Monday, 2 Nov 2026

Surprise Alone Learns Lies: Evidence-Gated Continual Learning for LLMs from Streaming Claims

A surprise-driven learner absorbs planted lies as fast as random selection. Its own prior flags contradictions at AUC 0.78 to 0.83.

How Specific Do I Need to Be? Effects of User Query Underspecification on Deep Research Agents

Underspecified queries lower answer quality even when citations stay sound. Broader searches lean on training priors instead of the query.

Youtu-Parsing-Omni: One Encoder, One Schema, All Modalities

One encoder and one schema parse documents, images, audio and video. A 5B checkpoint scores 96.96 overall on OmniDocBench v1.6.

Incentivizing Self-Learning in Large Language Models via Reinforcement Learning

RL that rewards textbook study raised proof accuracy from 51.5% to 79.5%. Gains also hold on out-of-domain subjects.

Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns

Sparse-autoencoder crypto portfolios run at lower volatility through lower correlations. They still do not beat a simple contrarian strategy.

Accuracy is not compatibility: two Evo 2 checkpoints hinge on one FP8 matrix multiplication

One FP8 matrix multiplication decides whether Evo 2 checkpoints work on new GPUs. Fixing it lifts BRCA1 AUROC from 0.434 to 0.869.

Grounding Multimodal Reasoning with Evidence-Ablated Negatives

Evidence-ablated negatives stop RLVR models from answering without reading the image. Masked patches expose shortcuts the policy then suppresses.

WorldProbe: When Video World Models Fall Apart under Long-Horizon Spatial Exploration

Video world models keep local quality but lose long-range memory while exploring. WorldProbe runs 1,600 rollouts across 10 models.

MRC-MMAgent: Reinforcement Learning for Multi-Role Collaboration in Multimodal Tool-Using Agents

Multi-role RL lets a multimodal agent inspect evidence and resume when support is weak. It trains on the original baseline dataset only.

Lost in the Latent Space: How Code Embeddings Represent Functional Intent and Implementation Fidelity

Across 14 code embedding models, style fidelity trades off against semantic generalization. Similar retrieval scores hide very different behaviour.

Tokenizer Tokens Tell Training Tales

Rare tokens reveal whether a candidate dataset trained the tokenizer. Existing defenses keep leaking membership at production vocabulary sizes.

Low-Bit GDN Recurrent-State Quantization: Global Gradient-aware Allocation and Pure-Write Sliding Windows

Linear attention stores historical information in a recurrent state of fixed size, providing a compact memory representation for long-context inference.

Attention, Dynamic Wave and Hybrid Sequence Mixers in Language Models

Large Language Models (LLMs) rely on Multi-Head Attention (MHA) for sequence modeling, but scaling to long contexts is constrained by the quadratic cost of attention, motivating sub-quadratic recurrent alternatives.

KV Cache Quantization for Diffusion Language Models

KV cache quantization is well studied for autoregressive language models, where cached prefix states are exact under causal attention and quantization is mainly a cache reconstruction problem.

DWISE: Denoising Workload-Informed Scheduling Engine

Unlike autoregressive large language models (AR-LLMs), diffusion LLMs (dLLMs) generate text by iteratively denoising many tokens in parallel, offering a promising path to efficient inference.

Grammar Control for Language Learning with Sequential Monte Carlo and Small Language Models

Second language learners benefit from exposure to level-appropriate grammar in interactive language use.

Activation Probes Are Reliable With a Handful of Positive Examples

Efforts to monitor advanced AI for rare misalignments face a data challenge: abundant aligned examples but only a handful of misaligned ones.

When the Top Answer Decides: Gender Bias in Decision Models

Decision models are language models built to route, classify and triage: they return option probabilities from one forward pass, and an application acts on the top answer.

End-to-End Guarantees for Masked Diffusion Models under Conditional Mixing

Masked diffusion models train a denoiser on masked cross-entropy and generate by unmasking many positions in parallel.

Softmax Routing Dynamics in Mixture-of-Experts Models: Collapse and Recovery

Softmax routing in mixture-of-experts models can concentrate probability on a subset of experts, suppressing access to the others.

