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Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries

arXiv:2601.01842v2 Announce Type: replace Abstract: Dictionary definitions are an essential resource for learning word senses, but manually creating them is costly. We thus study dictionary definition

researcharxiv-cs-cl

Human

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Safety

Towards Mitigating Fabricated Consensus: The Active Provenance Gate for Multi-Agent Debate Synthesis

DGX agent

arXiv:2609.31422v1 Announce Type: cross Abstract: Large language model-based multi-agent debate (MAD) systems are being increasingly used as complex decision pipelines in distributed processes. Howeve

safetyarxiv-cs-ai

28 Sept 2026

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

arXiv:2609.31371v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and d

Towards Understanding Momentum Acceleration in River-Valley Loss Landscape

arXiv:2609.30957v1 Announce Type: new Abstract: The empirical success of pretraining large language models has inspired a deeper investigation into the underlying loss landscapes and the optimization

researcharxiv-cs-lg

Local Ai

Towards Universal Representation-Based Process Control

arXiv:2609.30790v1 Announce Type: new Abstract: Many temporal process learning and monitoring pipelines operate in local windows, making window-level decisions unavoidable in practice. In such setting

local-aiarxiv-cs-lg

Applications

Towards VLA-Dreamer: Refining VLA Behavior Using World Models

arXiv:2609.31313v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs), while showing strong potential for robot control, require massive amounts of high-quality imitation learning dat

applicationsarxiv-cs-ai

Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning

arXiv:2609.31376v1 Announce Type: cross Abstract: Congenital heart disease (CHD) is the most common birth defect, yet a large fraction of cases remain undetected on prenatal ultrasound, in part becaus

researcharxiv-cs-cv

TR-SSQP: A Trust-Region Method for Constrained Stochastic Optimization under Heavy-Tailed Noise

arXiv:2609.30732v1 Announce Type: cross Abstract: We consider stochastic nonlinear optimization problems with deterministic equality constraints. While unconstrained stochastic optimization is well un

TRACE: Temporal Audit and Condition-aware Evaluation of Streaming Video Understanding

model-releasesarxiv-cs-cl

arXiv:2609.30670v1 Announce Type: new Abstract: Streaming video understanding requires models to interpret evidence as it arrives, yet current evaluations often report task scores without specifying w

TRACKGRAPH: Online Open-Vocabulary 3D Scene Graphs via Image-Space Tracking

arXiv:2609.31005v1 Announce Type: new Abstract: Open-vocabulary 3D maps enable robots to reason about previously unknown environments using natural language. However, existing systems typically segmen

hardwarearxiv-cs-cv

TrafficImag: A Benchmark for Counterfactual Roadside Traffic Video Generation

arXiv:2609.30722v1 Announce Type: cross Abstract: Existing roadside traffic datasets support perception, forecasting, and visual question answering, but they do not evaluate counterfactual video gener

Trained locally: ultra-fast 0.8B/2B System 1 decision models that match Jev on benchmarks and Doom, ~30 ms per decision (open weights)

model-releasesr-localllama

TL;DR: The Jeff models are a set of Qwen3.5 and Gemma fine-tunes for zero-shot classification: small, efficient, open-weight models with respectable out-of-the-box performance that can be slotted righ

Training-Free Bottleneck Width Planning for Convolutional Autoencoders

model-releasesarxiv-cs-cv

arXiv:2609.30755v1 Announce Type: new Abstract: Multiscale Spectral Rate-Distortion (MS-SRD) estimates the bottleneck channels required at user-supplied spatial cuts from training images and a normali

Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring

arXiv:2609.30924v1 Announce Type: new Abstract: Accurate and efficient pronunciation transcription is essential for preparing text-to-speech training data at scale. Existing approaches have different

researcharxiv-cs-cl

Training Graph Foundation Models on The Web Graph

arXiv:2609.30894v1 Announce Type: new Abstract: We introduce Acacia, a graph foundation model, trained on the web graph. Acacia (i) supports arbitrary feature dimensionalities and semantics without ad

researcharxiv-cs-ai

Local Ai

Transformer-based Monte Carlo Localization in Construction Meshes

arXiv:2609.31357v1 Announce Type: new Abstract: To be able to perform inspection or digitization tasks, mobile robots on construction sites must be able to localize themselves reliably with respect to

local-aiarxiv-cs-ro

Trust Guided Decision Transformer

arXiv:2609.31586v1 Announce Type: new Abstract: Decision Transformer performance degrades on long rollouts because the conditioning context drifts out of the training distribution. We show that this d

researcharxiv-cs-lg

Two Conformal Constructions for Adaptive Within-Document AI-Text Screening

arXiv:2609.31547v1 Announce Type: cross Abstract: We study false-alert control when screening for text generated by artificial intelligence (AI). The screening procedure selects document prefixes and

safetyarxiv-cs-cl

UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound

arXiv:2609.30928v1 Announce Type: cross Abstract: Ultrasound is one of the most widely used medical imaging modalities, and recent large vision-language models(VLMs) have shown increasing capabilities

Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach

arXiv:2609.31570v1 Announce Type: cross Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captu

Uncertainty-Aware Federated Learning for Infant Movement Analysis

arXiv:2609.31463v1 Announce Type: cross Abstract: Infant movement analysis provides valuable biomarkers for the early identification of neurodevelopmental disorders. Recent advances in deep learning h

researcharxiv-cs-ai

Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

arXiv:2609.30420v1 Announce Type: cross Abstract: Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multiva

Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment

model-releasesarxiv-cs-cl

arXiv:2609.30802v1 Announce Type: new Abstract: Prior research has demonstrated that the choice of prompt template during Supervised Fine-Tuning (SFT) significantly impacts the robustness of safety al

UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning

arXiv:2609.31298v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder for which early and accurate diagnosis is critical to improving long-term deve

safetyarxiv-cs-ai

UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification

arXiv:2605.06221v2 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer conte

researcharxiv-cs-cl

Universal Drift Correction for Multidimensional Scanning Microscopy

arXiv:2609.30866v1 Announce Type: cross Abstract: In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the

hardwarearxiv-cs-cv

Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year

arXiv:2609.31141v1 Announce Type: cross Abstract: Knowledge distillation is the standard way to compress encrypted-traffic classifiers for the edge, and almost all such work judges students by accurac

Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques

arXiv:2507.08375v2 Announce Type: replace Abstract: Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the perfor

researcharxiv-cs-cv

Local Ai

Up and Down the Abstraction Ladder: Code-Based Skills for Language Agents

arXiv:2609.31076v1 Announce Type: new Abstract: Language agents struggle to act and learn in environments that require long sequences of low-level actions. Code-based abstractions can make these agent

local-aiarxiv-cs-ai

UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting

arXiv:2609.31491v1 Announce Type: new Abstract: Forecasting short-horizon mid-price movements from limit order book (LOB) data is central to algorithmic trading, yet most deep LOB forecasters are poin

researcharxiv-cs-ai

User Model Extraction via Belief Self-Distillation

arXiv:2609.31603v1 Announce Type: cross Abstract: Large language models (LLMs) implicitly infer attributes of their users and adapt their behavior accordingly, yet these beliefs remain difficult to in

safetyarxiv-cs-cl

VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation

model-releasesarxiv-cs-cv

arXiv:2503.10685v3 Announce Type: replace Abstract: Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited da

Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding

arXiv:2609.31452v1 Announce Type: cross Abstract: Cloth manipulation is a challenging task due to the deformable and high-dimensional nature of cloth, which leads to complex interaction dynamics and p

researcharxiv-cs-cv

ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMs

arXiv:2609.31448v1 Announce Type: cross Abstract: Clinical prediction models estimate risk from patient measurements, while large language models support medical text understanding and question answer

