HeavySkill: Inner Skill for Agentic Harness Outperforms Best-of-N
A recent study published on arXiv (2605.02396) presents HeavySkill, a concept that reinterprets heavy thinking as an intrinsic capability embedded within a model's parameters, rather than merely a component in agentic orchestration. The researchers outline a two-step process—parallel reasoning followed by summarization—as the fundamental mechanism that enhances performance in complex reasoning tasks. Results from various domains indicate that this inner skill consistently surpasses conventional Best-of-N (BoN) approaches, with more advanced LLMs nearing Pass@N performance. This research challenges the belief that sophisticated system architectures are the key contributors to success in multi-agent orchestration systems.
Key facts
- HeavySkill is introduced as a perspective on agentic harness.
- Heavy thinking is viewed as an inner skill internalized in model parameters.
- The skill operates as a two-stage pipeline: parallel reasoning then summarization.
- HeavySkill outperforms traditional Best-of-N (BoN) strategies.
- Stronger LLMs can approach Pass@N performance using HeavySkill.
- The paper is published on arXiv with ID 2605.02396.
- The study covers diverse domains.
- The work suggests that underlying mechanisms, not just system design, drive performance.
Entities
Institutions
- arXiv