Papers5

#task decomposition

WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

WideSeek-R1 teaches a small 4B-parameter language model to act like a well-run team: one leader plans, many helpers work in parallel, and everyone learns together with reinforcement learning.

#width scaling#multi-agent reinforcement learning#orchestration

D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use

Intermediate

Bowen Xu, Shaoyu Wu et al.Feb 2arXiv

This paper fixes a common problem in reasoning AIs called Lazy Reasoning, where the model rambles instead of making a good plan.

#task decomposition#tool use#large reasoning models

daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently

Intermediate

Mohan Jiang, Dayuan Fu et al.Feb 2arXiv

Long tasks trip up most AIs because they lose track of goals and make small mistakes that snowball over many steps.

#long-horizon agency#pull request chains#software evolution

Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy

Intermediate

Hosein Hasani, Mohammadali Banayeeanzade et al.Jan 6arXiv

Large language models (LLMs) are good at many math problems but often mess up simple counting when the list gets long.

#mechanistic interpretability#counting in LLMs#System-2 prompting

WebGym: Scaling Training Environments for Visual Web Agents with Realistic Tasks

Intermediate

Hao Bai, Alexey Taymanov et al.Jan 5arXiv

WebGym is a giant practice world (almost 300,000 tasks) that lets AI web agents learn on real, ever-changing websites instead of tiny, fake ones.

#WebGym#visual web agents#vision-language models