ARBITRA: Robust Document-Level ATT&CK Extraction via Multi-Expert Argumentation

Cyber threat intelligence (CTI) reports describe adversary behavior in natural language, but converting them into structured MITRE ATT&CK mappings remains difficult: existing methods decompose reports to sentence-level granularity for extraction, while modern LLMs can work with full reports in a single pass but with noticeable performance degradation.

LinQuant: Functional Invariances for Low-Precision Linear Recurrences

Linear recurrences are emerging as practical alternatives to softmax attention in large language models, appearing in both standalone and hybrid architectures.

Dobb: Shard-Local Preconditioning in Block-Diagonal Eigenbases

Training language models whose parameters and optimizer state exceed a single node's memory requires sharding these tensors across devices.

Robustness of Chatbot LLM Judgments of Presidential Actions and Hypothetical Reforms

Chatbots are increasingly consulted about public affairs, and their judgments of policies are useful only if they are robust.

Does the Citation Matter? Quality, Grounding, and Physician-Rubric Accuracy of Google's AI Overviews for Medical Information

Google's AI Overviews (AIOs) are the first result users encounter when searching the web, shifting the work of selecting and evaluating sources from the user to Gemini.

Can Image Agents Complete Visual Commissions? A Benchmark and Modular Framework

Visual commissions require image agents to integrate heterogeneous source materials, preserve requirements across dependent stages, and produce coordinated deliverables.

IMRouter: Imbalance-aware Temporal Pattern Routing for LLM-based Time Series Forecasting

Accurate time series forecasting (TSF) is essential for Web applications, such as traffic monitoring and online resource management.

AgentHeal: Evaluating Goal-Preserving Resource Recovery of Web Agents under Access Barriers

Large language model (LLM) agents have demonstrated strong capabilities in automating complex information-seeking and transactional tasks across the open web.

XLaw-JP-27B: A Lightweight Open Model Specialized for Japanese Law

We present XLaw-JP-27B, an open-weight model specialized for Japanese law that runs on a single 48\,GB GPU under the Apache~2.0 licence.

Pair-Dependent Judge Errors Change the Minimax Rate of Semantic Entropy

Semantic entropy, the entropy of the meanings among a language model's sampled answers, is estimated from the verdicts of a judge that declares pairs of answers equivalent or not, and automatic judges err in ways that depend on the answers they compare.

VEvo-Harness: Causally-Guarded Self-Evolving Harness for Agentic Image Generation

The central bottleneck in visual generation and editing has migrated from achieving pixel-level fidelity to satisfying densely constrained user specifications through increasingly agentic workflows.

HALO: Human-Grounded Granular Evaluation of Proactive Agents

Proactive agents must decide whether, when, and how far to assist without an explicit request.

HERA: Assessing an Autonomous Agent for Quantum Device Measurement

Large language model agents have begun to carry out laboratory measurements, but their scientific conclusions are useful only if they hold up under scrutiny.

Certified Access: Test-Time Compute on Conservative Physics-Structured AI Memory

Test-time compute mechanisms, such as retries, confidence-based escalation and non-local routing, are conventionally deployed as black-box additions to trained models.

When the Same Prompt Gives Different Answers (and the Same Answer Isn't Always Right): A Taxonomy of Failure Mechanisms, Testing, and Mitigation Strategies for LLM-Assisted Evidence Synthesis Read-and-Evaluate Tasks

LLM read-and-evaluate tasks include study screening, data extraction, risk-of-bias assessment, and critical appraisal in the evidence synthesis field.

PhysAg3D: Bridging 3D Generative and Agentic Models for High-Fidelity and Physically-Interactive Assets

Generating interactive 3D assets from a single image requires realistic appearance, fidelity to the depicted object, and plausible physical behavior.

TokenRepel: Generating Diverse Image Sets Without Compromising Quality

We present an inference-time approach that enhances the visual diversity of image sets generated by text-to-image models, without compromising the average image quality.

sDMD: Sinkhorn Distribution Matching Distillation

Distribution Matching Distillation (DMD) distills multi-step diffusion models into few-step generators by minimizing reverse KL divergence.

