paper2rebuttal-a-multi-agent-framework-for

Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce $ extbf{RebuttalAgent}$, the first multi-agents framework that reframes rebuttal generation as an evidence-centric ...

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リポジトリ: ADu2021/skillXiv
ソースの最終更新活動: 2026年3月24日 19:42
検出された SKILL.md の言語: 英語
スター: 6
フォーク: 0

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meaningful-kebab-case-name

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.

2026-03-26

action-quantization-behavior-cloning

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.

2026-03-26

adaptive-lora-personalized-ranks

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.

2026-03-26

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name	paper2rebuttal-a-multi-agent-framework-for
title	Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Generation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.14171
keywords	["Agent"]
description	Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce $ extbf{RebuttalAgent}$, the first multi-agents framework that reframes rebuttal generation as an evidence-centric ...

name	paper2rebuttal-a-multi-agent-framework-for
title	Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Generation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.14171
keywords	["Agent"]
description	Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce $ extbf{RebuttalAgent}$, the first multi-agents framework that reframes rebuttal generation as an evidence-centric ...

paper2rebuttal-a-multi-agent-framework-for

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