1 citations · 1 across the 10 of their papers we have counts for
10 papers
EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering
Ziliang Zhao, Zenan Xu, Shuting Wang +6
In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier information, changing what remains va…
Agents in the Large: Perception-Centered Architecture for Persistent Agents
Shihan Dou, Haoxiang Jia, Shichun Liu +14
Cognitive language agents have achieved substantial progress by equipping language models with memory, tools, and decision-making procedures, enabling agents to reason and act in i…
Certified Multi-Turn Robustness for LLM Safety via Compositional Bounds and Safety Persistence
Yang Liu, Bin Chong, Wenkai Yang +9
Large language models (LLMs) are vulnerable to multi-turn jailbreak attacks that progressively manipulate conversation context. Existing certified robustness methods are limited to…
SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information
Junjie Ye, Zhuohui Sheng, Shaofan Liu +12
Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps)…
PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models
Ziliang Zhao, Zenan Xu, Shuting Wang +7
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequen…
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
A Decomposition Perspective to Long-context Reasoning for LLMs
Yanling Xiao, Huaibing Xie, Guoliang Zhao +8
Long-context reasoning is essential for complex real-world applications, yet remains a significant challenge for Large Language Models (LLMs). Despite the rapid evolution in long-c…
Probing How Scalable Table Data Enhances General Long-Context Reasoning
Huaibing Xie, Guoliang Zhao, Yang Liu +8
As real-world tasks grow increasingly complex, long-context reasoning has become a core capability for Large Language Models (LLMs). However, few studies explore which data types a…
Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration
Bowei He, Minda Hu, Zenan Xu +7
Search-integrated reasoning enables language agents to transcend static parametric knowledge by actively querying external sources. However, training these agents via reinforcement…
CL-bench: A Benchmark for Context Learning
Shihan Dou, Ming Zhang, Zhangyue Yin +24
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…