All entries

SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video

model-releasesarxiv-cs-cv

arXiv:2609.37969v1 Announce Type: new Abstract: High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens. A practical alternative first generat

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Agents

Solver Agent: an Agentic AI Framework for Theoretical Physics Computations Applied to F-theory Uplifts of O3-planes and S-folds

DGX agent

arXiv:2609.35958v1 Announce Type: cross Abstract: We introduce Solver Agent, an AI framework based on large language models for calculations and proofs in mathematics and theoretical physics. The solu

agentsarxiv-cs-ai

30 Sept 2026

Solving Robust POMDPs with Omega-regular Objectives via Partially Observable Stochastic Games

arXiv:2608.24986v2 Announce Type: replace Abstract: Robust POMDPs (RPOMDPs) generalize classical POMDPs to the setting where exact transition probabilities are not known -- rather, they are only known

Solving Without Stopping: On-Policy Distillation at Small Scale

arXiv:2609.37326v1 Announce Type: new Abstract: On-policy distillation, where a student learns from a stronger teacher's feedback on its own outputs, is a common way to pass reasoning to smaller model

Sources: Google is paying ~100 digital publishers for how much their content contributes to AI Overviews, AI Mode, and Gemini in a pilot; payments vary widely (The Information)

The Information: Sources: Google is paying ~100 digital publishers for how much their content contributes to AI Overviews, AI Mode, and Gemini in a pilot; payments vary widely — Google has been paying

model-releasestechmeme

Hardware

Sources: Jensen Huang and other execs asked Dario Amodei at the White House why he was so extreme in public on AI risks; Amodei said it's important to be honest (Wall Street Journal)

Wall Street Journal: Sources: Jensen Huang and other execs asked Dario Amodei at the White House why he was so extreme in public on AI risks; Amodei said it's important to be honest — Nvidia's CEO was

hardwaretechmeme

Sources: some Google employees say Gemini 4 performs well on benchmarks but struggles with some real-world coding tasks; Google disputes that characterization (Bloomberg)

Bloomberg: Sources: some Google employees say Gemini 4 performs well on benchmarks but struggles with some real-world coding tasks; Google disputes that characterization — As Alphabet Inc.'s Google pr

model-releasestechmeme

Industry

Sources: the US FTC is expanding a sweeping probe of Anthropic, OpenAI, and other frontier AI labs, and plans to issue formal demands to turn over information (New York Post)

New York Post: Sources: the US FTC is expanding a sweeping probe of Anthropic, OpenAI, and other frontier AI labs, and plans to issue formal demands to turn over information — WASHINGTON — The Federal

industrytechmeme

Agents

SpaceXAI just literally shared its engineering team’s Grok Bots with everyone And Grok Bots just got much more powerful for building softwar…

SpaceXAI just literally shared its engineering team’s Grok Bots with everyone And Grok Bots just got much more powerful for building software Bots can now hand off coding tasks directly to Cursor, man

agentselon-musk--x

Spanner Omni, now GA: A distributed, multi-model database that you can deploy anywhere

model-releasesgoogle-cloud-ai

Spanner Omni, the deploy-anywhere version of Spanner, is now generally available, ready to power your most demanding production workloads in your on-premises data center or on other clouds. With Spann

Sparse cubical complexes for efficient topology-preservation in image data

arXiv:2609.37177v1 Announce Type: new Abstract: Persistent homology (PH) is a frequently used tool for extracting and preserving topological information from image data, particularly in image segmenta

Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding

arXiv:2609.36217v1 Announce Type: new Abstract: A deeper understanding of brain function requires a precise, structured characterization of behavior.Yet, extracting behavioral representations from vid

safetyarxiv-cs-cv

Spatial-OPSD: Self-Improving Spatial Reasoning via Label-Free Self-Distillation

model-releasesarxiv-cs-cv

arXiv:2609.37055v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly operate in embodied and spatially grounded settings, where accurate understanding of depth, viewpoint, and th

Local Ai

Spatiotemporal Hyperedges for EEG Seizure Detection and Prediction

arXiv:2609.37730v1 Announce Type: new Abstract: Seizure detection and prediction from EEG are clinically important but challenging because seizures are rare, temporally localized, and propagate as coo

local-aiarxiv-cs-ai

Agents

Speed in the Blind Spot: An Interpretability Analysis of Dynamic Perception in VLMs for Autonomous Driving

arXiv:2609.37046v1 Announce Type: new Abstract: Vision-Language Models are increasingly used in autonomous-driving systems, yet their ability to recover dynamic physical state from visual input remain

agentsarxiv-cs-cv

SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving

arXiv:2609.37626v1 Announce Type: cross Abstract: No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tensor parallelism, many independen

Spotter: Let the Embodied Model Lead, and the VLM Reflect for It

arXiv:2609.36808v1 Announce Type: cross Abstract: Current embodied models do not respond to their own failures, although what just went wrong could inform a small adjustment on the next attempt, the k

