A new paper proposes a Supervised Reinforcement Learning (SRL) framework for coordinating distributed energy resources (DERs). The approach pre-trains policies on demonstration data, then fine-tunes with offline and online RL, outperforming benchmarks even with low-quality data.

Learned world models are useful only over horizons on which their rollout error remains controlled. This paper studies trust-horizon certification for latent world models with known group symmetries. Using split-conformal calibration, the authors show that exact equivariance transports the calibrated trust-horizon curve over the group orbit, making rollout errors and trust horizons orbit-constant. Experiments on 2D and 3D tasks demonstrate that equivariant models achieve safe and non-vacuous orbit-valid certificates from a single calibration sector, while non-equivariant baselines incur additional costs. The certificate is a conservative distributional audit, not a global reachability guarantee.

This research explores when conservation laws remain certifiable after a physical world model learns a latent representation. The authors introduce 'certified horizons' that bound how many rollout steps provably stay on an invariant's level set. Instead of certifying learned latent Hamiltonians, they certify decoded physical invariants. The framework decomposes certification budgets into representation, readout, and latent-dynamics defects, using a monotone alignment bridge for soft witnesses. Results show hard symplectic structure provides long horizons in known coordinates but fails across learned charts, while controlled-Lipschitz soft invariants survive representation learning. Pixel certification is recovered on readout-stable sub-tubes.

This paper introduces a saturation index to determine when to stop collecting labeled data in binary few-shot classification. Computed from support features alone, it measures the effective rank of the within-class covariance relative to shot count. Empirical evaluation on 246 observations from 17 tasks shows strong correlation with marginal accuracy gain (median Spearman ρ=0.811) and identifies three phases: exploration, transition, saturation. As a stopping rule, it achieves AUC 0.752. A low saturation index with low accuracy indicates representational inadequacy.

This survey reformulates industrial continual learning for LLMs as a closed-loop update-and-release problem in a versioned ecosystem. It identifies three core challenges (plasticity erosion, capability inheritance breakage, sustainability constraints) and proposes five lifecycle design principles. The paper evaluates maturity of each principle and outlines a deployment blueprint.

This paper proposes a lightweight neural architecture search performed directly on deployment devices for near-sensor computing, enabling adaptation to individual users in human-machine interfaces. Validated on Italian Sign Language and CWRU datasets, the method reduces RAM usage by 0.44–0.63× and improves accuracy by 0.2–5.96 percentage points on a Raspberry Pi 4.

A new paper presents a case study of human-AI collaboration transforming a vague research intuition into concrete mathematical discoveries, specifically sign-embedding quantum algorithms for matrix equations and functions. The AI system AIM played a key role in expanding the intuition, comparing candidate formulations, and connecting known identities, while humans retained final scientific judgments such as selecting routes, rejecting invalid approximations, and refining implementations. The authors argue that human-AI co-discovery workflows are most valuable as research partners, not standalone theorem provers.