Best-of-N sampling improves LLM output quality by generating multiple candidates and selecting the highest reward, yet its scaling behavior under imperfect reward models is poorly understood. This paper recasts BoN as extreme-value competition between the upper tails of correct and incorrect reward distributions, yielding phase transitions that explain both monotonic gains and reward hacking. Using extreme value theory, the authors derive finite-sample scaling laws showing polynomial rather than exponential convergence, and propose a lightweight tail-tension criterion for label-free early stopping. The work matters because it replaces vague intuitions about reward hacking with precise, testable distributional conditions and a practical inference-time safeguard.

Activation steering is used to steer language models toward named behaviors, but standard baselines cannot tell whether a vector controls a specific concept or a neighboring one. This paper introduces the competing-target test, which compares a steering vector with the matched vector for a competing target and bounds their difference with an equivalence test. In three open-weight models, the authors show that vectors for Kantian deontology and act utilitarianism are largely interchangeable---they move both framework scores alike---whereas explicit prompting separates them, and the same construction does separate optimistic and pessimistic tones.

This paper studies the infinite-generation limit of autoregressive transformers. Rather than fixing sequence length, the authors let generation time tend to infinity and ask whether the model's empirical state converges. Their core result is that, for a broad class of architectures, the stochastic generation process tracks a deterministic ODE on a slow time scale, and every fixed transformer has a finite temperature threshold above which it converges to a unique limit. However, no uniform temperature works for all models; for any finite temperature, some transformer can fail to converge, even with just one layer and three tokens.

This paper claims the first global joint lower bound for zeroth-order nonconvex optimization under unrestricted spatial queries and sample reuse. The core idea is to force any algorithm to advance through $T\asymp\min\{\Delta H,L_0^2\}/\epsilon^2$ hidden geometric stages and to prove that each fresh stage requires $\Omega(dL_0^2/\epsilon^2)$ adaptive scalar evaluations, yielding an overall $\Omega(dL_0^2\min\{\Delta H,L_0^2\}/\epsilon^4)$ bound. If correct, this would settle the open problem posed by Kornowski and Shamir (2024), though the manuscript is dated 14 October 2026, cites several future works, and explicitly states that its mathematics was primarily AI-generated.

In medical and crowdsourced annotation, the absence of clean ground truth biases standard expected calibration error (ECE) evaluation because annotator errors confound predictor miscalibration. This paper proposes Noise Corrected ECE (NC-ECE), which inverts annotator-specific class-conditional noise channels to recover clean-label ECE, and provides methods to estimate those channels from repeated annotations alone. It then derives post-hoc recalibration algorithms with either clean-risk or calibration guarantees. The work is directly motivated by settings such as medical imaging, where expert disagreement is common and reliable uncertainty assessment is essential.

Density functional theory (DFT) is accurate but expensive, while pretrained machine-learned interatomic potentials (MLIPs) are fast yet erratically wrong across configurations. This paper frames the decision of when to trust an MLIP and when to fall back to DFT as a risk-controlled selection problem solved with conformal risk control (CRC). A lightweight, chemistry-informed descriptor feeds a small neural network that estimates MLIP-DFT disagreement in energy and forces; CRC then calibrates a threshold so the expected retained error stays below a user-chosen fraction $\alpha$ of the fitting-set mean. The method cuts DFT costs—by 7–14% at $\alpha=0.3$ versus committee baselines—without requiring access to the target MLIP's internals, which matters for practitioners who want certified savings on a fixed, pretrained model.

Feel-Good Thompson Sampling (FGTS) addresses slow exploration in vanilla TS by adding an optimism term to the posterior. This paper proves the first high-probability regret bounds for both cumulative and current-step FGTS under general realizable function approximation, showing that in linear contextual bandits the regret scales as $O(d\sqrt{T}\log(2T)+\sqrt{T}\log(1/\delta))$ with probability at least $1-\delta$. The core technique is applying Markov's inequality to exponential quantities derived from the posterior normalizer, avoiding explicit confidence-set constructions.

Sequential conformal prediction struggles when the recent past is irrelevant and similar predictive states recur at nonadjacent times. This paper proposes State-Matched Sequence Conformal Prediction (SMSCP), which constructs an interpretable local-linear pre-target state and uses Mahalanobis similarity to select past calibration scores for ordinary conformal cutoff estimation. The work matters because it replaces fragile recency assumptions with an explicit state-matching mechanism, offering both modular implementation and a quantile-targeted theory that isolates when state matching helps and when it fails.

This paper addresses non-parametric estimation of the background intensity in Hawkes and ETAS models by treating the background as an unknown finite positive measure with a regular density. The core idea is to derive a distribution-valued Wasserstein-Fisher-Rao (WFR) gradient from the Hawkes likelihood and then project it onto finite-dimensional statistical manifolds, yielding an explicit finite-dimensional ODE system. This avoids spatial discretization and is motivated by seismic applications, though the practical algorithmic and statistical guarantees remain partially conditional.

Random reshuffling (RR) is the de facto standard for training machine learning models, yet theory has long lagged practice for last-iterate convergence. Existing guarantees required stepsizes as small as $1/(nL)$, while averaged-iterate analyses allowed $1/L$. Chen and Zhang close this gap for smooth convex objectives, proving that the last iterate of RR-SGD converges with stepsizes up to $1/(L(1+\log(nK)))$---removing the explicit $1/n$ barrier at the cost of only logarithmic factors. The result matters because practitioners return the final parameter, not an average, and large constant stepsizes are common in deep learning.

