Papers by Jingyu Wu
MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for retrieval-based in-context learning ignore model biases and fail to retrieve the most appropriate demonstrations for different LLMs. |
| Approach: | They propose a model-specific demonstration retrieval method that considers the biases of different LLMs at inference time. |
| Outcome: | The proposed method improves performance on seen and unseen tasks with multi-scale inference LLMs by up to 41.2%. |
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems (2026.eacl-long)
Copied to clipboard
Chenyang Zhu, Spencer Hong, Jingyu Wu, Kushal Chawla, Yuhui Tang, Youbing Yin, Nathan Wolfe, Erin Babinsky, Daben Liu
| Challenge: | Existing evaluation frameworks focus on simple metrics and end-to-end outcomes, but they struggle with longer contexts. |
| Approach: | They propose an offline evaluation architecture that incorporates iterative reasoning to evaluate the quality of the candidate faults and rationales of the Judge. |
| Outcome: | The proposed architecture outperforms baseline evaluation frameworks with two datasets to identify step-level faults in multi-agent systems and ReasonEval datasets. |
Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation (2025.emnlp-industry)
Copied to clipboard
| Challenge: | RAG systems often generate inconsistent outputs for semantically equivalent inputs . this unpredictability undermines the reliability of RAG and poses challenges for adoption in high-stakes or knowledge-sensitive domains such as finance, healthcare, and scientific research. |
| Approach: | They propose a method that integrates knowledge from specialized models into a single model to improve output consistency. |
| Outcome: | The proposed model significantly improves output consistency, achieving approximately 47.5% improvement in response similarity over baseline. |
JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents (2026.acl-long)
Copied to clipboard
Ada Chen, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang, Shuai Wang
| Challenge: | Recent advances in computer-using agents have created new safety and security risks . despite the impressive capabilities of CUAs, there are still significant security risks. |
| Approach: | They propose a systematization of knowledge on the safety and security threats of Computer-Using Agents. |
| Outcome: | The proposed framework provides a framework for assessing the safety and security risks of computer-using agents. |
Do not Abstain! Identify and Solve the Uncertainty (2025.acl-long)
Copied to clipboard
| Challenge: | Existing solutions rely on evasive responses when confronting uncertain scenarios. |
| Approach: | They propose a benchmark to assess LLMs' ability to recognize and address uncertainty . they generate context-aware inquiries that highlight the confusing aspect of the original query . |
| Outcome: | Experiments with ConfuseBench show that LLMs struggle to identify root cause of uncertainty and solve it. |
Beyond Static Profiles: Capturing the Fluidity of User Preferences in Diverse Scenarios (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to personalize Large Language Models often default to homogeneous behaviors . preferences can shift, and conflict, depending on context, authors argue . |
| Approach: | They propose a hierarchical taxonomy to differentiate between stable and situational preferences . they use a dataset of 10k meticulously curated preferences to test their taxonomies . |
| Outcome: | The proposed model differentiates between stable and situational preferences based on curated user preferences . it provides a practical testbed for advancing dynamic, context-aware personalization in conversational agents. |
JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models (2026.findings-acl)
Copied to clipboard
Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia
| Challenge: | Currently, evaluation criteria and methods used for jailbreak effectiveness are inconsistent. |
| Approach: | They propose a framework to measure jailbreak effectiveness using a model that filters out jailbreak noise while preserving the original malicious question. |
| Outcome: | The proposed framework outperforms existing evaluation methods on a challenging benchmark containing 330 human-labeled, non-rejected jailbreak instances. |
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation (2026.findings-eacl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is a common technique for grounding language models in domain-specific information. |
| Approach: | They propose a new retrieval technique that incorporates diversity into the retrieval step to improve performance on reasoning-intensive QA benchmarks. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive QA benchmarks by 4–10%. |
Thinking Alignment of Scenario-Oriented User Simulation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing user simulators based on prompting to role-play or SFT focus on imitating textual utterances without considering multi-faceted cognitive processes that underlie human decision-making during interactions. |
| Approach: | They construct a user-simulator dataset that augments 51k human–LLM conversations by reconstructing the user’s inner reasoning during and at the end of each dialogue. |
| Outcome: | The proposed user simulators augment 51k human–LLM conversations by reconstructing the user’s inner reasoning both during and at the end of each dialogue. |