3 citations · 4 across the 13 of their papers we have counts for
17 papers
DecepEval: A Benchmark for Evaluating Deception in LLM Agents
Yiming Xu, Hongyue Yu, Beihua Yang +8
As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing…
Spec2Game: Can LLMs Generate Complete Playable Games from Detailed Specifications?
Yixue Cai, Yuzhe Zhao, Hanxiang Chao +4
Generating an executable program does not necessarily mean that it correctly implements the behavioral requirements specified in natural language. To evaluate large language models…
StepKV: Step-Aware KV Cache Compression for LLM Agents
Boyu Feng, Jiahong Liu, Yifan Li +7
Key-value (KV) caching is essential for efficient autoregressive large language model (LLM) inference, but the cache grows linearly with context length, increasing storage and deco…
Think Before You Comfort: Reflective Cognitive Alignment for Protocol-Grounded Elderly Stimulation Agents
Jiyue Jiang, Ziyi Li, He Hu +9
Cognitive Stimulation Therapy (CST) offers non-pharmacological support for elders with cognitive impairment, yet scalability remains constrained by reliance on trained facilitators…
Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning
Jinhu Qi, Minda Hu, Wentao Zhang +4
Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output schema for a series of questio…
TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
Jinhu Qi, Wentao Zhang, Siu Man Ng +4
Travel planning is a demanding stress test for tool-using LLM agents: a usable itinerary is a single artifact that must be right along many axes at once - every flight, hotel, and…
From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents
Yifan Li, Shengbin Yue, Boyu Feng +6
The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting se…
LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition
Yanyu Chen, Jiyue Jiang, Dianzhi Yu +8
The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, m…
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Jinhu Qi, Muzhi Li, Jiahong Liu +9
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their mul…
Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI
Jinhu Qi, Yifan Li, Minghao Zhao +4
Agentic AI systems increasingly act through tool-augmented, multi-step workflows whose failures (unsafe tool use, unauthorised actions, social harm) carry deployment-level conseque…
TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents
Yanyu Chen, Jiyue Jiang, Jiahong Liu +3
The evaluation of Deep Research Agents is a critical challenge, as conventional outcome-based metrics fail to capture the nuances of their complex reasoning. Current evaluation fac…
ADRA-Bank: A Modular Benchmark for Academic Deep Research Agents
Zhihan Guo, Feiyang Xu, Yifan Li +7
A surge in academic publications calls for automated deep research (DR) systems, but accurately evaluating them is still an open problem. First, existing benchmarks often focus nar…
From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development
Muzhi Li, Jinhu Qi, Yihong Wu +9
Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories. Existing datasets provide ques…
Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Yihong Wu, Liheng Ma, Muzhi Li +7
Large Language Models (LLMs) equipped with modern Retrieval-Augmented Generation (RAG) systems often employ multi-turn interaction pipelines to interface with search engines for co…
3D-MoE: Towards Spatial Intelligence with Mixture-of-Experts for 3D Reasoning and Action Generation
Yueen Ma, Zenglin Xu, Yuzheng Zhuang +2
Spatial intelligence, encompassing 3D perception and reasoning, is the essential next frontier of AI. Scaling current 3D vision-language models (VLMs) that rely on dense Transforme…
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
Muzhi Li, Cehao Yang, Chengjin Xu +5
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vu…
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Muzhi Li, Cehao Yang, Chengjin Xu +5
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as…