What Survives Label Downsampling? Exact Region Survival for Coarse Segmentation Targets

Segmentation networks that predict at an output stride are trained against label maps downsampled by strided sampling, nearest-neighbor resampling or majority pooling.

Parametric Gaussian Noise Mixer (PGNM): Unified Synthesis of Camera Noise and Film Grain

Noise synthesis is commonly approached through physical models of image formation or neural networks trained to generate noisy images.

M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models

Prompt-tuning has emerged as a parameter-efficient technique for adapting large-scale vision-language models (VLMs) to downstream tasks while keeping the pre-trained weights frozen.

Open-set multi-view seahorse re-identification from underwater photograph sequences

Automated animal re-identification from photograph sequences can facilitate minimally invasive Capture-Mark-Recapture studies at scale.

GROME: GROunding Factorized MEmory into Video World Models

Long-horizon consistency is essential for video world models, yet it becomes difficult to maintain once observed content falls outside the context window.

SCOPE: Semantic COdec Pre-Editing for Object-Preserving Compression

Image compression is increasingly used before machine vision systems, yet standard codecs are mainly optimized for human perception rather than downstream task accuracy.

Quantum-Inspired Convolutional Networks for High-Resolution Pseudo-Label Generation from Multimodal Earth Observation Data

Accurate land-cover mapping from multimodal Earth observation (EO) data remains a challenge in regions where high-resolution annotations are scarce or costly to obtain.

Cross-Encoder Anchor Refinement for Zero-Shot Anomaly Detection

Zero-shot anomaly detection aims to transfer anomaly discrimination from auxiliary training domains to unseen categories while localizing subtle defects.

NIF: Normal-Intersection Fields for Geometrically Consistent Oriented Box Refinement

Accurate oriented detection requires a box to follow object boundaries, yet a standard regression head predicts its center, size, and angle from a pooled region representation.

Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift

Video-based gait assessment often keeps only high-confidence pose observations, which can lower measurement error but change the movement assessed.

DEQ-Stereo: Deep Euilibrium Video Stereo Matching via Adaptive Convergence

Iterative refinement models have significantly improved stereo matching accuracy, but their rigid, fixed update schedules incur heavy computational costs and a strict trade-off between speed and accuracy.

EditAgent: Rethinking Agentic Image Editing

Agentic image editors decompose complex requests into steps, but when a step fails, they mostly retry with the same narrow set of tools and a judge that scores each output by eye directly.

AB-Attention: Accurate Block Attention for Accelerating Video Diffusion Transformers

Video diffusion transformers generate high-quality videos, but the quadratic cost of 3D full attention dominates their inference.

Look at Where You Think: Localization-Head Alignment for Fine-Grained Perception in Thinking VLMs

Thinking vision-language models reason for thousands of tokens, yet still miss small details in high-resolution images.

Annotation-Free Representation Learning for Polyp Retrieval in Colonoscopy Videos

Colonoscopy is inherently comparative: reliable visual assessment of polyps often depends on complementary views acquired under different viewpoints and imaging conditions.

FlexTry-On: A Time-Efficient Framework for Multi-Scenario Virtual Try-on

Virtual try-on has achieved remarkable progress in recent years, yet most existing methods are designed for a specific scenario, such as cloth-to-person or person-to-person try-on, and often require separate conditioning strategies and processing pipelines for different settings.

FutureWorld: A Plug-and-Play Causal Next-State Predictor for Interactive Video World Exploration

Interactive video world models aim to follow user actions while maintaining scene consistency over extended interactions.

UniTele: Learning Device-Conditioned Degradations for Unified Smartphone Telephoto Super-Resolution

Super-telephoto imaging on mobile phones suffers from complex, device-dependent degradations that are poorly captured by conventional handcrafted pipelines.

DiFuse: Fusing Native Diffusion Representations for Spatial Reasoning in Unified Multimodal Models

Unified multimodal models (UMMs) jointly support visual understanding and image generation, yet information flow between these two capabilities remains largely asymmetric: understanding representations routinely condition generation, whereas generative representations are rarely reused for multimodal reasoning.