VisTacAlign: Co-Training Dexterous Policies on Tactile Human and Robot Demonstrations

arXiv:2609.30959v1 Announce Type: new Abstract: Human demonstrations are a cheap source of data for dexterous manipulation, but co-training a robot policy on them requires closing the human--robot gap

safetyarxiv-cs-ro

VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan

arXiv:2609.30459v1 Announce Type: cross Abstract: Perception in robotics and XR fundamentally relies on good state estimation. Visual-inertial odometry (VIO) and Simultaneous Localization and Mapping

hardwarearxiv-cs-cv

VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control

arXiv:2609.30709v1 Announce Type: cross Abstract: Traffic signal control (TSC) is essential for mitigating urban congestion. Recent advances in vision-language models (VLMs) enable richer interpretati

VLaRL: Augmenting Vision-Language-Action Models with Simulation-Trained Latent-Conditioned Residual RL

arXiv:2609.30868v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide broad, instruction-conditioned manipulation behaviors, but their physical execution can remain imprecise dur

safetyarxiv-cs-ro

Industry

Voice AI startup Modulate raises $25M to bring audio-native models to more developers

Voice artificial intelligence startup Modulate Inc. today announced it has raised 25 million in new funding to get its audio-native models in front of more developers. Modulate offers AI models that w

industrysiliconangle

WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving

arXiv:2609.30436v1 Announce Type: cross Abstract: Driving world models learn rich predictive representations of the surrounding environment from visual observations, yet accurate visual prediction doe

safetyarxiv-cs-cv

Agents

Warned alike, AI agents avoid the less-crowded road while people take it

arXiv:2609.30883v1 Announce Type: cross Abstract: AI agents built on a few shared models increasingly act for many people. A shared forecast about others can align their choices and change how scarce

agentsarxiv-cs-ai

Watch me post-train AliceAI-Foundation-80B-A3B from base to instruct at home, live, on my V100s!

model-releasesr-localllama

No click bait baby I promise - I'm live streaming the training process kinda like MiMo. Original thread: https://www.reddit.com/r/LocalLLaMA/comments/1wpg4a8/im_trying_to_posttrain/ If you didn't see

We are proud to be launch partners for NVIDIA Open Agent Safety Platform. We believe safe and responsible AI development is critical as the …

We are proud to be launch partners for NVIDIA Open Agent Safety Platform. We believe safe and responsible AI development is critical as the use of agents scale and they get more prevalent. Together AI

safetytogether-ai--x

Agents

We built state-of-the-art models for reading forms 📋 Form documents have the following properties that trip up VLMs: ✅ They carry much more…

We built state-of-the-art models for reading forms 📋 Form documents have the following properties that trip up VLMs: ✅ They carry much more structure than can be represented in standard markdown. You

agentsjerry-liu--x

@WeAreDevs Here's how I define 'Fable class models' - first Claude Fable 5, now Claude Opus 5.5 and GPT-Astra 6 and maybe GPT-5.6 Sol as wel…

model-releasessimon-willison--x

Simon Willison defines “Fable class models” as a lineage of generative AI systems that began with Claude Fable 5, progressed to Claude Opus 5.5, and later included GPT‑Astra 6 (with a possible additio

WeaveAgent: A Two-Stage Tool-Routing Agent for Ultra-High-Resolution Remote Sensing Imagery

model-releasesarxiv-cs-cv

arXiv:2609.31234v1 Announce Type: new Abstract: Problem. Ultra-high-resolution (UHR) remote sensing with vague user intents has two bottlenecks: visual tokens are expensive, and tool calling must be f

Weight Pair Encoding: Inducing a Smaller Grammar in Neural Network Weights

arXiv:2609.31564v1 Announce Type: new Abstract: We show that neural network weights can be explicilty fintuned to admit a smaller grammar. Weight Pair Encoding (WeightPE) does so by placing a lossy Re

researcharxiv-cs-lg

We’re partnering with Nvidia and 100+ industry partners to build infrastructure that contains rogue AI agents. In this research, we gave 9 A…

We’re partnering with Nvidia and 100+ industry partners to build infrastructure that contains rogue AI agents. In this research, we gave 9 AI models root access inside SPACE and told them to break out

hardwareperplexity--x