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Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.
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Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.
Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.
Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.
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SKILL.md を表示中
| name | digital-twin-ai-opportunities-and-challenges-from |
| title | Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.01321 |
| keywords | ["llm"] |
| description | Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation. |
This skill is based on the research paper "Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models" (arXiv:2601.01321). It demonstrates advanced techniques for improving agent capabilities and reasoning.
Research-driven approaches to enhancing autonomous agent performance, reasoning quality, and system integration across diverse domains.
The paper presents novel methodologies and frameworks for:
The research contributes to the field by addressing:
For detailed implementation guidance, see the original paper at https://arxiv.org/html/2601.01321 or https://arxiv.org/pdf/2601.01321.pdf.
