4 citations · 12 across the 30 of their papers we have counts for
35 papers
Does Latent Chain-of-Thought Actually Reason? A Single, Unifying Taxonomy for Latent Reasoning
Jingjie Qiu, Sheldon Yu, Xunyi Jiang +6
Latent chain-of-thought replaces written reasoning steps with continuous hidden states, giving models additional steps to compute before answering. It remains unclear what computat…
Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight
Sheldon Yu, Tong Yu, Nikki Lijing Kuang +6
When every rollout for a problem fails, GRPO receives neither a successful exemplar nor a nonzero group-relative advantage, so it learns nothing from exactly the problems it most n…
Toward Latent Language Model Skills Steering and Optimization: An Empirical Study
Xunyi Jiang, Junda Wu, Yuxin Xiong +6
Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reasoning and program execution.…
Agent2UCB: Agentic System for Generative Engine Optimization
Sheldon Yu, Rui Wang, Tong Yu +4
Large language model driven search engines such as Google AI Overviews and Perplexity have created new opportunities for Generative Engine Optimization (GEO) the practice of refini…
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
Sizhe Zhou, Sheldon Yu, Hui Wei +8
Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generi…
How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories
Hui Wei, Junda Wu, Sheldon Yu +8
Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpretability methods rely on ou…
RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts
Yuxin Xiong, Xunyi Jiang, Rohan Surana +8
Group Relative Policy Optimization (GRPO) has shown strong effectiveness in reinforcement learning from verifiable feedback, where sampled rollouts can be compared within a group u…
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Sheldon Yu, Tong Yu, Xunyi Jiang +6
Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable. Existing methods for shap…
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
Rohan Surana, Gagan Mundada, Junda Wu +9
Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models…
OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents
Sheldon Yu, Junda Wu, Xintong Li +6
Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks. In deployed settings where agents repeatedly handle r…
MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization
Rohan Surana, Xintong Li, Sheldon Yu +7
Multi-negative preference optimization under the Plackett--Luce (PL) model extends Direct Preference Optimization (DPO) by leveraging comparative signals across one preferred and m…
FERA: Uncertainty-Aware Federated Reasoning for Large Language Models
Ruhan Wang, Chengkai Huang, Zhiyong Wang +6
Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across organizations that cannot c…
Skill-R1: Agent Skill Evolution via Reinforcement Learning
Yash Vishe, Rohan Surana, Xunyi Jiang +8
Agentic large language models often rely on skills, reusable natural language procedures that guide planning, action, and tool use. In practice, skills are typically improved throu…
Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
Zihan Huang, Junda Wu, Tong Yu +6
While LLM-based agents excel at planning and executing long action sequences, their execution often remains inconsistent across trials, limiting reliability. Consolidating agent co…
AMPS: Adaptive Modality Preference Steering via Functional Entropy
Zihan Huang, Xintong Li, Rohan Surana +5
Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may ove…
Importance Sampling for Multi-Negative Multimodal Direct Preference Optimization
Xintong Li, Chuhan Wang, Junda Wu +4
Direct Preference Optimization (DPO) has recently been extended from text-only models to vision-language models. However, existing methods rely on oversimplified pairwise compariso…
Pluralistic Off-policy Evaluation and Alignment
Chengkai Huang, Junda Wu, Zhouhang Xie +6
Personalized preference alignment for LLMs with diverse human preferences requires evaluation and alignment methods that capture pluralism. Most existing preference alignment datas…
Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics
Sheldon Yu, Yuxin Xiong, Junda Wu +6
Recent advances in chain-of-thought (CoT) prompting have enabled large language models (LLMs) to perform multi-step reasoning. However, the explainability of such reasoning remains…
SAND: Boosting LLM Agents with Self-Taught Action Deliberation
Yu Xia, Yiran Shen, Junda Wu +5
Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these…
DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer
Ruoyu Wang, Junda Wu, Yu Xia +4
Large language model-based agents, empowered by in-context learning (ICL), have demonstrated strong capabilities in complex reasoning and tool-use tasks. However, existing works ha…
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition
Junda Wu, Yuxin Xiong, Xintong Li +7
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to break down complex problems into interpretable intermediate steps, significantly enhancing model transparen…
Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation
Ruoyu Wang, Tong Yu, Junda Wu +3
Visual Language Navigation (VLN) is a fundamental task within the field of Embodied AI, focusing on the ability of agents to navigate complex environments based on natural language…
CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing
Rui Wang, Junda Wu, Yu Xia +6
Large Language Models (LLMs) are susceptible to indirect prompt injection attacks, where the model inadvertently responds to instructions injected into the prompt context. This vul…
In-context Ranking Preference Optimization
Junda Wu, Rohan Surana, Zhouhang Xie +6
Recent developments in Direct Preference Optimization (DPO) allow large language models (LLMs) to function as implicit ranking models by maximizing the margin between preferred and…