{"ID":2853831,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15222","arxiv_id":"2510.15222","title":"Stress-Aware Learning under KL Drift via Trust-Decayed Mirror Descent","abstract":"We study sequential decision-making under distribution drift. We propose entropy-regularized trust-decay, which injects stress-aware exponential tilting into both belief updates and mirror-descent decisions. On the simplex, a Fenchel-dual equivalence shows that belief tilt and decision tilt coincide. We formalize robustness via fragility (worst-case excess risk in a KL ball), belief bandwidth (radius sustaining a target excess), and a decision-space Fragility Index (drift tolerated at $O(\\sqrt{T})$ regret). We prove high-probability sensitivity bounds and establish dynamic-regret guarantees of $\\tilde{O}(\\sqrt{T})$ under KL-drift path length $S_T = \\sum_{t\\ge2}\\sqrt{{\\rm KL}(D_t|D_{t-1})/2}$. In particular, trust-decay achieves $O(1)$ per-switch regret, while stress-free updates incur $Ω(1)$ tails. A parameter-free hedge adapts the tilt to unknown drift, whereas persistent over-tilting yields an $Ω(λ^2 T)$ stationary penalty. We further obtain calibrated-stress bounds and extensions to second-order updates, bandit feedback, outliers, stress variation, distributed optimization, and plug-in KL-drift estimation. The framework unifies dynamic-regret analysis, distributionally robust objectives, and KL-regularized control within a single stress-adaptive update.","short_abstract":"We study sequential decision-making under distribution drift. We propose entropy-regularized trust-decay, which injects stress-aware exponential tilting into both belief updates and mirror-descent decisions. On the simplex, a Fenchel-dual equivalence shows that belief tilt and decision tilt coincide. We formalize robus...","url_abs":"https://arxiv.org/abs/2510.15222","url_pdf":"https://arxiv.org/pdf/2510.15222v1","authors":"[\"Gabriel Nixon Raj\"]","published":"2025-10-17T01:08:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"math.ST\"]","methods":"[]","has_code":false}