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20232026
most citedA Survey of Personalized Large Language Models: Progress and Future Directions

8 citations · 16 across the 21 of their papers we have counts for

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Showing 2026 · cs.AIShow all

5 papers · 2 filters

cs.AI2026

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…

cs.AI2026

HUSH-Bench: Measuring Memory-Use Boundaries for Sensitive History in Conversational Agents

Lingxiang Xu, Jiaoyun Yang, Min Hu +1

Long-term memory helps conversational agents maintain continuity across sessions, while relevance and current-turn warrant remain distinct decisions. We study this boundary under a…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning

Minda Hu, Zexuan Qiu, Zenan Xu +3

Recent breakthroughs in Large Reasoning Models (LRMs) have demonstrated that extensive Chain-of-Thought (CoT) generation is critical for enabling intricate cognitive behaviors, suc…