Kimi K2.5: Visual Agentic Intelligence — AI Paper Summary
Kimi K2.5 is an open-source multimodal agentic model that jointly optimizes text and vision through techniques including joint pre-training, zero-vision SF
01 In brief Summary Kimi K2.5 is an open-source multimodal agentic model that jointly optimizes text and vision through techniques including joint pre-training, zero-vision SFT, and joint reinforcement learning. It introduces Agent Swarm, a parallel agent orchestration framework using Parallel-Agent Reinforcement Learning (PARL) to decompose tasks into heterogeneous sub-problems executed concurrently. Evaluations show state-of-the-art results across coding, vision, reasoning, and agentic tasks, with Agent Swarm reducing latency by up to 4.5× and improving WideSearch item-level F1 from…
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