This tutorial reframes model validation as the design of a performance estimator rather than a mere software setting. It surveys splits from hold-out to nested group cross-validation, links geometric fold choices to the correct independent unit (record, subject, center), and provides eight controlled leakage scenarios grounded in biomedical examples such as EEG epochs and paired-eye OCT. The core argument is that an elaborate fold structure is scientifically invalid if applied to the wrong unit, and that even a simple hold-out can be rigorous when the unit, size, and isolation constraints are correct.

Technical documentation preserves collaborative context, but sentence-level specificity predictors were trained on general-domain prose. This paper audits whether two such predictors—SpeciTeller and a pinned post-publication implementation of Ko et al.'s domain-adapted model—produce stable, useful signals when applied to technical corpora (GitHub, Ansible, Python docs) and used to rank LLM-generated revisions. Using a three-stage evidence structure, the authors separate score interpretation, local correspondence with humans and controlled edits, and bounded decision value for post-generation candidate selection.

Coordinated multi-humanoid loco-manipulation is notoriously difficult because it couples high-dimensional whole-body control with decentralized decision making and scalability. MASkillBlender addresses this by learning a shared decentralized high-level multi-agent RL policy that blends frozen, pre-trained single-humanoid primitive skills, eliminating the need for task-specific motion references or painstaking per-task reward engineering. The authors further introduce a permutation-based data augmentation strategy and prove, under a homogeneous Markov game formulation, that it preserves the policy-gradient direction. Simulated experiments across cooperative manipulation and locomotion tasks suggest broad versatility across robot embodiments and simulators, though the work stops short of real-world validation and is limited to small, homogeneous teams.

Training hybrid Transformer-SSM models requires expensive per-scale learning rate (LR) tuning. This paper empirically shows that "naïve" Maximal Update Parameterization (μP) — as originally prescribed for pure Transformers, with no SSM-specific modifications — combined with AdamW achieves near-zero LR transfer gap across hybrid architectures from width 256 to 2048 (8×) and depth 4 to 32 (8×), up to billion parameters and on a production Nemotron-H model. The authors propose a two-condition decomposition (global update-to-weight invariance from μP, local per-component balance from AdamW's g/√v normalization) to explain why coordinate checks fail while transfer still succeeds.

Model merging for LLMs typically requires expensive benchmark evaluations to tune combination coefficients. This paper proposes SLIM, which treats coefficient selection as a classical mixture experiment on the simplex. It builds a quadratic surrogate of aggregate performance using only $T(T+1)/2$ evaluations—individual experts and equal-weight pairs—then optimizes the surrogate without further measurements. The core finding is that this minimal structured design predicts unseen mixtures accurately and yields competitive merges at a fraction of the cost of evolutionary or surrogate baselines, though restricting coefficients to the simplex may limit absolute performance.

Counterfactual generation aims to sample the distribution of outcomes that would occur if a treatment were applied to an entire population, but observational data suffer from selection bias because treated individuals differ systematically from the target population. This paper develops a flow-matching method that learns to transport observed outcomes to counterfactual targets using a learned source-target coupling, a doubly robust training objective, and a score-corrected stochastic sampler with Gaussian smoothing. The core theoretical result is a finite-step KL bound governed by the coupling displacement moment $D_5$ rather than worst-case regularity, yielding near-linear dimension scaling and an end-to-end nonparametric sample-complexity guarantee; experiments on synthetic and semi-synthetic image data support the predicted coupling dependence and show that stochastic sampling can outperform deterministic counterparts at limited discretization budgets.

Time series anomaly detection benchmarks traditionally optimize for detection accuracy while neglecting explainability and interpretability. This paper introduces SHAD, a real-world benchmark comprising 215 high-dimensional multivariate time series collected from distributed cloud storage systems, complete with per-dimension labels and rich semantic annotations. The authors evaluate 18 detection algorithms, probe spatial attribution for explainability, and test frozen LLMs for interpretability, demonstrating that accurate detection does not guarantee diagnostic utility and that zero-shot semantic interpretation remains largely unresolved.

Most interpretability studies ablate or probe a single training checkpoint, leaving it unclear whether observed depth-dependent sensitivities are stable or transient. This paper tracks whole-block identity bypass across five released pretraining trajectories and eleven model-domain combinations to ask what persists and what shifts. The core finding is that the relative depth ordering of block sensitivities remains recognizable even as the magnitudes redistribute, and that the intervention pathway itself evolves differently across models and perturbation scales. The work matters because it cautions against treating single-checkpoint ablation profiles as static descriptions of a model's internal organization.

The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian mixture is a convex program over an infinite-dimensional space, yet its maximizers can be nonunique and are only known to have at most $n$ atoms. This paper shows that a vanishingly small random reweighting of the likelihood---using independent Gamma weights that concentrate around one---selects a unique maximizer with polylogarithmically many atoms in any fixed dimension $d$. The resulting estimator retains a Hellinger rate that is parametric up to logarithmic factors, solving the open problem of exact sparsity for multivariate Gaussian NPMLEs without any support penalty.

Looped Transformers reuse shared blocks to scale depth without adding parameters, but MoE variants degrade beyond two loops. This paper diagnoses two bottlenecks—hidden-state drift and expert selection collapse—and proposes LOOM, combining residual scaling, embedding re-injection, per-loop routers, and a Looping Residual. It matters because it attempts to establish recurrent depth as a genuine, compute-efficient scaling axis for sparse LLMs.

