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Yuxuan Liu

المنشورات 9

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SkillCycle: Co-Evolving Agent Policies and Skill Banks

Ling Li, Qiuyu Shen, Zheng Jiang وآخرون · 2026

Internalizing external skills changes a language agent's capabilities and, with them, the value of its remaining guidance: rules can become redundant, misleading, or insufficient for newly encountered decisions. This creates a coupled problem of learning from skills and adapting the skills that supervise further learni …

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You Changed Your Mind, The Model Didn't: Demystifying Intent in Multi-Turn Dialogue

Junle Chen, Wei Chen, Zhengjun Huang وآخرون · 2026

When a large language model handles a multi-turn task and a user proposes a change but ultimately rejects it, the model should continue as if nothing changed. We find a surprising failure: merely mentioning a rejected change can derail task execution, even when the user's final intent remains unchanged. To systematical …

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SkillScriptBench: Benchmarking Self-Evolution of Executable Agent Skill Packages Beyond Markdown

Yuxuan Liu, Haoran Li, Yuhao Zhang وآخرون · 2026

Executable Agent Skills combine natural-language instructions and scripts into reusable packages for LLM agents, and revising them requires fixing errors without breaking correct behavior. Existing benchmarks do not systematically distinguish documentation repair, script repair, and preservation when evaluating skill s …

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Verifiable quantum advantage based on polynomials with planted structures

A central question in the theory of quantum advantage is whether there are quantum advantage protocols with similar resource requirements as random circuit sampling that are also verifiable just from the classical outputs of the quantum computation. Here, we develop the idea of simulation secrets for verifiable advanta …

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VISTA: Internalizing Collective Visual Experience via On-Policy Distillation for Active Multimodal Agents

Zheng Jiang, Houde Qian, Yiming Chen وآخرون · 2026

Active multimodal agents use visual tools to acquire task-relevant evidence while reasoning. Although reinforcement learning samples multiple interaction trajectories per input, outcome-based objectives primarily use the group to estimate scalar advantages, leaving complementary visual discoveries underused. We introdu …

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CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning

Wenbin Hu, Huihao Jing, Haochen Shi وآخرون · 2026

Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing rewards across rollouts of the same prompt. For multiple rewards, GRPO sums the reward components and normalizes the total reward by its within-group standard deviation. …

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How Far Do Persona Effects Generalize in Language Models?

Yufan Zhou, Yuxuan Liu, Enze Ma وآخرون · 2026

Persona prompts ask language models to answer as particular kinds of people. We test whether relationships learned from these effects predict responses to new questions and remain useful across models and prompts. Across 57 attributes, three behavioral domains, and seven pairs of open 7 to 9B checkpoints, persona effec …

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DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

DeepSeek-AI, Anyi Xu, B. Li وآخرون · 2026

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Togeth …

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