Search: “research”

Temporal Heterogeneous Graph Pretraining for Relational Deep Learning

arXiv:2609.35219v1 Announce Type: new Abstract: Relational deep learning models database rows and foreign-key links as a heterogeneous graph for prediction from record attributes and relational contex

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The Composition Gap in Dataset Distillation

DGX agent

arXiv:2609.34343v1 Announce Type: new Abstract: Dataset distillation compresses a training set into a small synthetic set, usually evaluated one at a time. In federated and data-governance settings, s

researcharxiv-cs-lg

29 Sept 2026

The Exponentially Weighted Signature

arXiv:2603.19198v2 Announce Type: replace-cross Abstract: We introduce the exponentially weighted signature (EWS), a continuous-time model that computes iterated integrals of a path, where each increm

The Extender: A Log-Structured Transformer

arXiv:2609.32759v1 Announce Type: new Abstract: We introduce the Extender, a log-structured variant of the standard Transformer architecture. In a standard Transformer, each layer communicates with su

The Hidden Ratio in Adam: Stable Structure, Compression, and Sign Dynamics

arXiv:2609.35392v1 Announce Type: new Abstract: Adam is the default optimizer for training modern deep neural networks, yet its adaptive behavior remains poorly understood due to the complex interacti

The Impact of Stochasticity on the Rashomon Effect in Machine Learning

arXiv:2609.32934v1 Announce Type: new Abstract: Neural network training is inherently stochastic, with factors such as weight initialization leading to distinct models despite comparable predictive pe

The Judge Is Not Its Twin: Post-training makes a model's writing more predictable but barely moves its taste, as a judge, toward predictable writing

arXiv:2609.32196v1 Announce Type: cross Abstract: Language models are now routinely graded by other language models. If post-training makes a model's own writing more predictable, it may also teach th

The Key Handoff: Retrieval in Hybrid Language Models

arXiv:2609.32942v1 Announce Type: cross Abstract: A two-hop question makes a language model retrieve twice: once to produce a bridge entity, and once to retrieve with it. Transformers resolve that ent

The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning

arXiv:2609.33078v1 Announce Type: new Abstract: Automatic differentiation (AD) lets neural networks compute derivatives of governing equations to machine precision, and this precision has made it the

The Price of Locality: Why Forward-Forward Underperforms Backpropagation?

arXiv:2609.33240v1 Announce Type: new Abstract: The Forward-Forward Algorithm (FFA) replaces backpropagation (BP) with layer-wise local contrastive objectives, eliminating the backward pass and the ne

The Price of Peeking: Anytime-Valid Leakage Detection on ML-KEM EM Traces

arXiv:2609.33597v1 Announce Type: cross Abstract: Side-channel evaluators routinely inspect leakage tests while acquisition is still running, and extend or stop the campaign based on what they see. Fi

The Statistical Cost of Causal Discovery with Feedback

arXiv:2609.34050v1 Announce Type: cross Abstract: What determines the unavoidable sample cost of learning cyclic causal structure? For cyclic linear non-Gaussian models, we study exact condensation re

The Steering Budget: Examples beat Knobs

arXiv:2607.14246v2 Announce Type: replace-cross Abstract: Generative models are steered with knobs -- prompts, guidance scales, property tags. Turn one as hard as you like and, past a point, it stops

The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance

arXiv:2609.31684v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts para

Tilted Schrodinger Bridge Matching

arXiv:2609.34642v1 Announce Type: new Abstract: Schrodinger bridges provide an entropy-regularized framework and a principled solution for unpaired domain translation. In practice, a pretrained bridge

Tokens Change, Structure Endures: Spectral Watermarking for Generated Speech

arXiv:2609.33774v1 Announce Type: cross Abstract: Watermarking is a promising tool for establishing the provenance of AI-generated speech. While many neural audio watermarking methods rely on a separa

TopoMamba: A Load-Support Relation-Guided Multi-Directional State-Space Model for Topology Optimization

arXiv:2609.33688v1 Announce Type: cross Abstract: Deep learning has emerged as an efficient alternative for predicting high-performance material distributions in topology optimization. Existing method

Toward Comprehensive 3D Grounding: Orientation Grounding through Vision-Language Models

arXiv:2609.33109v1 Announce Type: new Abstract: Grounding is a core capability of spatial vision-language models, yet most existing work focuses only on where a referred object is. Many 3D tasks also

Toward Open-World Video Segmentation over Long Horizons

arXiv:2606.15632v3 Announce Type: replace Abstract: Long videos challenge open-world segmentation systems to continually discover objects and preserve their identities through disappearance and reappe

Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling

arXiv:2609.35059v1 Announce Type: new Abstract: 3D anomaly detection (3DAD) aims to identify defective regions in point cloud data, serving as a critical component in industrial inspection systems. Ex

Towards Identifiable Representations under Misspecified Structure

arXiv:2609.33273v1 Announce Type: new Abstract: The presence of noise that depends on the latent variables poses a fundamental challenge to identifiability. Existing results rely on conditional indepe

Towards Transparent Diagnostics: Investigating Architectural Trade-offs and Explainability in Malaria Detection

arXiv:2609.31682v1 Announce Type: new Abstract: More than 80 countries have reported malaria cases with 610 thousand deaths and are projected to increase. Identifying malaria early and accurately help

Training-Free Uncertainty Estimation for Embedding Models

arXiv:2306.00206v3 Announce Type: replace Abstract: Embedding models, often obtained via self-supervised learning, extract general-purpose representations from data. Quantifying the reliability of the

