5 citations · 11 across the 39 of their papers we have counts for
19 papers · 1 filter
CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
Qing Zong, Jiayu Liu, Tianshi Zheng +7
Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional…
DixitWorld: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay
Yunxiang Mo, Tianshi Zheng, Qing Zong +6
Multimodal abductive reasoning--the generation and selection of explanatory hypotheses from partial observations--is a cornerstone of intelligence. Current evaluations of this abil…
The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
Baixuan Xu, Tianshi Zheng, Zhaowei Wang +4
Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt…
LLM-Hanabi: Evaluating Multi-Agent Gameplays with Theory-of-Mind and Rationale Inference in Imperfect Information Collaboration Game
Fangzhou Liang, Tianshi Zheng, Chunkit Chan +2
Effective multi-agent collaboration requires agents to infer the rationale behind others' actions, a capability rooted in Theory-of-Mind (ToM). While recent Large Language Models (…
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
Hong Ting Tsang, Jiaxin Bai, Haoyu Huang +5
Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered…
NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen +10
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer…
Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework
Zheye Deng, Chunkit Chan, Tianshi Zheng +3
The evolution of AI systems toward agentic operation and context-aware retrieval necessitates transforming unstructured text into structured formats like tables, knowledge graphs,…
Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty
Rui Wang, Qihan Lin, Jiayu Liu +7
Real-world decision-making often involves uncertainty expressed in linguistic rather than numerical terms, and Prospect Theory (PT) provides a classic framework for modeling human…
INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling
Haochen Shi, Tianshi Zheng, Weiqi Wang +6
Large Language Model (LLM) routing is a pivotal technique for navigating a diverse landscape of LLMs, aiming to select the best-performing LLMs tailored to the domains of user quer…
Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study
Baixuan Xu, Chunyang Li, Weiqi Wang +6
Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically inve…
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
Jiaxin Bai, Wei Fan, Qi Hu +17
We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models t…
Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs
Yisen Gao, Jiaxin Bai, Tianshi Zheng +5
Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such as clinical diagnosis and scient…
Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents
Wei Fan, Tianshi Zheng, Yiran Hu +7
Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. Wh…
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery
Tianshi Zheng, Zheye Deng, Hong Ting Tsang +4
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundament…
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
Tianshi Zheng, Yixiang Chen, Chengxi Li +7
Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs). However, our study reveals a surprisi…
Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations
Chunyang Li, Weiqi Wang, Tianshi Zheng +1
Inductive reasoning, a cornerstone of human cognition, enables generalization from limited data but hasn't yet been fully achieved by large language models (LLMs). While modern LLM…
LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning
Tianshi Zheng, Jiayang Cheng, Chunyang Li +6
Modern large language models (LLMs) employ diverse logical inference mechanisms for reasoning, making the strategic optimization of these approaches critical for advancing their ca…
Top Ten Challenges Towards Agentic Neural Graph Databases
Jiaxin Bai, Zihao Wang, Yukun Zhou +16
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by inte…
Enhancing Transformers for Generalizable First-Order Logical Entailment
Tianshi Zheng, Jiazheng Wang, Zihao Wang +5
Transformers, as the fundamental deep learning architecture, have demonstrated great capability in reasoning. This paper studies the generalizable first-order logical reasoning abi…