Lost in the Noise: How Reasoning Models Fail with Contextual Distractors
Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information. However, this shift introduces input contexts that are inherently noisy, a reality that current sanitized benchmarks fail to capture. We introduce NoisyBench, a comprehensive benchmark that systematically evaluates model robustness across 11 datasets in RAG, reasoning, alignment, and tool-use tasks against diverse noise types, including random documents, irrelevant chat histories, and hard negative distractors. Our evaluation reveals a catastrophic performance drop of up to 80% in state-of-the-art models when faced with contextual distractors. Crucially, we find that agentic workflows often amplify these errors by over-trusting noisy tool outputs, and distractors can trigger emergent misalignment even without adversarial intent. We find that prompting, context engineering, SFT, and outcome-reward only RL fail to ensure robustness; in contrast, our proposed Rationale-Aware Reward (RARE) significantly strengthens resilience by incentivizing the identification of helpful information within noise. Finally, we uncover an inverse scaling trend where increased test-time computation leads to worse performance in noisy settings and demonstrate via attention visualization that models disproportionately focus on distractor tokens, providing vital insights for building the next generation of robust, reasoning-capable agents.
6965bbbcfc8c4ecc02c7f907
Seongyun Lee
claimed_verified
2026-01-13T08:24:04.198Z
6550c4f27bbfce1878f5f280
/avatars/0ecedbcd8a55b2c4abd1da9e741a6652.svg
seongyun_lee
Seongyun
user
Seongyun
6965bbbcfc8c4ecc02c7f908
Yongrae Jo
6965bbbcfc8c4ecc02c7f909
Minju Seo
6965bbbcfc8c4ecc02c7f90a
Moontae Lee
6965bbbcfc8c4ecc02c7f90b
Minjoon Seo
Lost in the Noise: How Reasoning Models Fail with Contextual Distractors
Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information. However, this shift introduces input contexts that are inherently noisy, a reality that current sanitized benchmarks fail to capture. We introduce NoisyBench, a comprehensive benchmark that systematically evaluates model robustness across 11 datasets in RAG, reasoning, alignment, and tool-use tasks against diverse noise types, including random documents, irrelevant chat histories, and hard negative distractors. Our evaluation reveals a catastrophic performance drop of up to 80% in state-of-the-art models when faced with contextual distractors. Crucially, we find that agentic workflows often amplify these errors by over-trusting noisy tool outputs, and distractors can trigger emergent misalignment even without adversarial intent. We find that prompting, context engineering, SFT, and outcome-reward only RL fail to ensure robustness; in contrast, our proposed Rationale-Aware Reward (RARE) significantly strengthens resilience by incentivizing the identification of helpful information within noise. Finally, we uncover an inverse scaling trend where increased test-time computation leads to worse performance in noisy settings and demonstrate via attention visualization that models disproportionately focus on distractor tokens, providing vital insights for building the next generation of robust, reasoning-capable agents.
NoisyBench benchmark reveals significant performance degradation in state-of-the-art models when exposed to noisy contextual information, with agentic workflows amplifying errors and attention mechanisms disproportionately focusing on distractor tokens.
RAG reasoning models agentic AI systems contextual distractors attention visualization reward modeling SFT outcome-reward RL Rationale-Aware Reward inverse scaling trend