Making Agents More Consistent: Skills Should Form Habits for Repeat Tasks
arXiv cs.AIen
arXiv:2609.25299v1 Announce Type: new Abstract: On repeated work, agents are inconsistent. We ran 42 tasks three times each and found that, depending on the model, 38% to 74% returned answers that did not agree. Consistency is what a buyer, an auditor, or a regulator requires, and agents do not have it. They are wasteful too: 95.3% to 97.2% of what an agent generates goes to re-deriving a plan the system already knows. We propose skill habit formation. An agent mines its own execution history for candidate skills, deterministic variants that compete against the incumbent rather than replacing it. A candidate declares the region of input space it claims, so the common case runs as a script an
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
- Agenter
Related AI news
- They built AI agents on WhatsApp. Then Meta entered the chatTech in Asia · September 23, 2026
- 4DGS-JEPA: Temporally Compositional Joint-Embedding Prediction for Dynamic Gaussian SplattingarXiv cs.AI · September 23, 2026
- Lean Pool: An AI-Maintained Archive of Formalized MathematicsarXiv cs.AI · September 23, 2026
- Real-Time Hand Gesture Recognition for OpenXR Using Transformer-Based Machine LearningarXiv cs.AI · September 23, 2026
- X-Planner: Event-Structured Task Planning for Embodied IntelligencearXiv cs.AI · September 23, 2026
- Recovering Agentic Sovereignty: Mitigating the Consensus Paradox via Contrastive Epistemic DecodingarXiv cs.AI · September 23, 2026