an effective lever for controlling it.

Multi-turn Text-to-SQL aims to translate a user’s conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, most existing systems only regard this task as a simple text translation task and follow a short-horizon paradigm, gener...

When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is crit...