Search: “applications”

Residual Diffusion Implicit Models

arXiv:2609.33020v1 Announce Type: new Abstract: Diffusion models achieve state-of-the-art results across multiple tasks. However, in inverse problems, standard initialization from pure Gaussian noise

Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching

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DGX agent

arXiv:2609.35469v1 Announce Type: cross Abstract: Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenizers treat tokenization as a com

applicationsarxiv-cs-lg

29 Sept 2026

RoboSTAR: Next-Scale Autoregressive Sign Language Translation for Humanoid Robots

arXiv:2609.32250v1 Announce Type: new Abstract: Sign-language interpretation in public communication relies on qualified professional interpreters and can be difficult to scale, motivating robotic sig

Rondo: Unsupervised Discovery of Recurring Temporal Structure

arXiv:2609.32500v1 Announce Type: new Abstract: Many real-world time series data exhibit structural properties at multiple scales, from short, recurring units to complex sequences composed of these un

seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequences

arXiv:2609.31801v1 Announce Type: new Abstract: Complex systems such as vehicles, patients, or genomes emit discrete event sequences whose operative question is causal, not predictive: which events ca

SIFT: Enhancing Time Series Foundation Models via Semantic Invariance and Structural Fidelity Fine-Tuning

arXiv:2609.32676v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable zero-shot performance through extensive pre-training on massive time series datasets. Nev

Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization

arXiv:2609.31940v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to expli

Simultaneous Translation between Sign Languages

arXiv:2609.35608v1 Announce Type: new Abstract: Deaf and hard-of-hearing (DHH) signers cannot converse in real time across different sign languages today: existing sign-to-sign translation systems run

SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization

arXiv:2609.33515v1 Announce Type: new Abstract: Many probabilistic predictors must satisfy exact structure in every stochastic realization, yet common hard-constraint approaches form predictions in am

STAR: Adaptive Spatial-Temporal Normalization for Unified Microservice Incident Management

arXiv:2609.31645v1 Announce Type: new Abstract: Automated incident management in large-scale microservice systems relies on learning robust representations from multimodal observability data, includin

Task-Oriented Communications for Edge-Assisted Multi-View Localization

arXiv:2609.35173v1 Announce Type: cross Abstract: Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) often lose satellite positioning in urban canyons, indoor facilities, and jammed o

applicationsarxiv-cs-ro

Test-Time Generalized Category Discovery

arXiv:2609.33937v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) and Generalized Category Discovery (GCD) are traditionally treated as disjoint problems: the former adapts models to domain s

TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

arXiv:2609.32384v1 Announce Type: new Abstract: Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offer

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

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

Using the biggest model for every task can work in a demo. In production, cost, latency, and task-specific quality can tell a different stor…

applicationsfireworks-ai--x

Using the biggest model for every task can work in a demo. In production, cost, latency, and task-specific quality can tell a different story. At ODSC AI West 2026, Rob Ferguson, VP of Technology and

ViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision

arXiv:2609.33603v1 Announce Type: new Abstract: Reliable optical chemical structure recognition (OCSR) is essential for building high-quality chemical data from scientific literature, yet even small r

WB-WAM: Heterogeneous Body-Hand Pre-training for Humanoid Loco-Manipulation

arXiv:2609.34199v1 Announce Type: new Abstract: Humanoid loco-manipulation demands coordinated body and hand behavior, while conventional robot pre-training data provide limited coverage of such whole

applicationsarxiv-cs-ro

What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling

arXiv:2609.34981v1 Announce Type: new Abstract: World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the futur

When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection

arXiv:2609.32898v1 Announce Type: new Abstract: Pretrained tabular foundation models process every dataset at a fixed depth, with inference costs growing with dataset size. To address this, we present

When Should the Count Change? Learning State Maintenance for Causal Video Counting

arXiv:2609.35416v1 Announce Type: new Abstract: Continuous video counting requires distinguishing new observations from new objects or completed events. We introduce StaMina (State Maintenance), which

Safety

ZeroCode: On-demand Error-Correcting Code Construction from the Zero Matrix via Reinforcement Learning

arXiv:2609.34265v1 Announce Type: new Abstract: Error-correcting codes (ECCs) are essential across diverse applications, from wireless communications and storage to quantum computing, yet each applica

safetyarxiv-cs-lg

AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

arXiv:2609.31166v1 Announce Type: cross Abstract: Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platf

ALF: An Active Learning Framework for Scientific Discovery

arXiv:2609.31197v1 Announce Type: new Abstract: Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongs

AppDirect acquires interactive AI avatar firm Soul Machines for advisers and businesses

AppDirect Inc., the cloud-based business commerce platform and marketplace provider, today announced it acquired Soul Machines Ltd., an artificial intelligence human-like avatar experience provider, t

applicationssiliconangle

Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection

arXiv:2609.31157v1 Announce Type: new Abstract: Multi-dimensional time series, inherently tensorial, are common in practice. Despite great progress in time series anomaly detection, most existing meth

ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization

arXiv:2609.31339v1 Announce Type: cross Abstract: Reconstructing environments where parts of the scene change between captured image sets poses a challenge for 3D scene reconstruction. We present Chro