Bayesian posterior sampling offers a potentially 'free' source of differential privacy, but existing guarantees are fragmented and often require strong boundedness assumptions. This paper develops a unified Rényi-divergence framework that characterizes the inherent privacy loss of One-Posterior-Sampling (OPS) through exponential moments of the single-record log-likelihood ratio. The results yield exact, tighter bounds for a broader class of models and clarify how prior strength, sample size, and neighboring relations jointly shape privacy.

The paper tackles whether iterative image generation with flow matching can actually perform deep sequential reasoning, or whether the many sampling steps are merely an illusion of compute. It proves that vanilla flow matching collapses to constant-depth circuits because each ODE step changes state by only $O(1/K)$, but shows that self-conditioning—feeding back an endpoint prediction as an internal scratchpad—breaks this barrier and can reach $\mathsf{P/poly}$ and even $\mathsf{PSPACE/poly}$. The result is a sharp theoretical and empirical argument that how information flows between sampling steps matters more than how many steps you take.

Unsupervised feature selection often forces practitioners to hand-tune regularization parameters in sparse PCA models or to collect large labeled datasets for deep learning. This paper proposes MDUFS-Net and MDUFS-Net+, which unfold an ADMM solver for $\ell_{2,0}$-norm regularized PCA into a trainable deep network, embed a Stiefel manifold projection to preserve orthogonality, and optionally add element-wise $\ell_0$ sparsity. The work matters because it tries to retain the interpretability of model-driven optimization while automating parameter adaptation via gradient-based learning.

The paper solves zero-shot stitching of autoencoders: enabling an encoder from one independently trained model to work with a decoder from another. The core idea is a training-time topological regularizer based on 0-dimensional persistent homology (MST edges) that preserves the input data's geometric skeleton in each latent space, so that after training only a lightweight affine map is needed. This matters for distributed and privacy-sensitive settings where encoders and decoders cannot be jointly optimized or share weights.

The paper proposes a two-loop inference architecture inspired by System 1/System 2 cognition: a fast loop (MobileNetV3-Large) classifies an image and, if confidence is low, a medium-speed loop (DINOv2-B) processes additional evidence. The authors evaluate this REASON framework on four fine-grained image classification benchmarks under distribution shift and synthetic corruptions, showing gains over the fast loop alone at roughly half the computation of the full medium model.

This paper tackles the practical problem of choosing detectors and trackers for multi-pedestrian tracking across scenes with different densities. It carries out a broad empirical comparison of the YOLO11, RT-DETR, and YOLO26 detector families paired with ByteTrack and BoT-SORT on MOT17, MOT20, and SOMPT22, including an ablation of NMS versus end-to-end inference for YOLO26. The study offers a useful survey of quality-speed trade-offs, although its conclusions are constrained by out-of-the-box COCO weights and very small benchmark slices.

This PhD thesis tackles novelty detection across weather sensor networks and computer vision. For sensor networks, the core problem is detecting blocked rain gauges that falsely report zero rainfall—a failure mode that is hard to catch because zero is also the most frequent correct reading. The work benchmarks existing quality-control methods and proposes new joint-reasoning algorithms (PCSD-SAT, PCSD-JIGM, PCSD-JIKR) to improve detection of spatially clustered faults. For computer vision, it claims to introduce the NOVEL-SS benchmark and a Detect-Characterize-Accommodate (DCA) baseline for handling multiple simultaneous visual novelties. The research matters because reliable precipitation data underpins agriculture and disaster management in data-scarce regions, while real-world vision systems must cope with diverse, concurrent anomalies rather than single-type novelties.

Equivariant GNNs for molecular modeling are bottlenecked by Clebsch-Gordan tensor products, which suffer from high complexity and limited non-linearity. This paper proposes the Equivariant Spherical Transformer (EST), a plug-and-play module that applies self-attention over sampled points on the spherical spatial domain of group representations, using a Fibonacci Lattice to preserve approximate SO(3) equivariance. The work matters because it reports state-of-the-art results on OC20 and QM9 while empirically breaking the degree-limited expressiveness barrier that constrains traditional tensor-product architectures.

Multi-teacher on-policy distillation (MOPD) fuses specialized RL teachers into one student, but the mechanistic path from supervision choices to parameter changes and task capabilities has been unclear. This paper traces that path through controlled experiments on Qwen3-1.7B and SmolLM3-3B, using identical student parameters, responses, and optimizer states to compare gradients, Adam updates, and learning curves across loss-averaging rules and vocabulary truncations. Its core insight is that stronger gradient approximations do not necessarily yield better task performance; instead, outcomes depend heavily on implicit response weighting and optimizer dynamics that prior recipes treat as secondary.

The paper asks whether effective resistance in tissue-specific protein interaction networks can flag which protein-function predictions a graph neural network will get wrong. Because effective resistance on these networks is almost completely determined by inverse degree, the author strips out that degenerate component and tests whether the residual predicts per-node loss beyond standard uncertainty scores. The study concludes that a reproducible but tiny signal exists across 24 interactomes, and that degree degeneration strongly bounds how useful resistance can ever be on this class of biological graphs.