Evaluating Cultural Knowledge Editing in Vision-Language Models

Current Vision-Language Models (VLMs) frequently fail to recognize culture-specific concepts, especially from underrepresented regions.

TactoHand: Learning Geometry-Based Dense Hand-Surface Interaction Fields from Video

Understanding how humans interact with the world requires a representation of physical interactions at the hand surface.

LatentWeave: Test-Time Latent Optimization for Multimodal Reasoning

Recent advances in test-time optimization have shown that the multimodal reasoning performance of large multimodal language models (MLLMs) can be improved during inference without modifying backbone model parameters.

BEACON: Distilled Begin/End-Aware Structured Decoding for Continuous Sign Language Segmentation

In segmentation-based continuous sign language recognition, an untrimmed video is first partitioned into lexical units; segmentation is therefore a means to an end---downstream recognition.

Comprehensive empirical evaluation of feature extractors in computer vision

Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications.

ROAD: Modern Adversarial Training for Simple One-Step Distillation at Scale

One-step distillation turns pretrained diffusion models into fast generators, but strong results often require substantial training machinery and compute.

MOSAIC-Flow: Orthogonal-Axis and Status-Conditioned Fusion for Missing-Sequence Completion in Brain Tumor MRI

Missing-sequence completion is important for comprehensive brain tumor characterization, but cross-modal fusion can obscure sequence-specific contrast and lose fine-grained three-dimensional tumor detail.

MorphSIG: Subject-Driven Image Generation via Decoupled Anchoring and Feature Transport

We propose MorphSIG, a training-free framework that reformulates subject-driven image generation (SIG) as pseudo-video feature transport.

FFAI: Flow Matching with Feedback Training and Adaptive Initialization for CT Reconstruction

Although low-dose, sparse-view, and limited-angle CT involve distinct degradation mechanisms, all three pose reconstruction challenges due to limited measurement information and can therefore be addressed within a unified framework.

Robust LIDAR Sensor-to-Sensor Domain Generalization via Accumulated-Point Scene Completion

We study domain generalization for LIDAR semantic segmentation.

Regional Anchor Routing for Multi-Object Motion Transfer

Compositional motion transfer requires a model to extract motions from multiple reference videos, and apply each motion to a user-specified object in one target scene.

Calibrating Attention-Based Token Pruning for Efficient Multi-Image Synthesis in UMMs

Unified Multimodal Models (UMMs) enable powerful multi-image synthesis but incur substantial computation from long visual contexts.

PathCowork: Learning from real-world pathology practice to build AI copilots

Medical AI learns from the answers doctors record, but rarely from how they interpret the evidence to reach them.

Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning

Multimodal large language models can recognize visual content yet struggle to reason reliably over it across multiple steps.

Decoding Geometry, Not Tokens: Efficient VLMs for Robot Affordance and Trajectory Prediction

Predicting where to grasp and how to move is fundamentally a geometry problem.

Anatomical Prior-Guided Routing for 3D Lung Tumor Segmentation

Accurate lung tumor segmentation in computed tomography (CT) is essential for treatment planning, response assessment, and quantitative imaging analysis.

YOLOF-MLD: A Multi-Level Distillation Framework for Efficient Object Detection

Efficient object detection on resource-constrained edge devices is essential for safety-critical applications such as fire-smoke monitoring.

ACD-GS: Asymmetric Curvature-aware Densification for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time photorealistic novel view synthesis.

CASE: Concept-Aware Segmentation from Positive and Negative Visual Exemplars

Few-shot semantic segmentation typically identifies target objects based on positive support examples of a single semantic class.

PicturePerfect: Diffusion-based Same-identity Facial Replacement

With the growing success of diffusion models in computer vision, we explore their potential for same-identity facial replacement in photographs.

MemoryFusion: Cross-Temporal Memory Learning for Multimodal Video Fusion

Multimodal videos contain rich temporal cues that can facilitate faithful fusion.

