smartsnap-agents

Shift agent verification from post-hoc external judgment to proactive in-situ self-evidence curation. Agents generate atomic evidence tuples during execution, guided by 3C principles (Completeness, Conciseness, Creativity), with structured verifier feedback across four dimensions—reducing verification costs and enabling dense learning signals.

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Repository: ADu2021/skillXiv
Last source activity: March 24, 2026 at 19:42
Detected SKILL.md language: English
Stars: 6
Forks: 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	smartsnap-agents
title	SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.22322
keywords	["agents","verification","self-improvement","evidence-gathering"]
description	Shift agent verification from post-hoc external judgment to proactive in-situ self-evidence curation. Agents generate atomic evidence tuples during execution, guided by 3C principles (Completeness, Conciseness, Creativity), with structured verifier feedback across four dimensions—reducing verification costs and enabling dense learning signals.

Overview

SmartSnap enables agents to prove their success rather than waiting for external verification.

Core Technique

3C Evidence Principles:

evidence = agent.gather_evidence(
    completeness=include_all_pivotal_actions,
    conciseness=minimize_redundancy,
    creativity=generate_additional_proof_actions
)

Evidence Definition: Atomic (action, observation) tuples—objective facts.

When to Use

Use when: Agent verification critical, reducing cognitive load, dense learning signals.

References

Proactive evidence gathering
Atomic evidence definition
Structured verifier feedback

smartsnap-agents

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More from this repository

More from this repository

Overview

Core Technique

When to Use

References

Overview

Core Technique

When to Use

References