| 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].
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.
