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TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection

arXiv:2609.33376v1 Announce Type: cross Abstract: Fraud detection is an important area of research in the insurance business due to its financial implications. The primary aim of a fraud detection mod

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Model Releases

Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning

DGX agent

arXiv:2609.35228v1 Announce Type: new Abstract: Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to a

model-releasesarxiv-cs-cv

29 Sept 2026

TokenCast: Forecasting Token Consumption During LLM Agent Execution

arXiv:2609.35760v1 Announce Type: new Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses

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

Hardware

TopoEP: Topology-Aware Load Balancing for Expert-Parallel MoE Training

arXiv:2609.35481v1 Announce Type: cross Abstract: Dynamic routing creates severe load imbalance in large-scale expert-parallel Mixture-of-Experts (MoE) training, turning GPUs that host hot experts int

hardwarearxiv-cs-lg

Topology-Adaptive Hyperbolic Graph Attention Networks Guided by the Hyperbolic Sombor Index

arXiv:2609.32275v1 Announce Type: new Abstract: Hyperbolic geometry has emerged as a principled space for representing hierarchical graphs. However, existing hyperbolic graph neural networks typically

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 AI-Assisted Poultry Coccidiosis Diagnosis: Evaluating Gemini and BiomedParse on Eimeria Microscopy Images

arXiv:2609.31679v1 Announce Type: new Abstract: Coccidiosis caused by Eimeria parasites is a major economic burden in poultry production, and effective control depends on accurate species-level diagno

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 On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision

arXiv:2609.32405v1 Announce Type: new Abstract: Event cameras provide low-latency, asynchronous visual sensing for resource-constrained robotics. Spiking neural networks (SNNs) process event streams n

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

Agents

Towards Autonomous and Auditable Medical Imaging Model Development

arXiv:2607.10522v2 Announce Type: replace Abstract: Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and

agentsarxiv-cs-cv

Towards Eliminating Catastrophic Forgetting in the Curriculum Learning of Math Reasoning Tasks

arXiv:2609.33655v1 Announce Type: new Abstract: Curriculum learning has found broad application across numerous domains. Nevertheless, its effectiveness is intrinsically curtailed by catastrophic forg

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

Hardware

Towards Kinematic Actionable Infeasibility Detection in Motion Planning

arXiv:2609.32806v1 Announce Type: new Abstract: Motion planning in robotics requires not only computing collision-free paths but also certifying infeasibility when no such path exists. Complete method

hardwarearxiv-cs-ro

Local Ai

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations

arXiv:2407.08983v2 Announce Type: replace-cross Abstract: Trustworthiness and interpretability are inextricably linked concepts for LLMs. The more interpretable an LLM is, the more trustworthy it beco

local-aiarxiv-cs-lg

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

TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care

arXiv:2609.34088v1 Announce Type: new Abstract: TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embe

TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment

arXiv:2607.16242v2 Announce Type: replace Abstract: Fine-Tuning-as-a-Service (FTaaS) platforms let users perform supervised fine-tuning (SFT) on customized data, but this pipeline can erode model safe

Traceable Human-to-Humanoid Sign Language Benchmarking

model-releasesarxiv-cs-ro

arXiv:2609.33354v1 Announce Type: new Abstract: Sign data collection is costly, and teleoperation scales poorly, motivating reuse of large video corpora. Humanoid signing requires converting video-der

Train Together or Merge Later? Unifying VLA Experts via a Shared Action Interface

arXiv:2609.33125v1 Announce Type: new Abstract: Co-training offers a straightforward way to build a multi-task vision-language-action (VLA) policy, but can fall short of the performance achieved by tr

safetyarxiv-cs-cv

Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging

arXiv:2609.32303v1 Announce Type: cross Abstract: Domain experts trained from a shared checkpoint can be merged into one model through on-policy distillation (OPD), where they act as teachers supervis

Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction

arXiv:2609.34130v1 Announce Type: new Abstract: This monograph develops a unified account of training and inference in Power Law Decoder Representation language models (PLDR-LLMs). Exact finite work i

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

Trajectory-Safe Orienteering for Human-Robot Shared Environments

arXiv:2609.34485v1 Announce Type: new Abstract: Orienteering problem (OP) has wide real-world applications and also great potential in human-robot collaboration. However, existing approaches struggle

safetyarxiv-cs-ro

Applications

Transfer Calibrated Prediction Powered Inference

arXiv:2609.34156v1 Announce Type: new Abstract: Prediction-powered inference (PPI) and its power-tuned extension (PPI++) improve confidence intervals by combining a small gold-standard labeled sample

applicationsarxiv-cs-lg

Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs

arXiv:2609.32849v1 Announce Type: new Abstract: Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persis

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

Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression

arXiv:2609.34834v1 Announce Type: new Abstract: Transform-based methods provide an effective framework for point cloud attribute compression by representing attributes as transform coefficients. Intro

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

Transformer MLP Gate Thresholds Are Couplings to a Carried Reference Direction

arXiv:2609.31956v1 Announce Type: cross Abstract: The corpus-mean direction of a transformer's residual stream is a component shared across all inputs, and is commonly removed by mean-centering before

Tutorials

TRAP: Understanding and Mitigating Privacy Memorization in Language Models

arXiv:2609.32293v1 Announce Type: cross Abstract: Fine-tuning a language model on sensitive records can leave it able to reproduce them. We ask when this memorization arises and how to prevent it with

tutorialsarxiv-cs-lg

Local Ai

Trapped by Their Own Rollouts: Understanding Aggregation--Rollout Feedback in Federated On-Policy Distillation

arXiv:2609.32573v1 Announce Type: new Abstract: On-policy distillation (OPD) is a promising approach to language-model adaptation, aligning teacher supervision with the student's own generated traject

local-aiarxiv-cs-lg

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

TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring

arXiv:2609.33000v1 Announce Type: cross Abstract: Predicting how drivers, vehicles, and road scenes interact and evolve together is central to driver monitoring. Prior work models in-cabin activity or

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

Applications

Trustworthy synthetic visual media: Evidence across the media lifecycle

arXiv:2609.34232v1 Announce Type: new Abstract: Images and videos have long helped people understand what happened and how a work came into being. Generative systems complicate that role. Realistic me

applicationsarxiv-cs-cv

TSGate: Timestep-Aware Gated Attention for Diffusion Transformers

arXiv:2609.34539v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-fidelity image and video generation. Recent DiT systems increasingly us

safetyarxiv-cs-cv

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,

TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models

arXiv:2609.33414v1 Announce Type: new Abstract: Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual co

safetyarxiv-cs-cv

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

Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models

arXiv:2609.35609v1 Announce Type: new Abstract: Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constrai

Applications

Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts

arXiv:2609.33257v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to forecast real-world events, but access to the strongest individual forecaster may be costly or oth

applicationsarxiv-cs-lg

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