The paper studies minimax estimation of the pseudo-spectral gap $\gamma_{ps}$ of a continuous-state Markov chain on $[0,1]^d$ from a single trajectory. The core idea is to discretize the state space into a regular grid, approximate the transition density by a histogram, and adapt finite-state estimators to this compressed chain. The result matters because it yields fully empirical concentration inequalities for non-iid data, enabling UCB bandit algorithms and PAC-Bayes bounds without prior knowledge of mixing coefficients.

CAST addresses a key limitation of soft spectral clipping (SSC) for LLM pretraining: its effect depends on the gradient scale and a manually chosen threshold. The authors introduce CAST, which uses AdamW’s previous second-moment statistics to define per-matrix historical scales, and an exponential moving average of the clipped output’s estimated spectral norm to set each matrix’s threshold. On a 19B-parameter mixture-of-experts model trained for roughly 84B tokens, CAST reports lower training and validation losses than nine gradient-processing baselines and the fewest gradient spikes, though the evidence rests on a single run per method.

This paper studies the nonconvex optimisation landscape of finite-particle maximum mean discrepancy (MMD) quantisation, where a target probability measure is approximated by a weighted atomic measure. For the one-dimensional Gaussian kernel, the authors prove that beyond explicit particle thresholds every local minimum has arbitrarily small MMD and every high-MMD stationary point is unstable under unweighted gradient flow. These are the first finite-particle overparameterisation guarantees for weighted MMD, though the analysis is currently limited to one dimension.

HGRP targets routability prediction in VLSI physical design by representing circuits as heterogeneous graphs with three distinct node types—cells, nets, and routing grids. It introduces a staged message-passing pipeline that propagates information from logical topology to physical layout to spatial grid adjacency, aiming to capture the coupling between netlist structure and routing congestion. Accurate congestion estimation matters because it provides rapid feedback during placement, avoiding costly iterations through detailed routing.

The paper tackles online selection of prediction agents—such as classifiers, LLMs, or human experts—that must balance accuracy and subgroup fairness in a sequential streaming setting where evaluating every agent on every example is impractical. It formulates the problem as a Bayesian multi-armed bandit where utility combines accuracy, a fairness-gap penalty weighted by $\lambda$, and a query cost, then applies Thompson Sampling with conjugate Beta posteriors for both overall accuracy and per-subgroup prediction rates. This matters because fairness-aware sequential selection introduces new statistical dependence between accuracy and subgroup estimates that standard MAB analyses do not handle, and because many real-world deployments must learn while operating.

SNACK addresses the challenge of generating attributed graphs using standard sequence models without sacrificing structural validity or probabilistic correctness. Its core idea is a bijective string encoding of the lower-triangular adjacency matrix that, when paired with BFS ordering and invalid logit masking, guarantees 100% valid outputs and enables exact computation of the sequence-to-graph distribution $q(\mathbf{x}|G)$. This matters because it bridges the efficiency of autoregressive Transformers with the principled reward-matching guarantees of GFlowNets, a combination that prior non-injective string representations like SMILES could not support.

This paper tackles behavior cloning (BC) for autonomous driving in the CARLA simulator when only a handful of expert demonstrations are available. The core idea is to reweight the supervised BC loss by the inverse of a kernel density estimate over actions, up-weighting rare but safety-critical behaviors such as stopping at red lights and accelerating from a standstill. The motivation is that uniform BC easily ignores these low-density events in small datasets, which can lead to dangerous traffic-light violations and poor generalization.

Supervised fine-tuning can collapse the pretrained model's preference structure among non-target tokens. Instead of matching the reference distribution exactly like LP-SFT, this paper proposes SRP-SFT, which enforces soft, selective lower bounds on margins between top-K non-target token pairs. At one epoch on a 4B model, it improves MATH-500 pass@1 from 49.56 to 54.56 over LP-SFT, with consistent gains across three seeds.

As large language models are deployed at scale, the energy cost of inference has become a pressing systems question. This paper compares the energy efficiency of discrete diffusion language models against standard autoregressive models, measuring Joules-per-token across different output lengths. The authors find a crossover point: diffusion models are less efficient for short sequences but become roughly $2\times$ more efficient than KV-cache-optimized autoregressive models at longer sequences. The work highlights how architectural choices and sequence length together determine the sustainability of generative inference.

Biodegradation reaction data are far scarcer than synthesis data, which limits neural predictors for environmental transformation products and pathways. This paper introduces enviGraph, a framework that generates roughly 777,000 synthetic parent–product pairs by applying inherited environmental transformation rules to xenobiotic seed structures, pretrains a graph-to-sequence model on these data, and fine-tunes on observed environmental reactions. The authors evaluate whether these rule-derived pretraining corpora outperform general-chemistry USPTO datasets for immediate product and multi-step pathway prediction across BBD, Soil, and Sludge benchmarks.

Planetary landing controllers learned via deep RL are usually randomized with independent disturbances, yet real mission perturbations are temporally correlated. This paper proposes adversarial RL where a second agent generates smooth, physically bounded disturbance sequences to stress-test a 3-DOF lunar lander, framing training as an asymmetric zero-sum game. A staged curriculum (nominal, stochastic, adversarial) aims to expose hidden failure modes that standard uncorrelated randomization misses. Early results suggest the adversarially hardened policy resists larger out-of-distribution perturbations better, though at some cost in low-disturbance accuracy and with limited statistical validation.

