1 citations · 1 across the 13 of their papers we have counts for
14 papers
GACC-Bench: Benchmarking Goal-Adaptive Context Compression Ability across Large Language Models
Weiting Liu, Zhiyu Pan, Shuhong Wang +7
Context compression in long-running applications of large language models (LLMs) must preserve goal-relevant information, often before specific downstream questions are known. We i…
ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
Weiting Liu, Jieyi Bi, Wanqi Zhou +4
Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to…
PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents
Jingyi Peng, Zhongwei Wan, Weiting Liu +1
Long-horizon language agents accumulate conversation history far faster than any fixed context window can hold, making memory management critical to both answer accuracy and servin…
Seizure-Semiology-Suite (S3): A Clinically Multimodal Dataset, Benchmark, and Models for Seizure Semiology Understanding
Lina Zhang, Tonmoy Monsoor, Peizheng Li +23
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in general video understanding, their capacity to interpret involuntary, and spatio-temporal…
CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments
Yi Yu, Guangquan Hu, Chenghuang Shen +15
The increasing agentic capabilities of Large Language Models (LLMs) have enabled their deployment in real-world applications, such as cloud services, where customer-assistant inter…
AndroTMem: From Interaction Trajectories to Anchored Memory in Long-Horizon GUI Agents
Yibo Shi, Jungang Li, Linghao Zhang +25
Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explored. Replaying full interaction…
Temporal Gains, Spatial Costs: Revisiting Video Fine-Tuning in Multimodal Large Language Models
Linghao Zhang, Jungang Li, Yonghua Hei +12
Multimodal large language models (MLLMs) are typically trained in multiple stages, with video-based supervised fine-tuning (Video-SFT) serving as a key step for improving visual un…
Adapting Technical-Service LLM Agents with Latent Logic Augmentation, Robust Noise Reduction, and Hybrid Reward Modeling
Junzhuo Ma, Chenghuang Shen, Yi Yu +15
Technical-service LLM agents are entering production workflows, where value depends on whether engineers adopt generated replies. Service tickets hide decision logic, contain noisy…
Yunque DeepResearch Technical Report
Yuxuan Cai, Xinyi Lai, Peng Yuan +8
Deep research has emerged as a transformative capability for autonomous agents, empowering Large Language Models to navigate complex, open-ended tasks. However, realizing its full…
Automated Optimization Modeling via a Localizable Error-Driven Perspective
Weiting Liu, Han Wu, Yufei Kuang +4
Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivot…
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
Fei Zhao, Chonggang Lu, Yue Wang +22
As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform con…
Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services
Hongcheng Guo, Zheyong Xie, Shaosheng Cao +6
As interest in using Large Language Models for interactive and emotionally rich experiences grows, virtual pet companionship emerges as a novel yet underexplored application. Exist…
Symbolic Foundation Regressor on Complex Networks
Weiting Liu, Jiaxu Cui, Jiao Hu +2
In science, we are interested not only in forecasting but also in understanding how predictions are made, specifically what the interpretable underlying model looks like. Data-driv…
SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services
Hongcheng Guo, Zheyong Xie, Shaosheng Cao +5
With the increasing integration of visual and textual content in Social Networking Services (SNS), evaluating the multimodal capabilities of Large Language Models (LLMs) is crucial…