activity
20232026
most citedAdapting Large Language Models to Domains via Reading Comprehension

9 citations · 17 across the 18 of their papers we have counts for

collaborators
Showing 2026Show all

7 papers · 1 filter

cs.CL2026

ClawGym II: Exploring Black-Box RL on Agent Harness

Huatong Song, Fei Bai, Ming Yang +17

Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment. However, reinforcement learning through compl…

cs.CL2026

ClawGym: A Scalable Framework for Building Effective Claw Agents

Fei Bai, Huatong Song, Shuang Sun +11

Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around these environments remains const…

cs.CL2026

BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?

Guoxin Chen, Fanzhe Meng, Jiale Zhao +12

Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software engineering tasks that require ext…

cs.SE2026

SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training

Huatong Song, Lisheng Huang, Shuang Sun +11

In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master system…

cs.IR2026

HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders

Kun Yuan, Junyu Bi, Daixuan Cheng +5

Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory…

cs.IR2026

Generative Pseudo-Labeling for Pre-Ranking with LLMs

Junyu Bi, Xinting Niu, Daixuan Cheng +4

Pre-ranking is a critical stage in industrial recommendation systems, tasked with efficiently scoring thousands of recalled items for downstream ranking. A key challenge is the tra…

cs.CL2026

Computer Environments Elicit General Agentic Intelligence in LLMs

Daixuan Cheng, Shaohan Huang, Yuxian Gu +6

Agentic intelligence in large language models (LLMs) requires not only model intrinsic capabilities but also interactions with external environments. Equipping LLMs with computers…