# Inference Time Scaling Of Verification Self Evolvi

> 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

- Skill: `adu2021/inference-time-scaling-of-verification-self-evolvi` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/inference-time-scaling-of-verification-self-evolvi`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/inference-time-scaling-of-verification-self-evolvi/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/inference-time-scaling-of-verification-self-evolvi

---


## 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).