2 citations · 2 across the 11 of their papers we have counts for
13 papers
Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making
Jing Liu, Yongxing Qi, Ping Yao +1
What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the public TCMEval-SDT benchmark for…
From Association to Causation: Improving Retrieval Precision of Retrieval-Augmented Generation via Causal Relations and an Attention Mechanism
Jing Liu, Yongxing Qi, Muchen Jiang +7
Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity, optionally followed by reran…
Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation
Haoyue Yang, Zhangxiao Shen, Fan Ding +8
Front-end web code has become a core product surface for every frontier LLM release, yet evaluating these interactive applications at development speed remains costly because human…
LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
Xu Ouyang, Deyi Liu, Yuhang Cai +5
Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as catastrophic overtraining and q…
Emulating Clinician Cognition via Self-Evolving Deep Clinical Research
Ruiyang Ren, Yuhao Wang, Yunsen Liang +8
Clinical diagnosis is a complex cognitive process, grounded in dynamic cue acquisition and continuous expertise accumulation. Yet most current artificial intelligence (AI) systems…
FormalEvolve: Neuro-Symbolic Evolutionary Search for Diverse Autoformalization
Haijian Lu, Wei Wang, Jing Liu
Autoformalization aims to produce formal statements that compile and faithfully preserve the intended meaning of informal mathematics. Yet standard single-output evaluation collaps…
Semantic Energy: Detecting LLM Hallucination Beyond Entropy
Huan Ma, Jiadong Pan, Jing Liu +7
Large Language Models (LLMs) are being increasingly deployed in real-world applications, but they remain susceptible to hallucinations, which produce fluent yet incorrect responses…
BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation
Yuhao Wang, Ruiyang Ren, Yucheng Wang +4
With the rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG) has emerged as a critical approach to supplement the inherent knowledge limitations…
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation
Yuhao Wang, Ruiyang Ren, Yucheng Wang +4
Considering the inherent limitations of parametric knowledge in large language models (LLMs), retrieval-augmented generation (RAG) is widely employed to expand their knowledge scop…
Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
Yuhao Wang, Ruiyang Ren, Yucheng Wang +4
Long-form question answering (LFQA) requires open-ended long-form responses that synthesize coherent, factually grounded content from multi-source evidence. This makes reinforcemen…
Self-Calibrated Listwise Reranking with Large Language Models
Ruiyang Ren, Yuhao Wang, Kun Zhou +5
Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passa…
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Ruiyang Ren, Peng Qiu, Yingqi Qu +5
Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e…
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
Yuhao Wang, Ruiyang Ren, Junyi Li +3
Considering the limited internal parametric knowledge, retrieval-augmented generation (RAG) has been widely used to extend the knowledge scope of large language models (LLMs). Desp…