30 citations · 38 across the 28 of their papers we have counts for
32 papers
IsoVRBench: Video Reward Modeling Without Human Feedback
Shuyue Stella Li, Jacqueline He, Jiahui Chen +5
Video reward models are trained and evaluated on human preferences between videos that often differ simultaneously on properties such as prompt alignment, visual fidelity, motion,…
ExploreNet: Learning Where to Explore in Diffusion GRPO
Shuyue Stella Li, Xiaochuang Han, Yulia Tsvetkov +1
Group-relative RL methods such as Flow-GRPO post-train image generators by exploring with isotropic Gaussian noise added at every denoising step. This noise decides which rollouts…
Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts
Shuyue Stella Li, Xiaochuang Han, Yulia Tsvetkov +1
Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part because it is learned using un…
Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall
Jacqueline He, Howard Yen, Shuyue Stella Li +9
Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits are consistent across traini…
Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues
Mohammadamin Shafiei, Shuyue Stella Li, Yulia Tsvetkov
As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behaviors are genuine or superficia…
Multilingual Reasoning Cascades Need More Context
Arnav Mazumder, Dengjia Zhang, Shuyue Stella Li +2
Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a compet…
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs
Yang Luo, Xinran Liu, Tiantian Ji +3
Multimodal Large Language Models (MLLMs) excel at structural reasoning yet suffer from a sharp logical brittleness in structural consistency. We term this phenomenon Structural Cog…
ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts
Yang Luo, Zifeng Kang, Tiantian Ji +4
Graph-based agent memory is increasingly used in LLM agents to support structured long-term recall and multi-hop reasoning, but it also creates a new poisoning surface: an attacker…
Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
Dean Light, Michael Theologitis, Kshitish Ghate +7
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment,…
EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics
Shuyue Stella Li, Rui Xin, Teng Xiao +8
Language models encode substantial evaluative knowledge from pretraining, yet current post-training methods rely on external supervision (human annotations, proprietary models, or…
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning
Maksym Taranukhin, Shuyue Stella Li, Evangelos Milios +3
LLMs are increasingly deployed in high-stakes domains such as medical triage and legal assistance, often as document-grounded QA systems in which a user provides a description, rel…
Cold-Start Personalization via Training-Free Priors from Structured World Models
Avinandan Bose, Shuyue Stella Li, Faeze Brahman +6
Cold-start personalization requires inferring user preferences through interaction when no user-specific historical data is available. The core challenge is a routing problem: each…
Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch
Hyunwoo Kim, Niloofar Mireshghallah, Michael Duan +11
Research involving privacy-sensitive data has always been constrained by data scarcity, standing in sharp contrast to other areas that have benefited from data scaling. This challe…
Self-Improving VLM Judges Without Human Annotations
Inna Wanyin Lin, Yushi Hu, Shuyue Stella Li +5
Effective judges of Vision-Language Models (VLMs) are crucial for model development. Current methods for training VLM judges mainly rely on large-scale human preference annotations…
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang +9
Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reas…
RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
Zhiyuan Zeng, Hamish Ivison, Yiping Wang +14
We introduce Reinforcement Learning (RL) with Adaptive Verifiable Environments (RLVE), an approach using verifiable environments that procedurally generate problems and provide alg…
PrefDisco: Benchmarking Proactive Personalized Reasoning
Shuyue Stella Li, Avinandan Bose, Faeze Brahman +4
Current large language model (LLM) development treats task-solving and preference-alignment as separate challenges, optimizing first for objective correctness, then for alignment t…
PrefPalette: Personalized Preference Modeling with Latent Attributes
Shuyue Stella Li, Melanie Sclar, Hunter Lang +7
Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human ju…
Spurious Rewards: Rethinking Training Signals in RLVR
Rulin Shao, Shuyue Stella Li, Rui Xin +11
We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have little,…
Precise Information Control in Long-Form Text Generation
Jacqueline He, Howard Yen, Margaret Li +7
A central challenge in language models (LMs) is faithfulness hallucination: the generation of information unsubstantiated by input context. To study this problem, we propose Precis…
BehaviorSFT: Behavioral Token Conditioning for Clinical Agents Across the Proactivity Spectrum
Yubin Kim, Zhiyuan Hu, Hyewon Jeong +11
Large Language Models (LLMs) as clinical agents require careful behavioral adaptation. While adept at reactive tasks (e.g., diagnosis reasoning), LLMs often struggle with proactive…
BLAB: Brutally Long Audio Bench
Orevaoghene Ahia, Martijn Bartelds, Kabir Ahuja +13
Developing large audio language models (LMs) capable of understanding diverse spoken interactions is essential for accommodating the multimodal nature of human communication and ca…