Hashimoto and Kawachi study Hamiltonian function learning: predicting $\operatorname{Tr}[f(H)\rho]$ from classical descriptions of a Hamiltonian $H$ and state $\rho$ when the function $f$ is fixed and known. Their core result is a rigorous average-case classical hardness proof for two explicit instances—$f_{\cos,\pi}(\lambda)=\cos(\pi\lambda)$ and $f_{\exp,\beta}(\lambda)=e^{-\beta\lambda}$—obtained by reducing from the hardness of factoring random RSA moduli via Shor's algorithm embedded in projector Hamiltonians. This settles an open problem left by Morohoshi et al. and gives a computational quantum advantage in a quantum machine learning task that has already been demonstrated experimentally on superconducting devices.

GLoC-EHR addresses clinical outcome prediction and reasoning over structured EHRs by equipping a language model with two complementary memories: a compact global trajectory summary and a bounded local set of selected events. The architecture aligns both routes with clinical text through hospital-course generation and masked-concept description recovery, then learns record-grounded, evidence-cited reasoning via GRPO with rewards for answer correctness and lexical evidence matching. Its core contribution is showing that a small multimodal LLM can match strong direct-prediction AUROCs while producing interpretable rationales tied to the patient record.

This paper tackles the rigidity of standard Difference-in-Means (DiM) activation steering, which applies a single global vector to all activations and ignores nonlinear manifold structure. The authors propose Kernelized Activation Steering (KAS), lifting steering into a reproducing kernel Hilbert space so that each activation receives a locally adaptive, implicit update based on kernel similarities to source and target reference sets. The framework strictly generalizes DiM—recovering it under a linear kernel—and connects activation steering to maximum mean discrepancy witness functions, with empirical evaluations on jailbreaking, behavioral control, and image style generation.

Most fairness tests ask whether a model treats groups exactly equally, but real audits often need to decide whether a disparity exceeds a legally or practically meaningful tolerance. This paper formalizes tolerance-based fairness auditing as two distinct statistical objectives—formal violation certification and early-warning sensitivity screening—and develops empirical-likelihood procedures calibrated to each. Constrained empirical likelihood (CEL) controls false violation declarations over a tolerance region, while split empirical likelihood (SEL) and an adjusted variant (ASEL) use an adaptive boundary-proxy principle to increase detection near the boundary. The work matters because it translates abstract fairness requirements into actionable, objective-dependent statistical guarantees.

Treating model release as a design problem rather than a convention, this paper asks how many of $k$ automated tests a model should pass before deployment to guarantee a stated reliability target while retaining as many good models as possible. Using a two-class Gaussian latent-factor model with common within-class correlation $\rho$, the author proves that precision rises strictly with the required pass count $r$, so the optimal gate is the most lenient $r$-of-$k$ rule that meets the target. This matters because teams often default to pass-all gating, which can decimate the pool of acceptable models when tests are even modestly correlated; the paper shows that misjudging dependence by a factor of two can hurt more than shifting the gate by one test.

The paper identifies a failure mode in the standard RLVR pipeline: training one policy with reinforcement learning from verifiable rewards (RLVR) improves single-sample accuracy but can hurt majority-vote performance because RLVR increases correlation among voter errors and collapses output diversity. The proposed fix is an "adapter thicket" — splitting the same training budget across $K$ independent LoRA adapters, each trained on a disjoint random data shard. Across 16 (model, $K$) settings and four models (1.5B–8B), thickets consistently out-vote the fully trained single adapter, and for $K \geq 4$ they protect the base model's vote while keeping most of the single-sample gain.

Detecting cryptic ligand-binding pockets from unbound protein structures is difficult because the pockets are often absent or closed in apo conformations. The authors propose an algebraic counting field (ACF) that maps explicit local structural features into compact, integer-weighted lookup tables so that every residue score can be mechanically reconstructed from training counts and spatial aggregation. The goal is to achieve competitive cryptic-pocket prediction while keeping the inference fully auditable, without relying on sequence homology, structural templates, or deep learning at inference time.

In model-predictive control with the cross-entropy method, a learned world model scores candidate action sequences and refits the Gaussian proposal for the next search iteration, creating planner-induced query shift. The authors disentangle these two roles by saving every CEM candidate pool from four surrogate models, executing all candidates in a resettable environment for a ground-truth ranking, and rescoring each pool with every model in a crossed design; they then splice the first elite set with environment-ranked elites to trace causal consequences through later iterations. Their results show that standard proposal distances contract even as ranking agreement stays near chance and mean disagreement grows, and that a single environment-guided elite update lowers final selected cost—demonstrating that world-model quality must be judged by its effect on the proposal, not just on candidate scoring.

This work investigates how Kolmogorov-Arnold Networks (KANs) scale with training dataset size across three recent variants—BSRBF-KAN, Gottlieb-KAN, and Faster-KAN—on MNIST, Fashion-MNIST, and a magnetic-parameter regression task. The authors fit test loss versus dataset size with a smoothly broken power law, report regime crossings from random-guess to faster- and slower-scaling branches in classification, and analyze how the shapes of learnable edge functions evolve as data grows. The central takeaway is that KAN scaling is architecture-dependent and task-dependent, and that Gottlieb-KAN edge functions collapse into a single shape family rather than diversifying symbolically.

HADRec targets two key gaps in AI-driven medication recommendation: drugs are usually treated as anonymous tokens, and predictions ignore the hierarchical structure of the ATC taxonomy. The proposed framework fuses LLaMA-7B–encoded clinical text with ChemBERTa molecular embeddings via cross-attention, then generates prescriptions through a four-level top-down ATC predictor constrained by a novel consistency loss. This is important because grounding recommendations in both chemical structure and medical ontology could improve accuracy, interpretability, and clinical trustworthiness.