5 citations · 5 across the 5 of their papers we have counts for
5 papers
JURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR
Xinjie Chen, Biao Fu, Jing Wu +4
Reinforcement learning with verifiable rewards (RLVR) enhances the reasoning of large language models (LLMs), but standard RLVR often depends on human-annotated answers or carefull…
SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains
Longkun Li, Yuanben Zou, Jinghan Wu +4
Graph-RAG improves LLM reasoning using structured knowledge, yet conventional designs rely on a centralized knowledge graph. In distributed and access-restricted settings (e.g., ho…
Internal Representations as Indicators of Hallucinations in Agent Tool Selection
Kait Healy, Bharathi Srinivasan, Visakh Madathil +1
Large Language Models (LLMs) have shown remarkable capabilities in tool calling and tool usage, but suffer from hallucinations where they choose incorrect tools, provide malformed…
ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization
Guoxin Chen, Jing Wu, Xinjie Chen +6
Autoformalization, which translates natural language mathematics into machine-verifiable formal statements, is critical for using formal mathematical reasoning to solve math proble…
Designing Large Foundation Models for Efficient Training and Inference: A Survey
Dong Liu, Yanxuan Yu, Yite Wang +5
This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and Syste…