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The paper evaluates a recurrent network that learns monotone temporal aggregation for risk scoring, comparing it to hand‑crafted monotone aggregates used in deployed pipelines. Functional regression shows the learned channel can reproduce the geometrically weighted separable family of engineered statistics with Spearman ρ ≥ 0.996, while it struggles with consecutivity, time localization, and exposure floor, all of which can be fixed with an admissible basis. In practice, the learned aggregator matches or exceeds engineered counterparts in full‑sample scenarios, offers modest AUC gains in certain decay regions, and maintains performance where tree‑ensemble scores flatten.
arXiv:2606. 17572v1 Announce Type: new Abstract: Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector.
arXiv:2606. 15621v1 Announce Type: new Abstract: Per-token counterfactual credit estimation asks which token in a language-model rollout caused the final answer to be right or wrong: cut the transcript at a pivot, substitute an alternative token, replay continuations, and compare outcomes.
arXiv:2608. 08202v1 Announce Type: new Abstract: Data-centric curation pipelines frequently rely on model confidence scores to flag and filter noisy or mislabeled training instances.
arXiv:2608. 19760v1 Announce Type: cross Abstract: Audited against causal ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signals used to train LLM agents -- LLM-judge scores, outcome-conditioned logprob ratios, or the policy's own confidence -- identifies which steps causally matter better than chance.
arXiv:2608. 08126v1 Announce Type: new Abstract: Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters, in tension with supervisory expectations that adverse decisions be explainable.