ai-coscientist

Train LLMs to generate high-quality research plans via rubric-based RL without requiring experimental verification. Extracts research goals and domain-specific rubrics from scientific papers, uses frozen model as grader with 12-22% relative improvements, achieves human-expert preference 70% of time with strong cross-domain generalization.

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Dépôt: ADu2021/skillXiv
Dernière activité de la source: 24 mars 2026 à 19:42
Langue détectée de SKILL.md: anglais
Étoiles: 6
Forks: 0

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npx skills add https://github.com/ADu2021/skillXiv --skill ai-coscientist

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SKILL.md

Instructions source · Aperçu en lecture seule

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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

Tout voir dans ce dépôt

name	ai-coscientist
title	Training AI Co-Scientists Using Rubric Rewards
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.23707
keywords	["research","reinforcement-learning","rubric-rewards","cross-domain"]
description	Train LLMs to generate high-quality research plans via rubric-based RL without requiring experimental verification. Extracts research goals and domain-specific rubrics from scientific papers, uses frozen model as grader with 12-22% relative improvements, achieves human-expert preference 70% of time with strong cross-domain generalization.

Overview

Automated research plan generation using extractable domain knowledge from papers.

Core Technique

Rubric Extraction and Grading:

# Extract from papers
research_rubrics = extract_rubrics_from_papers(papers)

# Grade via frozen model
grade = frozen_model.score(generated_plan, rubrics)

# GRPO training on grade signal

When to Use

Use when: Research automation, domain-specific planning, cross-domain generalization.

References

Rubric extraction from scientific papers
Frozen model as grader
Self-reward GRPO training

ai-coscientist

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Overview

Core Technique

When to Use

References

Overview

Core Technique

When to Use

References