arXiv — cs.AI preprintsInternational9 October 2026
Distillation for Incrimination and Distillation for Capabilities
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.11012v1 Announce Type: new Abstract: Powerful misaligned AI models might recognize alignment evaluations and strategically behave well on them, rendering direct audits uninformative. However, distilling such a model into a weaker benign student places the teacher in a Distillation Double Bind: if misalignment transfers, the student may conceal it less effectively, revealing evidence about the teacher; if it does not, the student may learn useful capabilities while remaining benign. We introduce two distinct distillation approaches, one targeting each outcome. Distillation for Incrim
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment9 October 2026
- Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction9 October 2026
- Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice9 October 2026
- Verification and Self-Improvement in Agentic AI: Foundations and Limits9 October 2026
- The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate9 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.