17 citations · 28 across the 19 of their papers we have counts for
20 papers
OS-Bench: Evaluating SWE Agents on Cross-OS Code Migration
Qirui Jin, Kenan Li, Liao Zhu +8
Language-model (LM) agents are increasingly evaluated on repository-level software engineering (SWE) benchmarks derived from GitHub issues and pull requests. Although these benchma…
ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance
Qirui Jin, Lingching Tung, Kenan Li +12
Language model (LM) agents are increasingly deployed to maintain codebases over extended periods, fixing streams of related defects while carrying context from one fix to the next.…
Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification
Jinghan Xu, Yikai Zhang, Aili Chen +3
Agent harnesses shape how language-model agents use instructions, tools, and runtime components, but adapting these harnesses requires costly verification. Existing propose-and-ver…
CAST: Game Solvers as Turn-Level Teachers for LLM Agents
Yu Wang, Yi-Kai Zhang, Wentao Shi +8
Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR)…
From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier
Eric Jiang, Xiao Liang, Yikai Zhang +19
Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for we…
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
Yikai Zhang, Gaoxiang Jia, Jie Ding +1
TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The lib…
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents
Kenan Li, Qirui Jin, Liao Zhu +13
Recent advances in Language Model (LM) agents have increasingly focused on repository-level software engineering (SWE) tasks, leading to various agentic workflows, training methods…
SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent
Yikai Zhang, Jiaxin Pei, Kenan Li +9
Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface confl…
The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping
Yang Liu, Enxi Wang, Yufei Gao +6
Despite the success of reinforcement learning for large language models, a common failure mode is reduced sampling diversity, where the policy repeatedly generates similar erroneou…
RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms
Kenan Li, Rongzhi Li, Linghao Zhang +17
Language model (LM) agents have driven substantial progress in automated software engineering (SWE), yet building and testing software repositories at scale remains a largely manua…
Lita: Light Agent Uncovers the Agentic Coding Capabilities of LLMs
Hankun Dai, Maoquan Wang, Mengnan Qi +6
Large language models (LLMs) are increasingly being applied to programming tasks, ranging from single-turn code completion to autonomous agents. Current code agent designs frequent…
Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game
Rong Ye, Yongxin Zhang, Yikai Zhang +3
Achieving Artificial General Intelligence (AGI) requires AI agents that can not only make stratigic decisions but also engage in flexible and meaningful communication. Inspired by…
Wings: Learning Multimodal LLMs without Text-only Forgetting
Yi-Kai Zhang, Shiyin Lu, Yang Li +7
Multimodal large language models (MLLMs), initiated with a trained LLM, first align images with text and then fine-tune on multimodal mixed inputs. However, the MLLM catastrophical…
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
Zhen Huang, Zengzhi Wang, Shijie Xia +25
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…
Dissecting Human and LLM Preferences
Junlong Li, Fan Zhou, Shichao Sun +3
As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluatio…
Extending LLMs' Context Window with 100 Samples
Yikai Zhang, Junlong Li, Pengfei Liu
Large Language Models (LLMs) are known to have limited extrapolation ability beyond their pre-trained context window, constraining their application in downstream tasks with length…
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang +2
Real-world scenarios are usually accompanied by continuously appearing classes with scare labeled samples, which require the machine learning model to incrementally learn new class…
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse
Yi-Kai Zhang, Lu Ren, Chao Yi +3
The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning. Leveraging pre-trained…
Model Spider: Learning to Rank Pre-Trained Models Efficiently
Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding +2
Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is essential to take advantage of plentiful model resources. With the availability of numerous hete…