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Sage‑A1.5 is a 35B Mixture‑of‑Experts agentic model based on Qwen3.6-35B-A3B from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon A1, Sage‑A1.5 learns not only to solve individual tasks, but…
Language models are increasingly trained against several verifiable rewards at once, such as the correctness and the format of a tool call, or the individual constraints of a complex instruction. Reward-decoupled normalization (GDPO) normalizes each reward separately so that…
MemT-1.0 is a new generation of memory foundation models that makes persistent, dynamically evolving memory an intrinsic part of model computation.