Unlimited OCR Works 2

Unlimited OCR ushers the era of long-horizon parsing, substantially mitigating the growth of working memory as document lengthens.

Unlocking Any-Order Generation in Pretrained Autoregressive Image Models

Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the model, leaving it unable to inpaint, outpaint, or edit at inference time.

PriorMaskMap: Robust Online Vectorized Map Construction with Biased Priors

Online vectorized map construction provides autonomous vehicles with essential, real-time semantic and geometric scene understanding.

Efficient Covariance-Aware Vicinal Latent Diffusion for Continuous Conditional Image Generation

Continuous Conditional Generative Modeling (CCGM) aims to synthesize high-dimensional data conditioned on continuously valued scalar labels.

Seeing and Imagining as One: Unified Video Generation and 4D Perception for Embodied World Modeling

Embodied world models should not only imagine plausible futures, but also perceive the geometric, semantic, and motion structure of the evolving world.

COGS: A Diagnostic Benchmark for Attribute-Aware Object-Level 3D Gaussian Splatting Perception

3D Gaussian Splatting (3DGS) is increasingly explored as an input representation for 3D perception.

Multi-level Constrained Learning for Underwater Image Enhancement

Underwater image enhancement aims to correct color and recover detail degraded by wavelength-dependent attenuation, scattering, and noise.

Edited Endpoints as Partial Goals for Image-to-Video Generation

Edited endpoint images make the target of an image-to-video transition explicit and can improve transition completion, but also preserve context in the source image's layout.

EgoContact: Evaluating Physical Intelligence with Egocentric Video

Egocentric video has emerged as one of the most scalable sources of data for learning generalizable manipulation priors.

Joint Rigid Alignment and Morphable Reconstruction from Partial 3D Face Scans

Aligning a partial, real-world 3D face scan to a canonical representation requires solving two coupled problems at once: estimating the rigid pose that brings the scan into a common frame, and completing the facial shape in regions the scan never observed.

CLaW: Codec-Guided Adaptive Latent Watermarking for Traceable Diffusion Image Generation

With the rapid advancement of text-to-image diffusion models, increasingly realistic AI-generated content has raised serious concerns about misuse and copyright infringement.

Kinematic Causal Attribution: Black-Box Explanations of Motion versus Geometry for 4D Point-Cloud Video Models

4D perception models, which classify 3D point-cloud videos, are being built into robots, assistive monitoring and AR, yet before deploying one a practitioner cannot check which cue it relies on: how a body part moves, or what shape it has. 3D attribution is single-frame, video attribution is confined to RGB pixels, gradient maps need white-box access, attention is absent from state-space backbones, and point-level perturbations assume a point correspondence across frames that real depth and LiDAR data lack.

Optimal Contextual Pricing in Non-Stationary Environments

We study contextual dynamic pricing in a non-stationary environment.

Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem

The rapid expansion of digital banking and mobile financial services creates growing challenges for fraud detection, particularly under severe class imbalance and operational constraints.

Reading-Level Verification of Kanji Words in Japanese Financial Speech Synthesis

Few-shot voice cloning makes it cheap to produce daily Japanese financial commentary with several synthetic speakers, but kanji words with more than one reading, such as the word for "market" (shijo vs. ichiba) or "overall trend" (taisei vs. oze), are still misread.

PALATE: Pretrained Autoregressive LATent Embeddings

Local-commerce and food-delivery marketplaces run large ads businesses: across hundreds of thousands of stores, merchants pay to be surfaced on browse and search surfaces, and a ranker decides which ads each consumer sees.

Last-Iterate Convergence for Constrained MDPs: From Dual Descent to Primal-Dual

Dual descent gives last-iterate guarantees without coverage assumptions. An inner-loop-free primal-dual method improves those guarantees further.

Achieving Minimax-Optimal First-Order and Best-of-Both-Worlds Regret Simultaneously in Multi-Armed Bandits

We study first-order and best-of-both-worlds regret guarantees in multi-armed bandits.

