arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition c...
Signal‑Routed Temperature Scaling (SRTS‑BCE) is a 10‑parameter, argmax‑preserving calibration method that separates calibration objectives from adaptive capacity. It cross‑fits a correctness‑risk score over six logit statistics and assigns a top‑label BCE temperature to each of three risk groups, generalizing TvA‑TS when K=1. Experiments on fine‑tuned CIFAR‑100 and ViT‑B/16 show that SRTS‑BCE reduces ECE from 1.65 to 0.96 with a small calibration budget, outperforming higher‑capacity SMART+BCE when only 250 examples are available, and revealing a budget‑dependent ranking reversal on Swin‑T.
arXiv:2609.26217v1 Announce Type: new Abstract: Compact language models are typically deployed by retaining a single post-training checkpoint and sampling it repeatedly. In this work, we challenge th...
arXiv:2608. 07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone.
The paper introduces a method to certify selective prediction in machine learning systems by computing the availability of safety gates through exact-binomial inversion and dynamic programming. It demonstrates that a truth-informed planner can significantly improve mean coverage over naive approaches, and that reallocating error budgets further enhances coverage across diverse applications such as LLM tool‑calling, content moderation, lesion classification, and recommendation. The study highlights the importance of planning and finite‑sample estimation in ensuring reliable, granular deployment of selective predictors.
arXiv:2605. 22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.
Parameter-efficient fine-tuning is usually framed as a question of how many parameters to update. Under a severe trainable-state budget, however, where those coefficients act is equally consequential....
arXiv:2606. 29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and when it should be escalated to human review.
arXiv:2609. 29140v1 Announce Type: new Abstract: Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty.
arXiv:2609.00762v1 Announce Type: new Abstract: Parameter-efficient fine-tuning is usually framed as a question of how many parameters to update. Under a severe trainable-state budget, however, where...
arXiv:2609.38699v1 Announce Type: new Abstract: A cumulative token cap can fall inside a mathematical derivation, forcing a test-time controller to choose between stopping at the cap (strict) and all...
arXiv:2606. 25068v1 Announce Type: new Abstract: Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute.