84 citations · 194 across the 103 of their papers we have counts for
22 papers · 2 filters
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…
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…
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-…
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…
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…
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…
Multimodal QUD: Inquisitive Questions from Scientific Figures
Yating Wu, William Rudman, Venkata S Govindarajan +2
Discourse comprehension in complex documents often involves continuously posing and resolving Questions Under Discussion (QUDs). While QUD frameworks have so far focused on text, s…
AI generates well-liked but templatic empathic responses
Emma S. Gueorguieva, Hongli Zhan, Jina Suh +4
Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM responses as more empathic than hum…
When to Call an Apple Red: Humans Follow Introspective Rules, VLMs Don't
Jonathan Nemitz, Carsten Eickhoff, Junyi Jessy Li +3
Understanding when Vision-Language Models (VLMs) will behave unexpectedly, whether models can reliably predict their own behavior, and if models adhere to their introspective reaso…
CREATE: Testing LLMs for Associative Creativity
Manya Wadhwa, Tiasa Singha Roy, Harvey Lederman +2
A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate…
Which course? Discourse! Teaching Discourse and Generation in the Era of LLMs
Junyi Jessy Li, Yang Janet Liu, Kanishka Misra +2
The field of NLP has undergone vast, continuous transformations over the past few years, sparking debates going beyond discipline boundaries. This begs important questions in educa…
Value Entanglement: Conflation Between Different Kinds of Good In (Some) Large Language Models
Seong Hah Cho, Junyi Li, Anna Leshinskaya
Value alignment of Large Language Models (LLMs) requires us to empirically measure these models' actual, acquired representation of value. Among the characteristics of value repres…
ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution
Zican Dong, Peiyu Liu, Junyi Li +4
Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces. However, as the sequence length grows, the key-value (KV) cache…
Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence
Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib +4
In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the context does not align with mod…