84 citations · 194 across the 103 of their papers we have counts for
37 papers · 1 filter
PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives
Kaixin Ding, Xi Chen, Minghong Cai +9
Video world models simulate future states conditioned on current observations and user actions. Recent systems have demonstrated impressive video consistency and action controllabi…
HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents
Hy Vision Team, Huawen Shen, Zhengyang Tang +20
As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embod…
R-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search
Sheng Zhang, Junyi Li, Wenlin Zhang +6
Recent search agents for multi-hop reasoning often fail by either retrieving incomplete evidence or reasoning over irrelevant portions of the retrieved content, leading to a retrie…
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…
Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization
Junyi Li, Xiaowei Qian, Yingyi Zhang +6
Recent advances in tool-integrated language agents have significantly improved their ability to solve complex reasoning tasks. However, existing alignment methods predominantly foc…
RAGR: Review-Augmented Generative Recommendation
Yingyi Zhang, Junyi Li, Yejing Wang +8
Sequential recommendation (SR) is traditionally formulated as next-item prediction over chronological item interactions. Although recent generative recommendation (GR) methods intr…
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…
GENIE: A Fine-Grained Measure for Novelty
Ramya Namuduri, Manya Wadhwa, Anshun Asher Zheng +2
Large Language Models have consistently demonstrated a lack of creativity and diversity across tasks. Prior work has focused on addressing whether models are capable of generating…
Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models
Jia Deng, Junyi Li, Wayne Xin Zhao +3
Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training methods largely rely on random…
VESTA: Visual Exploration with Statistical Tool Agents
William Rudman, Abhishek Divekar, Kanishk Jain +6
Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated. Recent agent-based systems leverage language and vision-la…
LinkNav: Surfacing Interconnected Information in Scientific Articles
Sebastian Joseph, Jennifer Healey, Junyi Jessy Li +1
We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we inst…
What's in a Name? Morphological Shortcuts by LLMs in Pharmacology
Kaijie Mo, Thomas Yang, Chantal Shaib +6
The morphological form of a word can often give cues to its meaning, but purely relying on these mappings can lead to overgeneralization in high-stakes domains. In the medical doma…
HERO'S JOURNEY: Testing Complex Rule Induction with Text Games
Anshun Asher Zheng, Kanishka Misra, David I. Beaver +1
We introduce HERO'S JOURNEY, a benchmark for rule induction in goal-directed episodic tasks, where agents must infer hidden rules from demonstrations and act on them through multi-…
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…
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search
Sheng Zhang, Junyi Li, Yingyi Zhang +7
Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external kno…
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…
Towards Long-horizon Agentic Multimodal Search
Yifan Du, Zikang Liu, Jinbiao Peng +5
Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, managing the heterogeneous informa…
Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads
Guojing Li, Zichuan Fu, Junyi Li +6
Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-mark…
Job Skill Extraction via LLM-Centric Multi-Module Framework
Guojing Li, Zichuan Fu, Junyi Li +8
Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (LLMs) often yield malformed s…
CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization
Junyi Li, Yongqiang Chen, Ningning Ding
Machine unlearning has gained increasing attention in recent years, as a promising technique to selectively remove unwanted privacy or copyrighted information from Large Language M…
AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot
Joydeep Biswas, Sheila Schoepp, Gautham Vasan +10
Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and timeliness. Recent advances i…
Discourse Diversity in Multi-Turn Empathic Dialogue
Hongli Zhan, Emma S. Gueorguieva, Javier Hernandez +3
Large language models (LLMs) produce responses rated as highly empathic in single-turn settings (Ayers et al., 2023; Lee et al., 2024), yet they are also known to be formulaic gene…
This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA
Hye Sun Yun, Geetika Kapoor, Michael Mackert +4
Patients are increasingly turning to large language models (LLMs) with medical questions that are complex and difficult to articulate clearly. However, LLMs are sensitive to prompt…