2 citations · 5 across the 16 of their papers we have counts for
16 papers
Learning Agent Routing From Early Experience
Yimin Wang, Jiahao Qiu, Xuan Qi +6
LLM agents achieve strong performance on complex reasoning tasks but incur high latency and compute cost. In practice, many queries fall within the capability boundary of cutting-e…
Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding
Zhiyuan Jiang, Shenghao Xie, Wenyi Li +8
Grounding is a fundamental capability for building graphical user interface (GUI) agents. Although existing approaches rely on large-scale bounding box supervision, they still face…
Alita-G: Self-Evolving Generative Agent for Agent Generation
Jiahao Qiu, Xuan Qi, Hongru Wang +9
Large language models (LLMs) have been shown to perform better when scaffolded into agents with memory, tools, and feedback. Beyond this, self-evolving agents have emerged, but cur…
On the Role of Preference Variance in Preference Optimization
Jiacheng Guo, Zihao Li, Jiahao Qiu +2
Direct Preference Optimization (DPO) has emerged as an important approach for learning from human preferences in aligning large language models (LLMs). However, collecting human pr…
On Path to Multimodal Historical Reasoning: HistBench and HistAgent
Jiahao Qiu, Fulian Xiao, Yimin Wang +96
Recent advances in large language models (LLMs) have led to remarkable progress across domains, yet their capabilities in the humanities, particularly history, remain underexplored…
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes
Jiahao Qiu, Xinzhe Juan, Yimin Wang +11
While knowledge distillation has become a mature field for compressing large language models (LLMs) into smaller ones by aligning their outputs or internal representations, the dis…
Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution
Jiahao Qiu, Xuan Qi, Tongcheng Zhang +15
Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually…
Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data?
Xuan Qi, Jiahao Qiu, Xinzhe Juan +2
Aligning large language models (LLMs) with human preferences remains a key challenge in AI. Preference-based optimization methods, such as Reinforcement Learning with Human Feedbac…
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety
Jiahao Qiu, Yinghui He, Xinzhe Juan +7
The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a mu…
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment
Souradip Chakraborty, Sujay Bhatt, Udari Madhushani Sehwag +7
Alignment of Large Language models (LLMs) is crucial for safe and trustworthy deployment in applications. Reinforcement learning from human feedback (RLHF) has emerged as an effect…
Temporal Consistency for LLM Reasoning Process Error Identification
Jiacheng Guo, Yue Wu, Jiahao Qiu +4
Verification is crucial for effective mathematical reasoning. We present a new temporal consistency method where verifiers iteratively refine their judgments based on the previous…
Fast Best-of-N Decoding via Speculative Rejection
Hanshi Sun, Momin Haider, Ruiqi Zhang +6
The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human p…
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
Jiahao Qiu, Yifu Lu, Yifan Zeng +9
Inference-time alignment enhances the performance of large language models without requiring additional training or fine-tuning but presents challenges due to balancing computation…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
MaxMin-RLHF: Alignment with Diverse Human Preferences
Souradip Chakraborty, Jiahao Qiu, Hui Yuan +5
Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data. However, such an ap…
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
Jiahao Qiu, Hui Yuan, Jinghong Zhang +3
While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a dauntin…