4 citations · 14 across the 20 of their papers we have counts for
23 papers
ULTRADISCOVERY: Abductive Exploration in an Interconnected, Epistemically Open Universe
Weihan Li, Tianshi Zheng, Yangqiu Song +2
Scientific discovery often begins when scattered clues call for a new way of describing the world. Such abductive exploration can require constructing the representation in which a…
AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era
Yunxiang Mo, Tianshi Zheng, Yisen Gao +7
Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Existing evaluations largely ass…
SciLens: Multi-modal Scientific Claim Verification with Agentic Entailment and Grounding
Yueming Wang, Tianshi Zheng, Jiaxin Bai +3
Scientific discovery increasingly relies on automated systems that generate hypotheses, inspect multimodal evidence, and validate claims at scale. Yet scientific claim verification…
MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models
Xiyu Ren, Zhaowei Wang, Yiming Du +11
Memory is essential for large vision-language models (LVLMs) to handle long, multimodal interactions, with two method directions providing this capability: long-context LVLMs and m…
Can Retrieval Heads See Images? Multimodal Retrieval Heads in Long-Context Vision-Language Models
Aaron Branson Cigres Li, Zhaowei Wang, Yu Zhao +9
Large vision-language models increasingly rely on long-context modeling to reason over documents, hour-level videos, and long-horizon agent trajectories, requiring them to locate r…
MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence
Woohyeon Park, Jaeik Kim, Sunghwan Steve Cho +7
Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical Vision-Language Models (VLMs) still lack mechani…
is Theoretically Large Enough for Embedding-based Top- Retrieval
Zihao Wang, Hang Yin, Lihui Liu +4
This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of object vectors so that every subset of size at most …
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…
XToM: Exploring the Multilingual Theory of Mind for Large Language Models
Chunkit Chan, Yauwai Yim, Hongchuan Zeng +14
Theory of Mind (ToM), the ability to infer mental states in others, is pivotal for human social cognition. Existing evaluations of ToM in LLMs are largely limited to English, negle…
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
Zhaowei Wang, Wenhao Yu, Xiyu Ren +9
The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are capable of handling hundreds of ima…
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…
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…
Persona Knowledge-Aligned Prompt Tuning Method for Online Debate
Chunkit Chan, Cheng Jiayang, Xin Liu +7
Debate is the process of exchanging viewpoints or convincing others on a particular issue. Recent research has provided empirical evidence that the persuasiveness of an argument is…
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation
Zhaowei Wang, Wei Fan, Qing Zong +7
Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to…
TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining
Qing Zong, Zhaowei Wang, Baixuan Xu +6
A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining int…
Self-Consistent Narrative Prompts on Abductive Natural Language Inference
Chunkit Chan, Xin Liu, Tsz Ho Chan +4
Abduction has long been seen as crucial for narrative comprehension and reasoning about everyday situations. The abductive natural language inference (NLI) task has been propose…
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
Zhaowei Wang, Quyet V. Do, Hongming Zhang +6
Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different ca…
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport
Zihao Wang, Weizhi Fei, Hang Yin +3
Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-b…
DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition
Chunkit Chan, Xin Liu, Jiayang Cheng +4
Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse conn…
Logical Message Passing Networks with One-hop Inference on Atomic Formulas
Zihao Wang, Yangqiu Song, Ginny Y. Wong +1
Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural m…
Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
Huiru Xiao, Xin Liu, Yangqiu Song +2
The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to…
SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller
Zhaowei Wang, Hongming Zhang, Tianqing Fang +3
In this paper, we propose a new task of sub-event generation for an unseen process to evaluate the understanding of the coherence of sub-event actions and objects. To solve the pro…
PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population
Tianqing Fang, Quyet V. Do, Hongming Zhang +3
Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge…