Artemis: Structured Visual Reasoning for Perception Policy Learning
Recent reinforcement-learning frameworks for visual perception policy have begun to incorporate intermediate reasoning chains expressed in natural language. Empirical observations indicate that such purely linguistic intermediate reasoning often reduces performance on perception tasks. We argue that the core issue lies not in reasoning per se but in the form of reasoning: while these chains perform semantic reasoning in an unstructured linguistic space, visual perception requires reasoning in a spatial and object-centric space. In response, we introduce Artemis, a perception-policy learning framework that performs structured proposal-based reasoning, where each intermediate step is represented as a (label, bounding-box) pair capturing a verifiable visual state. This design enables explicit tracking of intermediate states, direct supervision for proposal quality, and avoids ambiguity introduced by language-based reasoning. Artemis is built on Qwen2.5-VL-3B, achieves strong performance on grounding and detection task and exhibits substantial generalization to counting and geometric-perception tasks. The consistent improvements across these diverse settings confirm that aligning reasoning with spatial representations enhances perception-policy learning. Owing to its strengthened visual reasoning, Artemis also achieves competitive performance on general MLLM benchmarks, illustrating that spatially grounded reasoning provides a principled route toward scalable and general perception policies.
692fe12026742347f61dadbb
Wei Tang
692fe12026742347f61dadbc
Yanpeng Sun
claimed_verified
2025-12-03T09:13:14.051Z
64297212e5f33939cf3a3d9b
/avatars/bd21759ab5d7e526b99fcb7ed813ffb3.svg
yanpeng_sun
syp115
user
syp115
692fe12026742347f61dadbd
Shan Zhang
692fe12026742347f61dadbe
Xiaofan Li
692fe12026742347f61dadbf
Piotr Koniusz
692fe12026742347f61dadc0
Wei Li
692fe12026742347f61dadc1
Na Zhao
692fe12026742347f61dadc2
Zechao Li
Artemis: Structured Visual Reasoning for Perception Policy Learning
Recent reinforcement-learning frameworks for visual perception policy have begun to incorporate intermediate reasoning chains expressed in natural language. Empirical observations indicate that such purely linguistic intermediate reasoning often reduces performance on perception tasks. We argue that the core issue lies not in reasoning per se but in the form of reasoning: while these chains perform semantic reasoning in an unstructured linguistic space, visual perception requires reasoning in a spatial and object-centric space. In response, we introduce Artemis, a perception-policy learning framework that performs structured proposal-based reasoning, where each intermediate step is represented as a (label, bounding-box) pair capturing a verifiable visual state. This design enables explicit tracking of intermediate states, direct supervision for proposal quality, and avoids ambiguity introduced by language-based reasoning. Artemis is built on Qwen2.5-VL-3B, achieves strong performance on grounding and detection task and exhibits substantial generalization to counting and geometric-perception tasks. The consistent improvements across these diverse settings confirm that aligning reasoning with spatial representations enhances perception-policy learning. Owing to its strengthened visual reasoning, Artemis also achieves competitive performance on general MLLM benchmarks, illustrating that spatially grounded reasoning provides a principled route toward scalable and general perception policies.
Artemis, a perception-policy learning framework, enhances performance on visual tasks by using structured spatial reasoning with (label, bounding-box) pairs instead of linguistic intermediate reasoning.
reinforcement-learning frameworks visual perception policy intermediate reasoning chains natural language semantic reasoning spatial reasoning object-centric space structured proposal-based reasoning bounding-box verifiable visual state direct supervision ambiguity grounding detection counting geometric-perception MLLM benchmarks scalable perception policies