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
Scanned 9/9/2026
Install to Claude Code
npx -y skills add ADu2021/skillXiv --skill inference-time-scaling-of-verification-self-evolvi --agent claude-codeInstalls into .claude/skills of the current project.
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---
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).
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