24 citations · 29 across the 9 of their papers we have counts for
12 papers
RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention
Jiening Siow, Amy Xin, Junjie Wang +7
Autonomous research agents are increasingly used to automate large portions of scientific research, yet they are prone to research integrity violations (RIVs): behaviors that compr…
OSWorld-Science: A Benchmark of Computer Use Agents for Learning and Using Scientific Software
Dingyuan Dai, Heli Qi, Lei Liu +28
Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized int…
AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs
Fanjin Zhang, Zhengyang Wang, Ruixuan Huang +7
Large language models (LLMs) augmented with tools are emerging as autonomous agents capable of using Web engine, APIs, and code to solve complex, long-horizon tasks. Current tool-u…
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
Amy Xin, Jiening Siow, Junjie Wang +5
LLM-based agents have shown increasing potential in automating scientific discovery. Given an optimizable metric and an execution environment, they can propose, validate, and itera…
On the Paradoxical Interference between Instruction-Following and Task Solving
Yunjia Qi, Hao Peng, Xintong Shi +5
Instruction following aims to align Large Language Models (LLMs) with human intent by specifying explicit constraints on how tasks should be performed. However, we reveal a counter…
AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
Yunjia Qi, Hao Peng, Xiaozhi Wang +5
Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications. Growing research efforts aim to develop LLM-based agents to address practic…
How do Transformers Learn Implicit Reasoning?
Jiaran Ye, Zijun Yao, Zhidian Huang +8
Recent work suggests that large language models (LLMs) can perform multi-hop reasoning implicitly -- producing correct answers without explicitly verbalizing intermediate steps --…
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning
Amy Xin, Jinxin Liu, Zijun Yao +4
Despite the outstanding capabilities of large language models (LLMs), knowledge-intensive reasoning still remains a challenging task due to LLMs' limitations in compositional reaso…
Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning
Trapoom Ukarapol, Zhicheng Lee, Amy Xin
While Large Language Models show remarkable performance in natural language understanding, their resource-intensive nature makes them less accessible. In contrast, smaller language…
LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
Amy Xin, Yunjia Qi, Zijun Yao +5
Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context. However, specialized EL models stru…
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
Zijun Yao, Yuanyong Chen, Xin Lv +8
We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the know…
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
Jifan Yu, Xiaozhi Wang, Shangqing Tu +32
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticu…