11 papers
AIBuildAI-2.5: Efficient Autonomous AI Model Development Through LLM-Guided Tree Search
Peijia Qin, Ruiyi Zhang, Qi Cao +3
Autonomous agents that automatically build artificial intelligence (AI) models could broaden access to AI across science and engineering. A popular line of such agents frames model…
ATLAS: Agentic Test-time Learning-to-Allocate Scaling
Peijia Qin, Qi Cao, Pengtao Xie
Test-time scaling has become a major way to improve large language model reasoning, but its orchestration has remained designer-engineered: a fixed sample budget, a fixed refinemen…
AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models
Ruiyi Zhang, Peijia Qin, Qi Cao +2
AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing them remains heavily manual,…
LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling
Qi Cao, Yufan Wang, Peijia Qin +2
Large language models (LLMs) often expose useful signals of self-monitoring: before solving a problem, they can estimate whether they are likely to succeed, and after solving it, t…
BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models
Xin Gao, Ruiyi Zhang, Meixi Du +2
Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key limi…
AIBuildAI: An AI Agent for Automatically Building AI Models
Ruiyi Zhang, Peijia Qin, Qi Cao +2
AI models underpin modern intelligent systems, driving advances across science, medicine, finance, and technology. Yet developing high-performing AI models remains a labor-intensiv…
Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
Qi Cao, Shuhao Zhang, Ruizhe Zhou +3
Model routing chooses which language model to use for each query. By sending easy queries to cheaper models and hard queries to stronger ones, it can significantly reduce inference…
FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation
Ruiyi Zhang, Peijia Qin, Qi Cao +2
Code generation is a core application of large language models (LLMs), yet LLMs still frequently fail on complex programming tasks. Given its success in mathematical reasoning, tes…
DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
Peijia Qin, Ruiyi Zhang, Qi Cao +1
Test-time scaling for code generation commonly relies on Best-of-N selection, in which multiple candidate solutions are sampled from a base model, and the best one is selected by a…
DreamPRM-Code: Function-as-Step Process Reward Model with Label Correction for LLM Coding
Ruiyi Zhang, Peijia Qin, Qi Cao +1
Process Reward Models (PRMs) have become essential for improving Large Language Models (LLMs) via test-time scaling, yet their effectiveness in coding remains limited due to the la…
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
Peijia Qin, Ruiyi Zhang, Pengtao Xie
Parameter-efficient fine-tuning (PEFT) is a flexible and efficient method for adapting large language models (LLMs) to downstream tasks. Among these methods, weight-decomposed low-…