This paper tackles continuous global optimization when only noisy pairwise preferences are observed, a setting where standard Gaussian process regression breaks down because sign observations produce non-Gaussian posteriors. The core contribution is a fully sequential assumed-density-filtering scheme: after each comparison, the posterior is approximated by a Gaussian process via moment matching, enabling closed-form recursive updates of the mean and covariance. The authors pair this with a novel sampling policy called DOVE, which randomizes between a proposed pair and intermediate bridge pairs to control approximation drift, and they prove the first convergence rates for estimation error and simple regret under RKHS regularity conditions.

LLM agents learn reusable skills from experience, but real-world noise can corrupt the rules they distill. This paper introduces RSE-Bench and RSEClaw, a benchmark and framework for evaluating skill self-evolution under ordinary, non-adversarial noise across four entry points: instructions, evidence, execution records, and outcome bindings. The central finding is that noisy learning retains only $25.5\%$–$42.1\%$ of the aggregate gains from clean evolution, and noise confined to learning records erodes performance as much as input-side perturbations. The work matters because it shows that benign deployment imperfections persist in agent memory and propagate to later tasks.

The paper studies how classification accuracy evolves when neural networks are trained from very small initialization. It identifies the "accuracy well": a striking non-monotone trajectory where accuracy first rises rapidly, then drops significantly, and only recovers much later. The authors argue this reveals learning stages invisible to the training loss, and they support the claim with experiments across architectures and datasets, plus rigorous theory for deep linear networks.

Prostate MRI segmentation suffers from regional boundary ambiguity, causing standard global-overlap losses to overlook clinically significant surface deviations. This paper proposes Contrastive Uncertainty Learning (CUL), which treats predictive uncertainty as a representation-level supervisory signal rather than a post-hoc filter: low-uncertainty same-class voxels are clustered in embedding space while high-uncertainty or misclassified voxels are actively repelled. Evaluated on 589 lower-pelvic MR scans across eight anatomical structures, CUL preserves mean Dice while significantly improving surface-sensitive metrics and suppressing extreme errors in hard cases, suggesting practical value for radiotherapy planning and contour-acceptance workflows.

Clinical assessments collected across heterogeneous systems create mixed longitudinal records that resist standard total-score linking. This paper proposes Bidirectional Feature Harmonization Decomposition (BFHD), a method that identifies which individual measurements can be reliably translated between two assessment systems in both directions while explicitly separating out non-translatable components. By operating directly in original measurement spaces via a shared invertible backbone and feature-wise output gating, BFHD aims to provide an interpretable decomposition for cross-protocol integration and longitudinal analysis.

Graph collaborative filtering models collapse in strict cold-start settings because zero-degree items sever the message-passing chain. DualGCN tackles this with an asymmetric two-branch distillation framework: a teacher network anchors item representations with frozen semantic priors smoothed over the graph topology, and a lightweight student learns to project unseen semantic features into that collaborative space via cosine distillation and residual mapping. If the claims hold up, it offers a much leaner alternative to heavy multi-modal fusion modules for industrial retrieval.

Latent world models based on Joint-Embedding Predictive Architectures (JEPA) offer a scalable route to embodied planning, but existing methods suffer from representational collapse, heavy compute, and weak long-horizon reasoning. This paper proposes MemWM, a lightweight JEPA world model augmented with a content-addressable associative memory bank that supplies high-level subgoals via Adaptive Layer Normalization. By operating at multiple temporal scales within a shared latent space and enforcing slot diversity through complementary losses, the authors aim to boost long-horizon performance without relying on pretrained encoders or doubling model size. Across four control tasks, MemWM achieves an average success rate of 89.9% with only 19M parameters, outperforming heavier baselines such as DINO-WM in aggregate.

This paper resolves the open problem of optimal dimension dependence for dynamic regret in exp-concave online learning. The authors propose SparseGauss, a proper full-information learner that achieves a $\widetilde{O}(d^{1/3}T^{1/3}P_T^{2/3})$ rate via sparse Gaussian histories and square-root KL movement control. A matching lower bound on a Euclidean ball establishes minimax optimality, up to logarithms, when $T \gtrsim d^2$.

Score-based generative models with heavy-tailed $\alpha$-stable Lévy noise have shown empirical promise for extreme-event modeling, yet theoretical guarantees remain scarce. This paper develops a non-asymptotic $W_p$ convergence framework for scale-preserving $\alpha$-stable dynamics and proves that global Wasserstein convergence alone does not separate stable from Brownian models on heavy-tailed targets. A sharp distinction emerges instead in the tails: stable dynamics inherit power-law tails with index $\alpha$, while Brownian outputs stay light-tailed, and asymptotically correct extreme-quantile scaling requires matching the target tail index $\nu$ to the noise index $\alpha$. These results establish tail-index matching as a principled design mechanism for capturing heavy-tailed phenomena.