AI RESEARCH
Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Environments
arXiv CS.LG
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ArXi:2510.04142v2 Announce Type: replace-cross This paper identifies a critical yet underexplored challenge in reasoning alignment from multiple multi-modal large language models (MLLMs): In non-stationary environments, the diverse reasoning distributions of source models often evolve unpredictably, transmitting systematic biases and drift to the target model. To address this, we formulate multi-source reasoning alignment as a constraint satisfaction problem under concept drift theory.