Completed Pairs Hide Capped Failures: A ReVerPi Case Study of Selective Context Projection

arXiv:2609.31381v1 Announce Type: new Abstract: Context projection replaces older tool observations with compact, addressable excerpts, reducing repeated input while potentially adding evidence-retrie

“connect[ing] AI to the internet … safely with current technology is not a realistic goal...”

applicationsgary-marcus--x

“connect[ing] AI to the internet … safely with current technology is not a realistic goal...” Regardless of how well you think OpenAI security did their job, the thing people should take from this is

Cost-Aware Best-LLM Identification using Dueling Feedback

arXiv:2609.30360v1 Announce Type: cross Abstract: Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulat

Local Ai

Distributed Learning as a Service: The Developer's Perspective

arXiv:2609.31061v1 Announce Type: new Abstract: Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model

local-aiarxiv-cs-lg

Do Neural Networks Preserve Case Structure? Case-Based Decomposition, Interpretation, and Decision Consistency

arXiv:2607.11347v2 Announce Type: replace Abstract: Neural networks increasingly inform consequential decisions, making their reliability increasingly important. Yet their internal mechanisms provide

Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models

arXiv:2609.30935v1 Announce Type: cross Abstract: Continual fine-tuning is essential for large language models (LLMs) to dynamically adapt to real-world environments, yet it inevitably suffers from ca

EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting

arXiv:2609.30880v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has

Florida invokes extinction fears in legal bid to halt OpenAI development

Florida has sought a temporary injunction against OpenAI, arguing that large‑language model development without third‑party approved safety guardrails poses an existential threat and preys on vulnerab

applicationsars-technica

Geometric-Photometric Event-based 3D Gaussian Ray Tracing

arXiv:2512.18640v3 Announce Type: replace-cross Abstract: Event cameras offer a high temporal resolution over traditional frame-based cameras, which makes them suitable for motion and structure estima

GraspTwin: Zero-Shot Task-Oriented Grasp Optimization via a Digital Twin

arXiv:2609.30543v1 Announce Type: new Abstract: As robots transition from structured factory settings into homes, they are required to interact with an ever-increasing variety of objects. Many tasks r

applicationsarxiv-cs-ro

HARDEN: Constrained Evolutionary Search for Harder, Answer-Preserving Evaluation Cases

arXiv:2609.30571v1 Announce Type: new Abstract: Language models are often evaluated on curated benchmarks that underrepresent the complexity of enterprise deployments. We introduce HARDEN, a constrain

“I dislike the passive vibe from OpenAI and the other companies that the breakneck pace of progress is simply happening to them, rather than…

applicationsgary-marcus--x

“I dislike the passive vibe from OpenAI and the other companies that the breakneck pace of progress is simply happening to them, rather than an active choice they are deliberately making.” Good essay.

IPO filing: Anthropic's seven co-founders will initially have 50.1% of the total voting power through a new 'Founder LLC' aimed at serving the common good (Reuters)

Reuters: IPO filing: Anthropic's seven co-founders will initially have 50.1% of the total voting power through a new “Founder LLC” aimed at serving the common good — Anthropic has long positioned itse

applicationstechmeme

IPO prospectus: Anthropic reports a net loss of 42B in 2025, with operating losses of 8B+; revenue grew 12x to ~$4.6B, ~25% of which came from two customers (Echo Wang/Reuters)

Echo Wang / Reuters: IPO prospectus: Anthropic reports a net loss of 42B in 2025, with operating losses of 8B+; revenue grew 12x to ~$4.6B, ~25% of which came from two customers — Anthropic is making

applicationstechmeme

Meta hires MongoDB CEO CJ Desai to lead new enterprise AI business

Meta Platforms Inc. is launching a new business unit that will provide artificial intelligence services to enterprises. The Meta Enterprise Platform, as it’s called, will be led by longtime technology

applicationssiliconangle

Motion Style Slider: Endpoint-Supervised Continuous Style Control for Human Motion Diffusion

arXiv:2609.30795v1 Announce Type: new Abstract: Existing human motion diffusion methods provide strong motion generation quality, and recent style transfer models can inject target style cues, but fin

NavGen: Visual Generative Models as a Scalable Data Engine for Embodied 3D Navigation

arXiv:2609.30770v1 Announce Type: cross Abstract: General-purpose robot models increasingly rely on large and diverse datasets. For embodied 3D navigation, however, existing data sources face a fundam

Not bad

Not bad Grok 4.7 xHigh ranks #1 in Artificial Analysis Cyber Index It's a powerful frontier model performing at the highest level in enterprise cyber defense Outperforming Fable 5.1 Max, Opus 5.5, Ast

applicationselon-musk--x

On theCUBE Pod: Agents reshape enterprise systems, and CoreWeave keeps rising

As artificial intelligence matures, the security guardrails — and the computer system itself — are changing. This week, the conversation centered around agents going rogue. Better guardrails are neede

applicationssiliconangle

PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while

Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control

arXiv:2609.31025v1 Announce Type: cross Abstract: Precise, high-speed control remains challenging for robots with complex actuation dynamics. Learning directly on hardware is further constrained by th