DMoA is a differentiable multi-agent LLM framework with recurrent context-aware routing and predictive entropy self-supervision that claims SOTA results on 9 benchmarks through elastic agent collaboration.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=
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Step-Video-T2V describes a 30B-parameter text-to-video model with custom Video-VAE, 3D DiT, flow matching, and Video-DPO that claims state-of-the-art results on a new internal benchmark.
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Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models
DMoA is a differentiable multi-agent LLM framework with recurrent context-aware routing and predictive entropy self-supervision that claims SOTA results on 9 benchmarks through elastic agent collaboration.
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Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Step-Video-T2V describes a 30B-parameter text-to-video model with custom Video-VAE, 3D DiT, flow matching, and Video-DPO that claims state-of-the-art results on a new internal benchmark.