Dung Huu Nguyen
Author directory2026
Tracing Error Propagation in Reasoning Language Models
Dung Huu Nguyen | Quan Pham | Dat Quoc Nguyen
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Dung Huu Nguyen | Quan Pham | Dat Quoc Nguyen
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Reasoning language models (RLMs) increasingly tackle complex problems through long reasoning traces, but identifying when and where failures arise remains difficult. In this work, we study whether reasoning errors are signaled in model representations before they manifest as failed traces. We first show that step-level errors are locally predictable within a short window of neighboring steps, suggesting that models encode early warning signals. However, trace-level failures often result not from isolated errors, but from persistent ones originating earlier in the trace. To study this, we distinguish *recoverable* from *unrecoverable* traces via counterfactual resampling. We find that unrecoverable traces induce final-step representations that are clearly separable from those of recoverable traces. Moreover, the persistent error can be retrospectively decoded from the final-step representation, indicating that error signals propagate and accumulate through the reasoning trace.