2026
Correcting in Hindsight: Editing Past Key-Value States for Robust LLM Reasoning
ICML 2026poster
Autoregressive Large Language Models (LLMs) often fail in complex reasoning because early-stage errors remain uncorrectable in subsequent steps—a limitation fundamentally rooted in the inherent irreversibility of the Transformer architecture. In this paper, we propose HEdit, a lightweight reasoning …