SILSA tackles the cost and topological fragility of high-resolution 3D generation by replacing dense or sparse voxel tokens with compact sliding-window slice latents along the three canonical axes. Each of the 384 fixed tokens summarizes a local depth window, preserving cross-sectional continuity while enabling a single-stage rectified-flow generator. Topology-aware slice supervision and a Volumetric Anchor Lattice then maintain geometric and structural coherence. The paper reports large efficiency gains—over 98% fewer tokens than sparse voxelizers—and solid improvements in PSNR, coverage, and Betti error.

Unsupervised reinforcement learning aims to eliminate hand-crafted rewards, yet existing intrinsic motivation objectives either require selecting information variables or serve only as exploration bonuses. This paper introduces Forward CIP (F-CIP), an exact forward-in-time decomposition of the Controllable Information Production (CIP) objective—defined by the sum of positive open-loop Lyapunov exponents—into a per-step Markovian reward and a propagated information matrix memory. By doing so, it enables standard RL algorithms to unsupervisedly discover upright balancing and to simplify reward engineering for dynamic locomotion tasks.

Sound classification with transformers usually demands stacking many layers, which inflates parameters and compute. LAST asks whether the same blocks can be reused to refine only a single class token while keeping audio features fixed, trading depth for recurrence. Their looped Audio Spectrogram Transformer matches or exceeds deeper sequential baselines on AudioSet at roughly half the parameter count, suggesting that how tokens are updated matters as much as how many layers exist.

This work tackles street-view weather recognition under severe domain shift, where labeled training images come from generic outdoor datasets and real driving scenes are unlabeled at training time. The proposed Weather-Aware ADDA conditions the adversarial domain discriminator on predicted weather probabilities, encouraging alignment that remains sensitive to weather semantics. The idea is to enable reliable onboard camera-based perception for autonomous vehicles without requiring expensive labeled street-view data for every adverse condition.

This paper introduces Higher-order Grammar Representation (HGR), a method that lifts molecular graphs into combinatorial complexes and parses them into compact sequences of context-free production rules, making higher-order topology such as ring systems tractable for standard sequence models. To reduce benchmark bias toward simple rings, the authors also construct RingDiv, a ring-enriched benchmark of 1.18 million molecules. HGR is instantiated in generative models (HGR-VAE and HGR-LDF) and a foundation model (HGR-FM), with claims of 100% validity by construction and state-of-the-art distributional alignment and transfer performance.

Speech-based Alzheimer's assessment increasingly relies on self-supervised learning models, but their vulnerability to nuisance acoustic factors remains unclear. This paper asks whether noise and reverberation are merely encoded in SSL representations or actively drive diagnostic predictions. Using controlled input- and representation-space interventions on the ADReSSo dataset, the authors show that acoustic manipulations systematically shift classifier outputs even when no baseline group difference exists, challenging the assumption that predictive accuracy implies robustness.

Deep neural networks in long-lived systems often face task obsolescence, where previously valid outputs become invalid after deployment. Deep Repurposing (DR) addresses this by analytically redirecting obsolete latent structure toward retained regions instead of simply erasing it. This enables lightweight post-hoc adaptation without gradient updates, achieving retained performance comparable to or better than unlearning and editing baselines while operating up to 60× faster than retraining.

Operational geostationary fire products like the GOES Fire Detection and Characterization (FDC) suffer from high false-alarm rates and poor nighttime recall. This study replaces the FDC's rule-based algorithm with a CatBoost gradient-boosting model trained on over 60,000 GOES ABI fire pixels labeled by VIIRS active-fire detections. By evaluating on large 2024 regions outside the training domain, the authors show that a comparatively simple ML model can substantially outperform the operational product in precision, recall, and early detection latency, especially after dark.

How complex can a quantum device's responses become when it runs longer with a fixed internal memory? This paper quantifies this through sequential response capacity—the depth of an adaptively shattered binary tree of experiments—and proves a tight law: for fixed visible and memory dimensions, capacity grows as $\Theta(K\log((K+1)/\gamma))$ in run length $K$ and probability gap $\gamma$. The core construction shows that memory-one qubit phase channels already attain this scaling with exact zero-or-one responses, while fully measured classical processes under the same tester permissions are limited to linear capacity. The result establishes a fundamental information-theoretic separation between coherent quantum control and classical stochastic transitions under a finite memory budget.

Looped Transformers reuse the same parameter block across recurrent iterations, producing intermediate states that are usually discarded at decode time. This paper proposes LoopCD, a training-free contrastive decoding framework that treats an early recurrent state as a weak predictor to guide the final prediction, either in logit space (one extra output pass) or hidden-state space (zero extra overhead). The core insight is that recurrence naturally provides aligned weak-and-strong pairs without auxiliary models, and the authors show this can both boost accuracy at full depth and—more interestingly—allow halving the iterations while matching baseline performance, cutting forward FLOPs by roughly 23–48%.

