Search: “research”

SubRot: Signed Gradient Subspace Calibration for VLM Rotation Quantization

arXiv:2609.34884v1 Announce Type: new Abstract: Post-training quantization reduces the deployment cost of vision-language models (VLMs), but preserving multimodal capabilities at low bit widths remain

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Suitable Measures for the Potential Operational Utility of AI NWP Rainfall Forecasts Over Africa

DGX agent

arXiv:2609.31775v1 Announce Type: cross Abstract: Artificial intelligence (AI)-based weather prediction is approaching the skill of physical numerical weather prediction (NWP) systems at a fraction of

researcharxiv-cs-lg

29 Sept 2026

Summarize Before Grounding: Query-Guided Chunk Condensation for Long-Video Temporal Grounding

arXiv:2609.34598v1 Announce Type: new Abstract: Video temporal grounding (VTG) aims to localize the video interval corresponding to a language query. Recent large vision-language models (LVLMs) show g

Superposed Inference for Hyperdimensional Computing

arXiv:2609.32320v1 Announce Type: new Abstract: Hyperdimensional computing (HDC) is attractive for efficient and robust learning, but conventional inference still encodes every query independently, re

SurgGMF: Fully Causal Gaussian Motion Forecasting for Anticipatory Surgical Scene Rendering

arXiv:2609.34733v1 Announce Type: new Abstract: Dynamic surgical scene modeling is essential for robotic perception, simulation, and decision support. Although existing neural rendering methods enable

Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting

arXiv:2602.15040v2 Announce Type: replace-cross Abstract: Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remain

T-SNN: Temporal Simplicial Neural Network for EEG Decoding

arXiv:2609.34002v1 Announce Type: new Abstract: Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG me

TACO: Training-free Sound Prompted Segmentation via Semantically Constrained Audio-visual CO-factorization

arXiv:2412.01488v4 Announce Type: replace-cross Abstract: Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of

Tags for DAGs: Graph Refinement with Meta-Informed Relations

arXiv:2506.19459v2 Announce Type: replace Abstract: Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models

Taming the Greeks: Option Portfolios with Inductive Biases

arXiv:2609.33767v1 Announce Type: cross Abstract: We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of port

TANGO: Watermarking Masked Diffusion Language Models in Token Pairs

arXiv:2609.35224v1 Announce Type: new Abstract: Masked-diffusion language models fill in masked positions in parallel and in no fixed order. Most practical text watermarks assume left-to-right generat

Task-Aware Discretization of Differentiable Logic Gate Networks

arXiv:2609.33747v1 Announce Type: new Abstract: Differentiable logic gate networks (DLGNs) enable gradient-based training of highly efficient Boolean networks by relaxing discrete logic gates during t

TC-ADA: One-Shot Active Domain Adaptation for Semantic Segmentation

arXiv:2609.33432v1 Announce Type: new Abstract: Manual dense annotation remains a major obstacle to deploying semantic segmentation models in new driving environments. Active domain adaptation (ADA) s

Temporal-Attention Head Specialization During Video Diffusion Training

arXiv:2609.31654v1 Announce Type: new Abstract: Video diffusion transformers depend on temporal attention to coordinate information across frames, yet nearly everything known about this mechanism come

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

The Composition Gap in Dataset Distillation

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

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