Papers160

#reinforcement learning

Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards

The paper fixes a big problem in training web-searching AI: rewarding only the final answer makes agents cut corners and sometimes hallucinate.

#deep search agents#reinforcement learning#rubric rewards

The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning

Intermediate

Qiguang Chen, Yantao Du et al.Jan 9arXiv

This paper says long chain-of-thought (Long CoT) works best when it follows a 'molecular' pattern with three kinds of thinking bonds: Deep-Reasoning, Self-Reflection, and Self-Exploration.

#Long Chain-of-Thought#reasoning bonds#Deep Reasoning

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

Intermediate

Xiaoshuai Song, Haofei Chang et al.Jan 9arXiv

EnvScaler is an automatic factory that builds many safe, rule-following practice worlds where AI agents can talk to users and call tools, just like real apps.

#EnvScaler#tool-interactive environments#programmatic synthesis

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

Beginner

Yuxiang Ji, Yong Wang et al.Jan 8arXiv

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

RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

Beginner

Yuan-Kang Lee, Kuan-Lin Chen et al.Jan 8arXiv

This paper teaches a camera to fix nighttime colors by combining a smart rule-based color trick (SGP-LRD) with a learning-by-trying helper (reinforcement learning).

#auto white balance#color constancy#nighttime imaging

VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice

Intermediate

Shuming Liu, Mingchen Zhuge et al.Jan 8arXiv

The paper asks a simple question: do video AIs really need to “think out loud” every time, or can they answer quickly most of the time and think deeply only when needed?

#video reasoning#adaptive reasoning#early exit

RelayLLM: Efficient Reasoning via Collaborative Decoding

Intermediate

Chengsong Huang, Tong Zheng et al.Jan 8arXiv

RelayLLM lets a small model do the talking and only asks a big model for help on a few, truly hard tokens.

#token-level collaboration#<call>n</call> command#collaborative decoding

Agent-as-a-Judge

Beginner

Runyang You, Hongru Cai et al.Jan 8arXiv

This survey explains how AI judges are changing from single smart readers (LLM-as-a-Judge) into full-on agents that can plan, use tools, remember, and work in teams (Agent-as-a-Judge).

#Agent-as-a-Judge#LLM-as-a-Judge#multi-agent collaboration

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

TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning

Intermediate

Yinuo Wang, Mining Tan et al.Jan 8arXiv

TourPlanner is a travel-planning system that first gathers the right places, then lets multiple expert ‘voices’ debate plans, and finally polishes the winner with a learning method that follows rules before style.

#travel planning#multi-agent reasoning#chain-of-thought

TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration

Intermediate

Jiuzhou Zhao, Chunrong Chen et al.Jan 8arXiv

Multi-agent systems are like teams of expert helpers; the tricky part is choosing which helpers to ask for each question.

#multi-agent systems#routing#reasoning chain

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

Beginner

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

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