11 citations · 15 across the 22 of their papers we have counts for
23 papers
SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
Kuan Li, Shuo Zhang, Huacan Wang +12
Smart homes are evolving toward complex state-dependent living environments, requiring Large Language Models (LLMs) to reason over user intent, preferences, and multi-device intera…
HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
Yi Gu, Huacan Wang, Shuo Zhang +10
Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain. Real domestic interaction requires u…
SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents
Yifan Zhou, Zhentao Zhang, Ziming Cheng +8
As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, re…
EpochX: Building the Infrastructure for an Emergent Agent Civilization
Huacan Wang, Chaofa Yuan, Xialie Zhuang +15
General-purpose technologies reshape economies less by improving individual tools than by enabling new ways to organize production and coordination. We believe AI agents are approa…
Controlled Self-Evolution for Algorithmic Code Optimization
Tu Hu, Ronghao Chen, Shuo Zhang +9
Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discove…
Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory
Sen Hu, Yuxiang Wei, Jiaxin Ran +5
Graph structures are increasingly used in dialog memory systems, but empirical findings on their effectiveness remain inconsistent, making it unclear which design choices truly mat…
EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines
Shuo Zhang, Chaofa Yuan, Ryan Guo +11
While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work…
QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
Jun Han, Shuo Zhang, Wei Li +14
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to backtest noise and regime shifts. While recent agentic frameworks improve automation, they o…
MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences
Qihao Wang, Ziming Cheng, Shuo Zhang +12
While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratc…
CloneMem: Benchmarking Long-Term Memory for AI Clones
Sen Hu, Zhiyu Zhang, Yuxiang Wei +4
AI Clones aim to simulate an individual's thoughts and behaviors to enable long-term, personalized interaction, placing stringent demands on memory systems to model experiences, em…
RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction
Haonan Bian, Zhiyuan Yao, Sen Hu +7
As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long-term consistency. However, exist…
Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning
Chengwen Liu, Xiaomin Yu, Zhuoyue Chang +15
In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore ne…
KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
Tingyu Wu, Zhisheng Chen, Ziyan Weng +8
Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding.…
Octopus: Agentic Multimodal Reasoning with Six-Capability Orchestration
Yifu Guo, Zishan Xu, Zhiyuan Yao +6
Existing multimodal reasoning models and frameworks suffer from fundamental architectural limitations: most lack the human-like ability to autonomously explore diverse reasoning pa…
GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging
Ziyi Ni, Huacan Wang, Shuo Zhang +15
Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evalua…
SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents
Jiaye Lin, Yifu Guo, Yuzhen Han +11
Large Language Model (LLM)-based agents have recently shown impressive capabilities in complex reasoning and tool use via multi-step interactions with their environments. While the…
RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving
Huacan Wang, Ziyi Ni, Shuo Zhang +11
The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks t…
Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style
Xueran Han, Yuhan Liu, Mingzhe Li +5
Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, sim…
Are LLM-based Evaluators Confusing NLG Quality Criteria?
Xinyu Hu, Mingqi Gao, Sen Hu +4
Some prior work has shown that LLMs perform well in NLG evaluation for different tasks. However, we discover that LLMs seem to confuse different evaluation criteria, which reduces…
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
Qiang Li, Xiaoyan Yang, Haowen Wang +14
Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However,…
S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering
Baokui Li, Sen Zhang, Wangshu Zhang +6
Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQ…
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation
Junjie Wang, Yicheng Chen, Wangshu Zhang +3
Leveraging knowledge from multiple tasks through introducing a small number of task specific parameters into each transformer layer, also known as adapters, receives much attention…
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Xianpei Han, Zhichun Wang, Jiangtao Zhang +22
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation…