[Submitted on 29 Oct 2026]
Title:Certified Behavioral Retention for Continual Adaptation with Held-Out Anchors
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Abstract:Continual adaptation requires deciding not only how to update a model, but whether to commit an update without exceeding a declared budget for changes to protected behavior. We introduce Certified Anchor-Based Adaptation (CABA), a candidate-agnostic post-update gate that reserves historical observations from candidate optimization, compares old and candidate policies on frozen representations, and selects the largest tested interpolation coefficient whose worst-anchor forward KL divergence remains within budget. The finite check requires neither action labels nor complete trajectories. We develop a theory-led certification framework that separates measured anchor retention from conditional off-anchor guarantees. Under explicit representation-coverage and Lipschitz assumptions, an accepted update satisfies a uniform behavioral-divergence bound on protected support. The analysis gives regularity bounds for affine softmax heads, probabilistic coverage and compression guarantees, interpolation monotonicity conditions, grid-resolution bounds, and trajectory-level and sequential-composition guarantees. In a controlled UCI Human Activity Recognition study used as a non-stationary sensor-policy proxy, CABA reduces mean forgetting from 1.01 percent for unconstrained adaptation to 0.00 percent at budget epsilon = 0.05, while reducing final average accuracy from 93.04 percent to 88.48 percent. The experiment illustrates a retention-plasticity trade-off and does not establish foundation-model or robotic performance. CABA provides an auditable retention check for candidate generators exposing comparable policy distributions and a declared update path, while deployment also requires separate evidence of current-task utility.
Comments: 11 pages, 1 figure, 3 tables. Accepted at the NeurIPS 2026 Workshop on Continual Learning in the Era of Foundation Models and Embodied Agents (CL4M / CL4FMAgents)
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
MSC classes: 68T05, 68T07
ACM classes: I.2.6; I.2.8
Cite as: arXiv:2610.40740 [cs.LG]
(or arXiv:2610.40740v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.40740
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ahanaf Hasan Ariq [view email] [v1] Thu, 29 Oct 2026 15:08:12 UTC (21 KB)
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