Smooth Action Chunking Via Movement-Primitive Representations for Imitation Learning

Imitation learning has become a widely adopted approach for vision-based robot policies, owing to its conceptual simplicity, scalability, and ability to leverage large-scale datasets.

Satisficing Regret Minimization in Bandits with Knapsacks

We study satisficing regret minimization in bandits with knapsacks (BwK), where a learner sequentially selects randomized policies, i.e., distributions over arms, to obtain mean rewards above a satisficing threshold as frequently as possible while respecting finite resource budgets.

Strongly Adaptive Online Learning Achieves Uniform Discounted Regret

Discounted regret measures the performance of an online learner by assigning exponentially decaying weights to past losses, reflecting the greater importance of recent losses in non-stationary online learning.

Event2Reward: Learning Robotic Rewards from Progress Relations

Learning task progress from robot demonstrations provides a practical way to construct dense shaping rewards for robotic reinforcement learning.

Data-driven Random-feature Approximation of Koopman Generators of Nonlinear Continuous-time Systems

Learning continuous-time dynamical systems from data is an active area of research.

TACFormer: Transformer-Based Generative Model for Target-Conditioned De Novo PROTAC Design

Targeted protein degradation (TPD) represents a fundamental shift in drug discovery, moving beyond traditional target inhibition toward the event-driven removal of disease-causing proteins.

Probabilistic Meshless Methods on Manifolds

Probabilistic meshless methods solve partial differential equations (PDEs) by placing a Gaussian process prior on the solution and conditioning it on the PDE at a finite set of collocation nodes.

Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making

Decision-making under uncertainty remains a key issue in Machine Learning (ML), particularly in the deployment of safety-critical systems, where reliable quantification of system behavior is essential for evaluating behavioral impacts and strengthening confidence in ML models.

AI at a Turning Point: From ChatGPT through Neural Networks to Neuromorphic AI

The tension between the established technologies of connectionist AI and their neuromorphic challengers is attracting growing attention, above all because of the enormous energy demand of the former and the low power consumption of the latter.

Diagnosing Optimizer-Landscape Interaction via Representation in Subset Selection Multitasking: Evidence from Piano Fingering

Continuous relaxations are commonly used as an encoding scheme for evolutionary multitasking optimization (EMTO), allowing heterogeneous tasks to be optimized within a unified search space.

Surface Conditioning for Zero-Shot Multivariate Correction in a Marine Biogeochemistry Emulator

Bias correction in process-based models usually means iterative data assimilation with explicit covariance modelling.

Going deep, reaching further: What can Boltzmann machines efficiently represent?

While universal approximation theorems theoretically underpin the representation capabilities of machine learning models, they have two shortcomings: They do not focus on efficient representations and they tend to be insensitive to depth and other heuristically important properties.

Grounding Predictive Reliability through Masked Observability

An uncertainty estimate is meaningful only if its semantics can be tied to observable predictive behavior.

Implicit Bias of (Stochastic) Gradient Descent on Deep Linear Diagonal Networks

Modern machine learning models are often overparameterized, i.e., have more parameters than training data.

Adaptive Federated Optimization Across Smooth and Nonsmooth Regimes

AdaGrad-style methods can simultaneously adapt to both the order and constants of smoothness in centralized optimization.

Bayes-Consistent Losses for Adaptive Inference in Classification

Adaptive inference systems balance accuracy and inference cost by routing inputs to models with varying capabilities.

Smartphone Indoor Localization Enhanced by Hierarchical Multi-Sensor Context Recognition

Smartphone indoor localization requires low-cost, robust positioning across scene transitions and phone-use variations.

Transformer-inspired architectures make machine-learned electron densities accurate and transferable enough to bypass self-consistency

We introduce machine learning models that accurately predict the electron density, the central variable of density functional theory.

Formalizing the Intrinsic Dimension of Objective Landscapes by a Generalized Notion of Co-dimension

In this work, we formalize the concept of intrinsic dimension of objective landscapes introduced in Li et al.

A New AFIB Detection Method with Deep Neural Networks

Early detection of atrial fibrillation (AFIB), characterized by irregular heart rhythms, can potentially save thousands of lives annually [20].

