SCHA-React: Staged Causal History-Assisted Refinement for Online Reaction Generation

Online reaction generation requires a policy to produce causal responses from bounded recent interaction context. Yet temporal proximity is not equivalent to interaction relevance: completed interactions outside the current window may remain informative, while directly enlarging the inference context introduces redundant history and additional runtime dependencies. We introduce SCHA-React, a staged causal-history-assisted refinement framework that augments a bounded reaction policy with selected historical evidence during training. Specifically, SCHA-React selects causally completed historical events, compresses them into compact event representations, and adaptively fuses them with recent interaction features during refinement. The shared policy uses conditional Flow Matching for next-latent prediction and an Online Motion Decoder for executable motion generation. We instantiate SCHA-React on DuoBox with 2,525 multimodal-model-assisted, manually verified annotations, including 1,310 admissible guidance events. Across the Reactive, Two-character, and 1,800-frame closed-loop evaluation settings, SCHA-React achieves the lowest reported per-frame, per-transition, and per-clip FIDs among evaluated methods. At inference, the historical pathway is disabled; the policy uses bounded recent motion and maintained latent buffers. Overall, the results support causal historical evidence as a training-time refinement resource that improves the bounded policy without adding inference-time inputs.