Papers22

#GRPO

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization

The paper teaches an AI to act like a careful traveler: it looks at a photo, forms guesses about where it might be, and uses real map tools to check each guess.

#image geolocalization#map-augmented agent#Thinking with Map

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Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

Beginner

Runze He, Yiji Cheng et al.Jan 8arXiv

Re-Align is a new way for AI to make and edit pictures by thinking in clear steps before drawing.

#In-Context Image Generation#Reference-based Image Editing#Structured Reasoning

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Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction

Beginner

Muzhao Tian, Zisu Huang et al.Jan 8arXiv

Long-term AI helpers remember past chats, but using all memories can trap them in old ideas (Memory Anchoring).

#steerable memory#memory anchoring#long-term agents

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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

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One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling

Beginner

Yiyuan Li, Zhen Huang et al.Jan 6arXiv

This paper shows that training a language model with reinforcement learning on just one super well-designed example can boost reasoning across many school subjects, not just math.

#polymath learning#one-shot reinforcement learning#GRPO

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Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

Beginner

Falcon LLM Team, Iheb Chaabane et al.Jan 5arXiv

Falcon-H1R is a small (7B) AI model that thinks really well without needing giant computers.

#Falcon-H1R#Hybrid Transformer-Mamba#Chain-of-Thought

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Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

Beginner

Yuqiao Tan, Minzheng Wang et al.Dec 22arXiv

Large language models (LLMs) don’t act as a single brain; inside, each layer and module quietly makes its own mini-decisions called internal policies.

#Bottom-up Policy Optimization#internal layer policy#internal modular policy

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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

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On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models

Beginner

Charlie Zhang, Graham Neubig et al.Dec 8arXiv

The paper asks when reinforcement learning (RL) really makes language models better at reasoning beyond what they learned in pre-training.

#edge of competence#process-verified evaluation#process-level rewards

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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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