7 citations · 36 across the 24 of their papers we have counts for
27 papers
Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents
Yutong Cheng, Wei Cheng, Wenchao Yu +2
A self-evolving agent rewrites its own harness and must decide which rewrites to keep, so its improvement signal is the check a rewrite has to pass. Verifying a rewrite by agent ro…
Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems
Xu Zheng, Zhuomin Chen, Chaohao Lin +4
Large Language Model~(LLM)-based agents have demonstrated exceptional performance across a wide range of complex interactive tasks. However, they often struggle with long-horizon i…
Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs
Xu Zheng, Chaohao Lin, Zhuomin Chen +4
Recent advancements in inference-time scaling have significantly unlocked the complex reasoning capabilities of Large Language Models~(LLMs). However, for agents, these approaches…
Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning
Renliang Sun, Wei Cheng, Dawei Li +2
Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or red…
Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
Cong Zeng, Shengkun Tang, Yuanzhou Chen +6
The rapid advancement of large language models (LLMs) such as ChatGPT, DeepSeek, and Claude has significantly increased the presence of AI-generated text in digital communication.…
Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation
Haoyue Bai, Haoyu Wang, Shengyu Chen +5
Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-dat…
Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework
Xu Zheng, Zhuomin Chen, Esteban Schafir +7
Despite the success of machine-generated text detectors, the black-box nature remains a critical limitation. Traditional explainability methods rely on token-level saliency, insuff…
Where's the liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content
Haoyue Bai, Yiyou Sun, Wei Cheng +1
The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real…
TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
Geon Lee, Wenchao Yu, Kijung Shin +2
Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associat…
TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
Yushan Jiang, Wenchao Yu, Geon Lee +5
Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual…
MixLLM: Dynamic Routing in Mixed Large Language Models
Xinyuan Wang, Yanchi Liu, Wei Cheng +5
Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency. Given mixed LLMs with their own…
DISC: Dynamic Decomposition Improves LLM Inference Scaling
Jonathan Light, Wei Cheng, Benjamin Riviere +6
Inference scaling methods for LLMs often rely on decomposing problems into steps (or groups of tokens), followed by sampling and selecting the best next steps. However, these steps…
On the Effect of Sampling Diversity in Scaling LLM Inference
Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5
Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…
Harnessing Vision Models for Time Series Analysis: A Survey
Jingchao Ni, Ziming Zhao, ChengAo Shen +5
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).…
Scattered Forest Search: Smarter Code Space Exploration with LLMs
Jonathan Light, Yue Wu, Yiyou Sun +6
We frame code generation as a black-box optimization problem within the code space and demonstrate how optimization-inspired techniques can enhance inference scaling. Based on this…
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
Yuyang Ye, Lu-An Tang, Haoyu Wang +5
Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity fo…
DALD: Improving Logits-based Detector without Logits from Black-box LLMs
Cong Zeng, Shengkun Tang, Xianjun Yang +7
The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human…
Parametric Augmentation for Time Series Contrastive Learning
Xu Zheng, Tianchun Wang, Wei Cheng +4
Modern techniques like contrastive learning have been effectively used in many areas, including computer vision, natural language processing, and graph-structured data. Creating po…
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
Fali Wang, Runxue Bao, Suhang Wang +4
Large Language Models (LLMs) have achieved exceptional capabilities in open generation across various domains, yet they encounter difficulties with tasks that require intensive kno…
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton
Yiyou Sun, Junjie Hu, Wei Cheng +1
This paper introduces the retrieval-augmented large language model with Definite Finite Automaton (DFA-RAG), a novel framework designed to enhance the capabilities of conversationa…
DyExplainer: Explainable Dynamic Graph Neural Networks
Tianchun Wang, Dongsheng Luo, Wei Cheng +2
Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box…
A Survey on Detection of LLMs-Generated Content
Xianjun Yang, Liangming Pan, Xuandong Zhao +4
The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of s…
Zero-Shot Detection of Machine-Generated Codes
Xianjun Yang, Kexun Zhang, Haifeng Chen +3
This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge…
Distantly-Supervised Joint Extraction with Noise-Robust Learning
Yufei Li, Xiao Yu, Yanghong Guo +3
Joint entity and relation extraction is a process that identifies entity pairs and their relations using a single model. We focus on the problem of joint extraction in distantly-la…