Many physical systems are driven and observed only on lower-dimensional submanifolds embedded in a larger ambient domain, forcing classical solvers to waste computation on full volumetric states and preventing black-box surrogates from reusing a fixed medium representation. This paper introduces the Green’s Observation Operator (GObO), which learns a time-dependent Green’s kernel restricted to the source and observation submanifolds so that, after a single conditioning pass on the ambient medium, evaluating an entirely new source requires only a cheap lower-dimensional integral with no additional network inference. The idea is supported by exponential spectral approximation guarantees, an exact finite streaming state, and strong empirical speedups and source-generalization gains on three-dimensional heat and advection–diffusion problems.

Understanding when neural-network loss landscapes contain spurious local minima is a foundational problem in deep learning. This paper studies shallow bias-free ReLU networks in a Gaussian teacher-student setting and proves that a learned linear skip connection eliminates all spurious local minima for planar teacher features with positive output weights once the student is at least as wide as the teacher. In contrast, without the skip, spurious minima can persist at every width—a striking separation between architectural bias and overparameterization.

Wrist electrodermal activity differs in amplitude from person to person, so affect-recognition models normalize the signal before classification. A hidden protocol choice is whether the normalization statistics come from training subjects only or from the held-out user's own complete recording, which retroactively gives the model a calibration that a newly worn device lacks. The paper introduces SAFE-EDA, a compact convolutional network pretrained on expert artifact annotations, and shows that its measured benefit over training from scratch ranges from 0.020 to 0.227 depending on this normalization choice, with the larger gains occurring in the realistic first-use setting. The authors also survey fifty published WESAD studies and find only ten percent report the source of their normalization statistics, making published scores difficult to compare.

Range-GRPO addresses semi-supervised LLM post-training when labeled data is scarce by treating LLM-as-a-Judge scores as conformally calibrated reward intervals instead of scalars. Its core idea is to replace standard GRPO pointwise comparisons with pairwise comparisons of interval midpoints, weighting each pair by $\omega_{ij}=\lambda/(h_i+h_j+\lambda)$ so that uncertain rewards can actually flip the direction of the learning signal. When all intervals collapse to points ($h_i=0$), the method exactly recovers Dr.GRPO, and experiments suggest it improves both in-distribution and out-of-distribution reasoning with a smaller compute budget.

The paper tackles autoregressive modelling of mineral-exploration drillholes, where lithology is revealed sequentially from shallow to deep. It introduces DrillBench, a 49,671-hole benchmark across Western Australia with a graded transfer spectrum from local prediction to cross-province generalisation, evaluating next-layer prediction (macro-F1) and free-running generation (joint $\mathrm{KL}(\text{true}\|\text{generated})$). The core finding is a stark transfer boundary: spatial and geochemical conditioning boosts local accuracy but collapses under shift, while sequence-only autoregressive models degrade more gracefully. To address this, the authors propose a backbone-agnostic recipe combining large-scale pretraining on historical archives with spatial retrieval of neighbouring lithology, showing that retrieval aids local cover prediction and pretraining aids bedrock and cross-province transfer.

Existing methods for structured imitation learning rely on extensive human input or static LLM knowledge that can conflict with expert demonstrations. This paper proposes a closed-loop framework that treats policy development like software debugging: an LLM writes diagnostic analysis code to test semantic latents from tabular rollouts, identifies structure-demonstration misalignment, and iteratively edits the symbolic policy structure before re-fitting continuous parameters through imitation learning. The work matters because it automates refinement of interpretable policies and shows on two control tasks that it can outperform zero-shot LLM structures while using far less subsequent RL compute.

Primal-dual hybrid gradient methods can solve large linear programs on GPUs, but their speed depends on delicately coordinated step sizes, restarts, and acceleration heuristics. GALLOP replaces these hand-tuned rules with a reinforcement learning policy that jointly picks continuous parameters and discrete restart decisions inside a generalized PDHG update. The approach is notable because the learned policy is dimension-agnostic and reportedly generalizes from small training instances to LPs hundreds of times larger without retraining.

Low-budget active learning seeks a tiny labeled subset—a coreset—that still trains an accurate model, which is crucial in medical domains where labeling is expensive. The authors propose using the Sinkhorn divergence, an entropic optimal transport distance, as a coreset quality criterion, and develop a Frank–Wolfe warm-started swap heuristic (FW-Swap) to optimize it. Their framework yields dimension-free generalization guarantees and shows competitive or superior accuracy on vision and EEG benchmarks with very small budgets.

MiLoop addresses a key inefficiency in constructive neural combinatorial optimization: dynamic-embedding methods typically rebuild subproblem representations from scratch at each step with deep attention stacks, and often depend on external solution labels or aggressive training-time pruning. The paper proposes Memory-in-the-Loop (MiLoop), an autoregressive framework that propagates a global node-wise memory matrix across construction steps, fusing current inputs with historical context before shallow attention layers and updating memory selectively via adaptive gates. By leveraging the multi-step rollouts already required for RL, MiLoop enables a three-layer policy to learn effective dynamic embeddings without external labels. Strong results on four COPs from 100 to 10 million nodes demonstrate both high solution quality and cross-scale generalization.

Cross-architecture weight-space transformations map trained networks into different architectures (e.g., compression, upscaling), yet prior equivariant weight-space models only handle fixed architectures. CrossGMN addresses this by treating the task as refining a target initialization using a source network, making equivariance well-defined: invariant to source permutations and equivariant to target permutations. The authors prove universality for continuous cross-architecture operators and show strong empirical speedups for knowledge distillation, especially on implicit neural representations.

