Adaptive strategic conflicts unfold through repeated, history-dependent interactions that conventional equilibrium analysis captures only imperfectly. We present a multi-agent reinforcement learning (MARL) phase-diagram methodology that maps structural parameter spaces to stable outcome regimes and coordination-sensiti...
Nikhil Muthukumar, Micah Chiang, Jeffrey Chen et al.· IEEE Conference on Computati...· 0 citations
Koa-action is a framework for low-latency atomic actions -- fast, single-step decisions such as classification, semantic endpointing, Boolean checks, and scoring -- formulated as constrained generation with single-token outputs by introducing atomic label tokens and applying supervised fine-tuning.
Sheng-Hong Dai, S. Pentyala, Ying-Chi Liu et al.· 0 citations
We present Salesforce Koa, an enterprise language model built by post-training the open-weight Nemotron-3-Super-120B foundation model with reinforcement learning using Group Relative Policy Optimization (GRPO). Salesforce Koa is trained on public and synthetically generated data, with no customer data, to improve tool...
Zi-Xiang Chen, Su-Feng Niu, Ying-Chieh Liu et al.· 0 citations
DarwinX is introduced, which treats self-evolution as selection over a population of harnesses with the model frozen: a preserve-and-extend contract admits only variants that extend coverage without regressing, an archive keeps alternative lineages for recombination, and failure-, teacher-, and self-derived evidence sh...
Yifang Zhang, Yutong Dai, Juntao Tan et al.· 2 citations· ⚡1
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