Carbon-Aware Electric Vehicle Routing Problem with Decision-Focused Learning

Electric vehicles produce no tailpipe emissions, but charging them produces emissions that depend on the time-varying generation mix of the electricity consumed.

GC-LIF: Spiking Group-Equivariant Neural Network With Group-Coupled Membrane Dynamics

Spiking neural networks (SNNs) promise event-driven, low-power inference, but on vision tasks they still acquire rotation invariance only through data augmentation.

Improving the Intrinsic Interpretability of Differentiable Decision Trees through Euclidean Feature-Concentration Regularisation

Differentiable decision trees (DDTs) support end-to-end optimisation and explicit decision paths, but dense oblique splits can obscure the role of individual input features.

Operator-Aware Posterior Sampling for Multivariate Weather Downscaling

Statistical downscaling is the task of reconstructing high-resolution meteorological fields from coarse inputs.

DOSE: Dominance-Guided Ordinal Severity Estimation from Sparse Relative Annotations

Medical severity estimation commonly relies on absolute grade annotations, which are fundamentally ordinal, yet existing methods often treat the grades as independent categories.

Learning Kolmogorov Backward Operators for Spectral Analysis and Forecasting: A Data-Driven Variational Galerkin Framework

For a time-homogeneous Markov process, the fixed-lag solution operator \( (T_τf)(x)=\mathbb E[f(X_{t+τ})\mid X_t=x] \) of the Kolmogorov backward equation evolves observables linearly and deterministically, even when the dynamics are stochastic.

Hidden Phase Transition in Single-Polymer Stretching under Elongational Flow

Using machine learning, we identify a second transition in polymer chain stretching driven by planar elongational flow, beyond the well-known coil-stretch transition.

Diffusion models for the $O(3)$ nonlinear sigma model

A score-based diffusion model is trained on the two-dimensional classical Heisenberg model, the lattice representation of the $O(3)$ nonlinear sigma model.

Trajeform: Embedding Crowd Data

Crowd simulation aims to generate agent behaviours that are rich, natural, and appropriate to their surrounding environment.

Quantifying the Relay-Coverage Trade-off in Mobile Multihop Flooding

Network-wide flooding in mobile multihop networks selects relays from locally maintained topology information.

TabCUQ: Context-Uncertainty Quantification for Tabular Foundation Models

Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatment effects for specific groups of individuals.

The Cost of Working Memory in Streaming Mean Estimation

A streaming estimator must compute from the evidence that fits in memory.

Conic-Lifting for Learning-rate-free Convex Optimization

A central topic in online and stochastic optimization is to design algorithms that do not require tuning of hyperparameters.

BRIDGE: Learning Brain-Gut Metabolic Evidence for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) diagnosis may benefit from gut metabolic information associated with brain-gut interactions, yet existing models largely analyze cerebral glucose metabolism alone.

Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties

Accurately predicting the transport properties of liquid electrolytes is critical for next-generation battery design and requires force fields that are both quantum-mechanically accurate and fast enough for long-timescale simulation.

Interpretable by Design Tabular Prediction via In-Context Local Linear Learning

In-context learning (ICL) lets tabular foundation models make predictions without dataset-specific training or fine-tuning.

Topology Survives Shuffling: Robust Long-Context Memory Access Prediction for Graph Analytics

Graph analytics generates irregular, topology-dependent memory-access streams via indirect traversals, limiting data prefetching.

CBE: A Complexity-adaptive Boundary-aware Ensemble Method for Imbalanced Data

In many real-world classification tasks, datasets exhibit class imbalance, which can degrade the generalization performance of conventional learning algorithms.

More Is Better? Exploring Latent Correspondence in Federated Hypergraph Learning

Federated hypergraph learning with latent cross-client hyperedge correspondence poses fundamental identifiability and computational challenges, as the same local release can correspond to multiple global hypergraph structures.

MulClip: A table-free activation function with monotonic and folding regimes for reservoir computing on microcontrollers

This letter proposes MulClip, a lightweight activation function for reservoir computing (RC) on microcontrollers (MCUs).