The paper tackles the frozen-context bottleneck in outcome-based reinforcement learning for forecasting: prior work either pastes fixed research snippets into the prompt before training or lets a frozen model search only at test time, so the policy is never rewarded for gathering better evidence. The authors close this gap by building a multi-turn agentic harness from 2,100+ resolved Polymarket questions, filtering every search and page-read to pre-cutoff information, and training Qwen3.5-35B-A3B with single-epoch GRPO under the Brier-score reward $r=1-(p-y)^2$. The core advance is that the model learns when and what to search for as a temporally grounded skill, rather than optimizing a frozen context window. This matters because it moves forecasting from prompt engineering to genuinely learned research discipline, and the trained open model reportedly beats several frontier systems at roughly 5% of the inference cost.

Multimodal reinforcement learning with verifiable rewards (RLVR) is widely assumed to improve visual reasoning when models are rewarded for correct answers. This paper exposes a critical gap: benchmark gains can be achieved without learning from images at all, and prolonged training can even erode visual grounding. The authors propose "visual resolvability"—designing tasks where visual evidence is strictly necessary for reward—and show that standard GRPO on carefully constructed counterfactual coordinate scenes drives genuine visual discovery, unlike conventional benchmark training.

Relational learning with tabular foundation models currently relies on Deep Feature Synthesis (DFS), which flattens multi-table schemas into single tables via column-wise aggregations along meta-paths. This paper identifies two critical limitations of DFS—feature explosion and interaction blindness—and proposes RelICL, a training-free method that instead propagates row embeddings through the schema graph using the foundation model itself. By iteratively pooling and fusing embeddings, RelICL keeps the TFM-agnostic benefits of DFS while scaling to complex schemas and capturing cross-column interactions. The experimental study on 21 RelBench tasks shows RelICL matches state-of-the-art DFS approaches, though it does not dominate them outright.

Many applications require sampling from high-dimensional categorical distributions with local dependence, but discrete diffusion models lack end-to-end statistical guarantees. This paper introduces a pinning decomposition of the discrete score under uniform noising, showing that time dependence separates multiplicatively from target dependence for Markov random fields of order $d$. Building on this, the authors design a weight-sharing neural score learner and prove a sample-complexity rate of $\widetilde O_{\mathbb P}(S^d/n)$, replacing the ambient dimension $D$ with the interaction order $d$.

Graph positional encodings (PSEs) are computed from pairwise edges and inherently miss higher-order interactions such as triangles and cliques. The paper proposes to enrich standard graph models by computing positional encodings on lifted higher-order domains (e.g., clique complexes) and projecting them back to nodes, without altering the backbone. Its core theoretical insight is that lift-induced symmetric node operators can mix the eigenmodes of the original graph Laplacian $L$, placing them outside the class of scalar spectral filters $g(L)$. This yields a principled design rule---the commutator $[L,M] \neq 0$---and an experimental pipeline using Hodge-Laplacian encodings.

Monitoring contact wire geometry is essential to prevent catastrophic failures like pantograph destruction and contact wire breakage in railway systems. Existing vision-based methods track wire height and stagger, but they process these measurements without knowing where along the route they occur. This paper proposes spatially aligning pantograph-catenary telemetry to a GPS master grid, partitioning the route into geographic segments, and training a localized bidirectional LSTM-autoencoder per segment to detect collective anomalies. It targets the blind spot of geographic agnosticism in current industrial monitoring, arguing that location-aware models reduce false positives caused by natural topological variation.

LoRA adapters often suffer from adaptation imbalance, where a handful of singular directions hoard the update energy and hurt both target-task specialization and off-task capability retention. This paper proposes LoRA-Norm, a data-free post-training edit that preserves the learned singular directions but rebalances their gains with a fixed hybrid Newton-Schulz transform and per-module nuclear-norm restoration. The authors argue that learning where to adapt does not ensure that adaptation gains are well balanced, and they evaluate the method on 18 adapters spanning two 8B backbones and three tasks.

Time series foundation models post strong scores on large benchmarks, yet their grasp of basic temporal logic remains unclear. This paper introduces SimpleTimeBench, a diagnostic suite of univariate and multivariate unit tests for primitives such as linear trends, periodic signals, and deterministic leading-indicator covariates. The authors show that prominent zero-shot TSFMs—including Chronos-2, Moirai, and Toto—fail on several trivial tasks, and that targeted fine-tuning on the full suite yields only marginal gains, suggesting that pre-training scale alone has not bought robust fundamental reasoning.

This paper studies fixed-confidence best-arm identification for Bernoulli bandits using the top-two $\beta$-EB-TCI rule, where the challenger is selected by a penalized Bernoulli GLR index. Its core contribution is a sharp non-asymptotic analysis: once the true best arm becomes the permanent leader and its sampling fraction stays near $\beta$, the stopping time is $T_\beta^\star(\mu)\log(1/\delta)$ up to lower-order terms. The paper also identifies exactly where the analysis stalls for the unguarded rule when suboptimal means are tied, and closes the gap with a mild $O(\sqrt{Kt})$ exploration guard.

This paper targets few-shot ransomware detection by pairing an Autoencoder Feature Extractor (AFE) with a Model-Agnostic Meta-Learning (MAML) classifier. The idea is to compress 72 tabular behavioral features into a 64-dimensional latent space via the autoencoder, then let MAML adapt a binary classifier from just a handful of labeled malware-versus-benign samples. The authors pitch this as a rapid-deployment defense for healthcare and critical infrastructure settings where new ransomware variants appear with little warning.