Skip to content
Back to skills

Inference Time Scaling Of Verification Self Evolvi

ASecurity

Implement techniques from Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics

  • 6 stars
  • 0 votes
  • 0 copies
  • 2 views
  • Added September 9, 2026
research

Security analysis

A100/100

Scanned September 9, 2026

npx -y skills add ADu2021/skillXiv --skill inference-time-scaling-of-verification-self-evolvi --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Inference Time Scaling Of Verification Self Evolvi?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Inference Time Scaling Of Verification Self Evolvi
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/adu2021-inference-time-scaling-of-verification-self-evolvi/badge)](https://www.skillsdirectory.com/skills/adu2021-inference-time-scaling-of-verification-self-evolvi)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: inference-time-scaling-of-verification-self-evolvi
title: "Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.15808"
keywords: ["agent"]
description: "Implement techniques from Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics"
---

## Overview

This skill implements concepts from the research paper [[2601.15808](https://arxiv.org/abs/2601.15808)].

## When to Use

- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology

## When NOT to Use

- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain

## Key Concepts

The paper addresses: Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent's ability by iteratively ver...

For detailed methodology and implementation details, refer to the [full paper](https://arxiv.org/html/2601.15808).

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.