1 citations · 1 across the 6 of their papers we have counts for
7 papers
RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases
Yingqian Wu, Jingcong Liang, Siyuan Wang +4
Large language models (LLMs) increasingly act as research agents, yet their ability to track shifts in research attention is difficult to evaluate because reviews and research idea…
SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task
Qiming Bao, Neşet Özkan Tan, Siyuan Wang +1
We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scientific claims against the tabl…
AI-driven ionic liquid discovery with unified chemical intelligence
Yuqi Yin, Yibo Fu, Siyuan Wang +10
The rational discovery of functional solvents remains challenging because macroscopic solvent behaviors emerge from complex many-body interactions across vast chemical spaces. Ioni…
LoongRL: Reinforcement Learning for Advanced Reasoning over Long Contexts
Siyuan Wang, Gaokai Zhang, Li Lyna Zhang +4
Reasoning over long contexts is essential for large language models. While reinforcement learning (RL) enhances short-context reasoning by inducing "Aha" moments in chain-of-though…
Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
Yaorui Shi, Yuxin Chen, Siyuan Wang +5
Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens. Existing works equip large langu…
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
Siyuan Wang, James R. Foulds, Md Osman Gani +1
In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corrobo…
LongRoPE2: Near-Lossless LLM Context Window Scaling
Ning Shang, Li Lyna Zhang, Siyuan Wang +5
LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the or…