Papers29

#reinforcement learning

Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning

Jinyang Wu, Guocheng Zhai et al.Jan 7arXiv

ATLAS is a system that picks the best mix of AI models and helper tools for each question, instead of using just one model or a fixed tool plan.

#ATLAS#LLM routing#tool augmentation

ThinkRL-Edit: Thinking in Reinforcement Learning for Reasoning-Centric Image Editing

Beginner

Hengjia Li, Liming Jiang et al.Jan 6arXiv

ThinkRL-Edit teaches an image editor to think first and draw second, which makes tricky, reasoning-heavy edits much more accurate.

#reasoning-centric image editing#reinforcement learning#chain-of-thought

Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding

Beginner

Jiaqi Tang, Jianmin Chen et al.Dec 19arXiv

Robust-R1 teaches vision-language models to notice how a picture is damaged, think through what that damage hides, and then answer as if the picture were clear.

#Robust-R1#degradation-aware reasoning#multimodal large language models

Image Diffusion Preview with Consistency Solver

Beginner

Fu-Yun Wang, Hao Zhou et al.Dec 15arXiv

Diffusion Preview is a two-step “preview-then-refine” workflow that shows you a fast draft image first and only spends full compute after you like the draft.

#diffusion preview#consistency solver#pf-ode

COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence

Beginner

Zefeng Zhang, Xiangzhao Hao et al.Dec 4arXiv

COOPER is a single AI model that both “looks better” (perceives depth and object boundaries) and “thinks smarter” (reasons step by step) to answer spatial questions about images.

#COOPER#multimodal large language model#unified model

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