Class-aware Latent Attention Replay (CLAR): Class Incremental Hazard Diagnosis for Resilient AI in the Manufacturing Industrial Internet

Artificial Intelligence (AI) systems provide critical computation services in the Manufacturing Industrial Internet (MII), and their resilience depends on timely diagnosis of hazards across the data, AI pipeline, and cyber-physical layers.

BEVLight: Generalizable Traffic Signal Control from UAV Bird's-Eye Views

Learning-based traffic signal control (TSC) has achieved promising performance, but most methods rely on structured traffic states that must be estimated from sensor observations in real deployments.

TaxoMFM: A Taxonomy-Aware Foundation Model for the Human Gut Microbiome

Existing gut microbiome foundation models typically use flat taxon representations, limiting their ability to exploit known taxonomic hierarchies.

CAD-WAM: Anticipating Geometry for Text-to-CAD Generation

Generating editable computer-aided design (CAD) models from text requires selecting operations that produce the intended changes to the geometry constructed so far.

Multi-Solution Inverse Design of Pixelated Patch Antennas Using an Autoregressive Transformer

Deep-learning-based inverse design has emerged as a promising framework for pixelated patch antenna synthesis by enabling broader exploration of discrete geometry spaces and reducing design time compared with conventional template-based methods.

Fast rigid rotational alignment of voxelized 3-D density maps in sliced Wasserstein distance

We develop a fast method for the rigid alignment of voxelized three-dimensional density functions over $\mathsf{SO}(3)$ using sliced optimal transport.

Analyzing Condensation on Patterned Surfaces via Deep Learning

Dropwise condensation is key to power and water systems.

Zero-Complement Extrapolation: Parameter Recovery and Classification in Severely Imbalanced Logistic Regression under Rank-Deficient Regimes

Parameter estimation and classification in binary logistic regression deteriorate severely when the number of minority class observations ($n_1$) is smaller than the feature dimension ($p$).

ACAM-J-MARS: Multi-Atlas Riemannian Spectral Representations for Decoding Advanced Meditation States from fMRI

Inferring jhana-type advanced concentration absorption meditation (ACAM-J) states from fMRI data is challenging due to the spatially distributed nature of meditation-related effects, substantial inter-individual variability that can exceed within-subject state differences, and limited sample sizes.

A Novel Approach to Evaluating Evaluation Metrics for Multi-Output Structured Prediction

In multi-output structured prediction tasks, while only one ground truth label may be provided in the training data, multiple equally valid outputs may be possible, making reliable evaluation a persistent challenge.

Learning Erasing Equal-Length Relational Pattern Languages in the Limit from Positive Data over Binary Alphabets

Relational pattern languages generalize Angluin's pattern languages by requiring the strings substituted for repeated occurrences of a variable to stand in a given relation, rather than to be identical.

STRATA: Reinforcement-driven Active Sampling with Tri-paradigm Adaptation for Efficient 3D Spatial Transcriptomics

Spatial transcriptomics (ST) enables joint characterization of tissue morphology and gene expression.

DICE: Decentralized Incentive-driven Federated Learning for Efficient & Scalable Customization of Pretrained Models in Web3

Customizing Pretrained Models (PMs) for downstream tasks demands task-specific data, which burgeoning Web3 applications provide abundantly.

Uncertainty-Guided Cross-Modal Alignment for Diffusion-Based IoT Intrusion Detection

Diffusion models evolve noisy states toward a learned data distribution, so their sampling behavior depends on the geometry of the representation in which denoising occurs.

Tactus: Tactile & Pose Sensing Glove for Natural Dexterity Data Collection

Collecting naturalistic human manipulation demonstrations with accurate contact force and hand pose is essential for robot dexterous manipulation policy and foundational world model learning - yet no existing wearable platform provides both calibrated fingertip force sensing and occlusion-resilient hand tracking in a form factor that preserves natural manipulation behavior.

PhysicsNeMo-Mesh: GPU-Native Meshes and Simulation Cases for Physics AI

PhysicsNeMo-Mesh is the mesh module of the open-source PhysicsNeMo library.