DenoiseFlow: Uncertainty-Aware Denoising for Reliable LLM Agentic Workflows
Autonomous agents are increasingly entrusted with complex, long-horizon tasks, ranging from mathematical reasoning to software generation. While agentic workflows facilitate these tasks by decomposing them into multi-step reasoning chains, reliability degrades significantly as the sequence lengthens. Specifically, minor interpretation errors in natural-language instructions tend to compound silently across steps. We term this failure mode accumulated semantic ambiguity. Existing approaches to mitigate this often lack runtime adaptivity, relying instead on static exploration budgets, reactive error recovery, or single-path execution that ignores uncertainty entirely. We formalize the multi...
| Authors | Yandong Yan, Junwei Peng, Shijie Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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