Generative recommenders represent items as short discrete Semantic IDs (SIDs) that are decoded autoregressively, yet most prior work derives these codes by quantising isolated content embeddings. This paper argues that SID construction is fundamentally a recursive clustering problem over a collaborative item graph, and proposes GrIS, a framework that separates graph construction from hierarchical partition to balance semantic and behavioural signals. By exposing these two axes, the authors show that content-only tokenisers are merely empty-graph instances of a broader design space, and they demonstrate new instantiations that improve SID quality for downstream recommendation.

The paper tackles a critical problem in automated image-editing evaluation: multimodal large language models (MLLMs) acting as judges may be swayed by irrelevant presentation cues rather than the edit itself. Because visual interventions can alter the very quality being assessed, the authors introduce EditJudgeBias, a counterfactual benchmark of 1,196 real samples where 13 cues are injected across four evaluation stages and rigorously verified to preserve underlying quality. They audit five judges along three dimensions—invariance to cues, agreement with humans, and pairwise stability—and show that every judge shifts beyond its own noise floor, with fabricated social proof inflating scores and display-order swaps reversing up to 60.9% of verdicts.

Predictive maintenance models are typically tuned to maximize $R^2$, a symmetric metric that ignores the fact that late remaining useful life (RUL) predictions are far more costly than early ones. This study treats the optimization objective itself as a variable, comparing single-objective and multi-objective hyperparameter optimization across MLP, LSTM, XGBoost, TCN, and Transformer on NASA C-MAPSS and BackBlaze benchmarks. The core result is that NSGA-II with Entropy–CRITIC selection cuts directional imbalance by roughly a third on simulated data without sacrificing accuracy, though this advantage collapses into near-random behavior on real-world hard-drive data.

This paper tackles privacy, energy, and infrastructure constraints in smart buildings by proposing a decentralized cloud-fog-edge architecture that treats the building itself as a data center. Its core idea couples the KOptim container orchestrator with FIWARE for dynamic SLA management, while running lightweight online ML algorithms such as GHA-PCA and K-means directly on Arduino-class microcontrollers. The humanitarian motivation—bringing autonomous AI to underserved communities—is compelling, although the current evaluation remains largely preliminary.

This workshop paper examines how fairness assessments for ICU mortality models on MIMIC-IV hinge on both the chosen performance metric and the demographic resolution of evaluation. It introduces a lightweight classifier-head adaptation, IBHFT, that jointly balances ethnicity–gender–insurance representation without conditioning on mortality outcomes, using this case study to show that marginal summaries and single-metric reports can paint a misleading picture of clinical fairness. The core argument matters because deploying models based on aggregate AUROC or one-axis parity alone can obscure dangerous error-rate disparities at clinically consequential intersections.

pCoMole addresses the problem of editing existing biomolecular sequences to improve multiple properties while obeying hard feasibility constraints such as PAM specificity or emission range. Its core idea is to steer a pre-trained Edit Flow via a Doob-$h$ transform, using an augmented Tchebycheff utility over terminal states that is approximated through short Monte Carlo rollouts. This matters because existing shrinkage methods like RayGun and SCISOR optimize length alone, whereas biomolecular engineering inherently requires trading off function, developability, and manufacturability. The work is notable for combining a clean theoretical justification with preliminary wet-lab validation on shortened eGFP variants.

This paper asks whether a fixed offline corpus generated by a behaviour policy can ever distinguish two candidate Regular Decision Processes (RDPs). It proves that when two candidates are $\pi$-observationally equivalent, their posterior odds remain frozen at the prior for any sample size (Theorem 6), gives an exact measure-zero characterization via the product-automaton blame language (Theorem 12), and derives PEC, a linear-time algorithm that certifies equivalence and names the missing experiment. The work matters because existing PAC offline guarantees for RDPs assume a distinguishability condition that was previously impossible to verify.

This paper develops a high-dimensional theory for why masked autoencoders learn better representations than standard autoencoders. In a spiked model with heterogeneous noise, the authors prove that a linear masked autoencoder recovers the latent feature at linear sample complexity $n=O(d)$ where PCA fails. They further quantify how dynamic mask resampling lowers sample complexity compared to static masking, and validate the qualitative predictions in CNNs, vision transformers, and a BERT pilot.

This paper proves worst-case lower bounds for stochastic first-order algorithms in nonconvex--concave minimax optimization, explicitly permitting variance-reduction via same-sample oracle queries. Its main result shows that the stochastic barrier remains $\Omega(L^3 D_Y^2 \Delta \sigma^2 \varepsilon^{-6})$ even with mean-square smoothness. Complementary nonconvex--strongly-concave bounds are also provided. Together the results establish that variance reduction does not improve the fundamental accuracy exponent in the minimax setting.

This paper studies stochastic multi-level compositional optimization, where the objective is a nested composition of $T$ smooth non-convex functions and only stochastic per-layer function and gradient values are available. The core idea is to use momentum-based estimators to track inner function values level-by-level, and then apply the chain rule to build a momentum gradient estimator for the overall objective. It matters because it establishes that the optimal $\mathcal{O}(\epsilon^{-4})$ sample complexity is achievable under standard smoothness assumptions, closing a gap with single-level lower bounds without relying on the stronger average-smoothness conditions common in prior multi-level literature.