Conflict Steering: Resolving Late-Layer Disagreement Repairs Non-Termination in Greedy Reasoning

Reasoning models are shipped with a warning not to decode them greedily: on a long trace a greedy decoder resolves every recurring near-tie in the same way, so it can loop or wander to the token limit. Sampling breaks such ties at random, but the output then depends on the draw. We show that the model's own late layers supply a deterministic tie-breaker: within a decodable band of the last $M$ layers, an intermediate layer sometimes commits to a token with low entropy and the final layer overrides it. We call these steps fights and resolve them by conflict steering, a spherical interpolation of the final hidden state toward the intermediate one, followed by ordinary hard-token decoding. Conflict steering repairs a failure mode, not a capability: the non-termination of a derivation the model can complete. Over four Qwen3 models spanning 4B to 32B one steered greedy decode wins 189 paired problems and loses 141 against greedy on AIME 2024 to 2026 and three code benchmarks. The gain sits where greedy leaves traces open: at 4B and 8B, where a fifth of AIME traces reach the token limit, it wins 112 and loses 70 and surpasses the accuracy of temperature-0.6 sampling on AIME in three of four serving configurations. On multiple-choice, near-ceiling and beyond-competence controls it predicts no gain and finds none. At 14B and 32B, where under a tenth of the traces are open, one decode gains little on code and, at 14B, stays below the sampled mean on AIME. The steering fallback, conflict steering only on non-terminating greedy traces, never loses in scope (92 wins, no losses over the four models). Under sampling, whose randomness already breaks ties, no training-free latent-reasoning method we evaluate, ours included, improves on plain sampling.