Training Witnesses: Trusting the Training without Trusting the Trainer

arXiv:2609.33915v1 Announce Type: new Abstract: Progress in machine learning cannot outpace our ability to verify it. With an explosion in papers today, every scientific claim rests initially on trust

Transferable Mass Spectrum Prediction via Reference-Guided Test-time Specialization

arXiv:2609.35649v1 Announce Type: new Abstract: Tandem mass spectrum prediction supports compound identification across metabolomics, natural-product discovery, and environmental analysis. However, pr

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems

arXiv:2509.14025v2 Announce Type: replace Abstract: Modular Aerial Robot Systems (MARS) consist of multiple drone modules that are physically bound together to form a single structure for flight. Expl

researcharxiv-cs-ro

TreeRef-BFN: Equivariance-Free De Novo Molecule Generation based on 2D Topology and Internal 3D Geometry

arXiv:2609.32502v1 Announce Type: cross Abstract: De novo 3D molecular generation jointly models molecular size, topology, and geometry. Most methods pre-sample molecular size and generate Cartesian c

Triangular Resampling for Long-Horizon Motion Generation

arXiv:2609.34697v1 Announce Type: new Abstract: We introduce Triangular Resampling (TR), a post-training method for mitigating long-horizon error accumulation in motion diffusion models. Built on Floo

TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception

arXiv:2609.32013v1 Announce Type: new Abstract: We present TriO, a multi-modal unsupervised world model that predicts 4D occupancy, obstacle segmentation, flow and LiDAR. In contrast to prior work, Tr

TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining

arXiv:2609.33419v1 Announce Type: new Abstract: Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective,

Tunable Leg Stiffness in a Monopedal Hopper for Energy-Efficient Vertical Hopping Across Varying Ground Profiles

arXiv:2508.02873v3 Announce Type: replace Abstract: We present the design and implementation of HASTA (Hopper with Adjustable Stiffness for Terrain Adaptation), a vertical hopping robot with real-time

researcharxiv-cs-ro

TwinS-GCN: Spectral conjugate for Spectral Graph Convolutional Networks

arXiv:2609.32940v1 Announce Type: new Abstract: Graph convolutional networks propagate information by repeated local aggregation through a graph shift operator; i.e., a K-layer network reaches K hops

Two-Sample Testing for Inhomogeneous Random Graphs in Non-Integral L_r Norms

arXiv:2609.33968v1 Announce Type: cross Abstract: Testing whether two populations of networks share the same edge probabilities is a basic problem in network inference. How hard it is depends on the n

Two-Stage Multi-View Gait Recognition with a Re-Embedding Network

arXiv:2609.32244v1 Announce Type: new Abstract: Gait recognition always remains challenging due to severe overfitting and the rigid view constraints common in single-stage approaches. We propose a two

Type-Balanced Federated Learning for Visual Analog Meter Reading

arXiv:2609.31998v1 Announce Type: new Abstract: Analog dial meters are widely deployed in industrial applications and utility sites, where environments and meter types vary and inspection data may be

Uncertainty-Aware Selection of Online Algorithms with Simulator Ensembles

arXiv:2609.32170v1 Announce Type: new Abstract: The performance of online reinforcement learning depends critically on design choices, especially those that affect exploration. These choices are often

Understanding Quantization of Optimizer States in LLM Pre-training: Dynamics of State Staleness and Effectiveness of State Resets

arXiv:2603.16731v2 Announce Type: replace Abstract: Quantizing optimizer states is becoming an important ingredient of memory-efficient large-scale pre-training, but the resulting optimizer dynamics r

Unified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation

arXiv:2609.34182v1 Announce Type: new Abstract: Dexterous manipulation requires tactile feedback.However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleoperation provi

researcharxiv-cs-ro

Unifying Distributional Training for One-Step Visual Generation

arXiv:2609.35763v1 Announce Type: new Abstract: Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation

Universal Approximation of Measure-to-Measure Operators by Pushforwards

arXiv:2609.35483v1 Announce Type: new Abstract: Many learning tasks map an input distribution to an output distribution. A natural way to model such an operator is to transform each input sample using

Universality and Generalization of Causal Transformers Across Context Lengths

arXiv:2609.35055v1 Announce Type: new Abstract: Long contexts are central to modern transformer systems, but most expressivity results choose a different network for each fixed sequence length. We stu

Unlocking Geodesic Gromov-Wasserstein Distances for 3D Modeling

arXiv:2609.32824v1 Announce Type: new Abstract: extit{Gromov-Wasserstein Distances} (GWDs) provide quantitative ways of comparing probabilistic distributions defined on different metric spaces by appl

Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation

arXiv:2602.05548v4 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), particularly GRPO, has become the standard for eliciting LLM reasoning. However, its efficien

Using Machine Learning to Investigate Predictors of Fasting Blood Glucose: Insights into Circadian Timing and Age Interactions

arXiv:2609.32386v1 Announce Type: new Abstract: Impaired glucose regulation is a major contributor to metabolic dysfunction and type 2 diabetes. This study developed an interpretable machine-learning

VC Dimension and Expressivity of Real-Valued Transformers

arXiv:2609.31999v1 Announce Type: new Abstract: Whereas previous results on abilities and limitations of transformers have restricted the definition of transformers in various ways, here we study soft

Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts

arXiv:2609.35641v1 Announce Type: cross Abstract: Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part

Video, Ergo Genero: Unifying Video Tasks via Spatiotemporal Analogy

arXiv:2609.33935v1 Announce Type: new Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative

Video-to-Music Generation for Gameplay Videos

arXiv:2609.31810v1 Announce Type: cross Abstract: Video-to-music models have advanced considerably in the last few years, particularly in film and music video applications. In this paper, we investiga