From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale
Researchers propose three-layer calibration so agents keep capabilities when models, jurisdictions, or scale change.
The paper formulates agent calibration as constrained behavioral adaptation across information preservation, harness adaptation, and user-acceptance contracts. The objective is non-degradation on prespecified capability measures when a driving model is replaced, jurisdictions change, or deployment scales across markets. It distinguishes trainable policies from frozen-backbone configuration and recommends held-out evaluations, factorial tests of source evidence and checkpoint repair, and group-level reporting. Implementation and empirical validation are left as future work.
- Calibration spans information preservation, harness adaptation, and user contracts.
- Goal is non-degradation when models, jurisdictions, or scale change.
- Proposal only; implementation and empirical validation are future work.
- Calls for held-out tests and group-level reporting of local failures.
From the original article
1 min read at arxiv.orgAgents need calibration when deployment conditions change: replacing a driving model, including a foundation-to-post-trained transition; crossing jurisdictions; or scaling across heterogeneous markets and sources. Interface compatibility alone does not establish capability retention or target-contract satisfaction.
By Yaxiao Liu · arxiv.org
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