56 citations · 67 across the 18 of their papers we have counts for
19 papers
PhoneBuddy: Training Open Models for Agentic Phone Use
Zhengyang Tang, Xin Lai, Pengyuan Lyu +23
Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable phone use remains difficult because the environment that matter…
GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?
Tongxu Luo, Rongsheng Wang, Jiaxi Bi +22
Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional cod…
PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions
Chenxin Li, Zhengyao Fang, Zhengyang Tang +18
Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature sti…
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
Chenyu Huang, Jianghao Lin, Zhengyang Tang +4
We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based e…
Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning
Jiaxi Bi, Tongxu Luo, Wenyu Du +2
Parallel reasoning enhances Large Reasoning Models (LRMs) but incurs prohibitive costs due to futile paths caused by early errors. To mitigate this, path pruning at the prefix leve…
Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents
Zhengyang Tang, Yi Zhang, Chenxin Li +18
When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent re…
PhoneWorld: From Real-App Trajectories to Dynamic and Verifiable Environments for Phone-Use Agents
Yuxuan Liu, Xin Lai, Junyi Li +21
Real applications provide the training setting closest to phone-agent deployment, but are difficult to reset, scale safely, and verify programmatically. Static screenshots and inte…
Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
Chenxin Li, Zhengyang Tang, Mingxin Huang +8
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at…
Do Phone-Use Agents Respect Your Privacy?
Zhengyang Tang, Ke Ji, Xidong Wang +19
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operatio…
Teaching Language Models to Reason with Tools
Chengpeng Li, Zhengyang Tang, Ziniu Li +8
Large reasoning models (LRMs) like OpenAI-o1 have shown impressive capabilities in natural language reasoning. However, these models frequently demonstrate inefficiencies or inaccu…
CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling
Zhengyang Tang, Zihan Ye, Chenyu Huang +9
Large Reasoning Models (LRMs) have demonstrated strong capabilities in complex multi-step reasoning, opening new opportunities for automating optimization modeling. However, existi…
CoRT: Code-integrated Reasoning within Thinking
Chengpeng Li, Zhengyang Tang, Ziniu Li +8
Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or…
Learning from Peers in Reasoning Models
Tongxu Luo, Wenyu Du, Jiaxi Bi +5
Large Reasoning Models (LRMs) have the ability to self-correct even when they make mistakes in their reasoning paths. However, our study reveals that when the reasoning process sta…
RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques
Zhengyang Tang, Ziniu Li, Zhenyang Xiao +8
Critiques are important for enhancing the performance of Large Language Models (LLMs), enabling both self-improvement and constructive feedback for others by identifying flaws and…
Self-Evolving Critique Abilities in Large Language Models
Zhengyang Tang, Ziniu Li, Zhenyang Xiao +8
Despite their remarkable performance, Large Language Models (LLMs) face a critical challenge: providing feedback for tasks where human evaluation is difficult or where LLMs potenti…
Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
Bofei Gao, Feifan Song, Zhe Yang +17
Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH ar…
ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling
Chenyu Huang, Zhengyang Tang, Shixi Hu +5
Optimization modeling plays a critical role in the application of Operations Research (OR) tools to address real-world problems, yet they pose challenges and require extensive expe…
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
Zhengyang Tang, Xingxing Zhang, Benyou Wang +1
Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propos…
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang +17
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on se…