4 citations · 12 across the 23 of their papers we have counts for
25 papers
JockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making
Zhongwei Xie, Xingsu Li, Jingqiao Yu +8
Repeated event forecasting requires extracting predictive signals from noisy evidence and evaluating their value across successive decisions. We introduce JockeyBench, a benchmark…
Finding the Right Evidence: Factor-Guided Coarse-to-Fine Reasoning for Long Videos
Baixuan Xu, Yinyui Xu, Tianshi Zheng +9
While LVLMs rapidly improve, long-video question answering still remains challenging: relevant evidence is sparse, and question-relevant context often fails to provide cues that di…
Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Design
Qing Zong, Jiayu Liu, Junhao Shen +9
Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedb…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
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…
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…
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…
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…
ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty
Qing Zong, Zhaowei Wang, Tianshi Zheng +2
The rapid development of LLMs has sparked extensive research into their factual knowledge. Current works find that LLMs fall short on questions around low-frequency entities. Howev…
What Really is Commonsense Knowledge?
Quyet V. Do, Junze Li, Tung-Duong Vuong +3
Commonsense datasets have been well developed in Natural Language Processing, mainly through crowdsource human annotation. However, there are debates on the genuineness of commonse…
Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction
Kaiqiao Han, Tianqing Fang, Zhaowei Wang +2
While Large Language Models (LLMs) have showcased remarkable proficiency in reasoning, there is still a concern about hallucinations and unreliable reasoning issues due to semantic…
DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes
Zhaowei Wang, Hongming Zhang, Tianqing Fang +6
Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. However, their potential to comprehend e…
CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting
Haobo Li, Zhaowei Wang, Jiachen Wang +3
Forecasting weather and climate events is crucial for making appropriate measures to mitigate environmental hazards and minimize losses. However, existing environmental forecasting…
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
Qiaolong Cai, Zhaowei Wang, Shizhe Diao +2
With the increasing popularity of large language models (LLMs), reasoning on basic graph algorithm problems is an essential intermediate step in assessing their abilities to proces…
KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining
Zihao Zheng, Zhaowei Wang, Qing Zong +1
Dialogical Argument Mining(DialAM) is an important branch of Argument Mining(AM). DialAM-2024 is a shared task focusing on dialogical argument mining, which requires us to identify…
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…
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases
Quyet V. Do, Tianqing Fang, Shizhe Diao +2
Reasoning over Commonsense Knowledge Bases (CSKB), i.e. CSKB reasoning, has been explored as a way to acquire new commonsense knowledge based on reference knowledge in the original…
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning
Weiqi Wang, Tianqing Fang, Chunyang Li +9
The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar s…
AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph
Zhaowei Wang, Haochen Shi, Weiqi Wang +5
Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present AbsPyramid, a u…
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection
Zheye Deng, Weiqi Wang, Zhaowei Wang +2
Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly. As a result, various automatic methods ha…
KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Sehyun Choi, Tianqing Fang, Zhaowei Wang +1
Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities. However, their potential to generate misinformation, often called th…
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…
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…
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
Tianqing Fang, Zhaowei Wang, Wenxuan Zhou +3
Event temporal reasoning aims at identifying the temporal relations between two or more events from narratives. However, knowledge conflicts arise when there is a mismatch between…