SQUARE: Structured Quantum Representation Adapters as Compact Quadratic Feature Maps for Frozen Language Models

arXiv:2609.37134v1 Announce Type: cross Abstract: Frozen language models (LMs) are increasingly used as fixed feature extractors for downstream reranking, scoring, and preference modeling, raising a p

SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging

arXiv:2609.36982v1 Announce Type: cross Abstract: Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners i

Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks

arXiv:2609.36471v1 Announce Type: cross Abstract: World-Action Models (WAMs) improve robotic manipulation by conditioning action generation on predicted future observations, but future prediction adds

STAR-GRPO: Canonical Anchoring and Reliability-First Advantages against Representation-Dependent Reward Hacking

arXiv:2609.36900v1 Announce Type: new Abstract: Reward hacking occurs when policy optimization exploits a brittle reward interface or an overly permissive proxy objective, improving the training score

Industry

Startup Ascerta collects $18M in funding to help enterprises understand the value AI provides

Many businesses are spending enormous sums of money on artificial intelligence, but they don’t have any way to understand how much value those investments are returning — and that’s a challenge that a

industrysiliconangle

State Trace Rationale As Auxiliary Task in Reinforcement Learning

arXiv:2609.36867v1 Announce Type: new Abstract: We propose STRAT, an auxiliary task that trains deep reinforcement learning (RL) agents to predict a short textual trace of their own state. Inspired by

State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting

arXiv:2609.36926v1 Announce Type: cross Abstract: Recent power measurements provide valuable information for photovoltaic(PV) power forecasting, but directly extrapolating short-term trends can introd

StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents

arXiv:2609.36319v1 Announce Type: new Abstract: Despite the recent success of coding agents built on large language models, it remains challenging to run them over long horizons, since every observati

Stealth Is a Relation, Not a Property: How Event Representations Create Blind Spots for Timing Attacks in Event-Based Perception

arXiv:2609.36386v1 Announce Type: new Abstract: An event camera produces an asynchronous stream, but what is visible in that stream depends on how a downstream consumer, such as a model or detector, p

Hardware

Stealthium Joins the Vultr Cloud Alliance to Bring GPU Observability and Runtime Security to AI Infrastructure

Stealthium has joined the Vultr Cloud Alliance, adding GPU‑level observability and runtime security to Vultr’s AI infrastructure platform. As a member of NVIDIA’s Inception Program, Stealthium supplie

hardwarevultr

Stein-based Optimization of Sampling Distributions in Model Predictive Path Integral Control

arXiv:2511.02015v3 Announce Type: replace Abstract: This paper introduces a method for Model Predictive Path Integral (MPPI) control that optimizes sample generation towards an optimal trajectory thro

researcharxiv-cs-ro

Hardware

STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization

arXiv:2609.38169v1 Announce Type: cross Abstract: Linear attention replaces growing KV caches with fixed-size recurrent states, yet these persistent states can become a substantial memory bottleneck u

hardwarearxiv-cs-ai

Stochastic Heavy Ball with Polyak Step Size and Armijo Line Search: A General Convergence Analysis

model-releasesarxiv-cs-lg

arXiv:2609.36668v1 Announce Type: new Abstract: Polyak step size (PS) and Armijo line search (ALS) have received increasing attention in stochastic optimization, with encouraging empirical performance

Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis

arXiv:2609.36271v1 Announce Type: new Abstract: Recent work has established that power-law spectral conditions on data enable tight convergence bounds for deterministic gradient descent, resolving the

researcharxiv-cs-lg

Stochastic World Models for Verifying Vision-Based Neural Feedback Systems

arXiv:2609.38120v1 Announce Type: new Abstract: Verifying a vision-based neural feedback system requires a model of the observations its controller acts upon. Such a model must capture the variation t

Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning

model-releasesarxiv-cs-cl

arXiv:2609.37858v1 Announce Type: cross Abstract: Many localized large language model (LLM) unlearning methods select a small parameter subset from a localization signal and keep it fixed during optim

Strict-Saddle Landscapes and Multi-Rank Geometry in Low-Tubal-Rank Tensor Sensing

arXiv:2609.37865v1 Announce Type: new Abstract: We study the optimization landscape of low-tubal-rank tensor sensing through a balanced factorization. Under a tubal restricted isometry condition, we e

researcharxiv-cs-lg

STRIDE: Automated Evaluation of Text-to-Trajectory Alignment across Diverse Contexts

arXiv:2609.34799v2 Announce Type: replace Abstract: Language-conditioned trajectory generation is here, but its evaluation has not kept pace. Existing pedestrian trajectory metrics compare trajectorie

StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks

arXiv:2609.36352v1 Announce Type: new Abstract: Vision-language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple

Structured Visual Target Learning For Cross-Subject eeg-to-image retrieval

arXiv:2609.36971v1 Announce Type: new Abstract: Cross-subject EEG-to-image retrieval requires a neural represen- tation trained on source subjects to remain aligned with a visual embedding space for a

Applications

Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability

arXiv:2609.36417v1 Announce Type: new Abstract: Relational in-context learning (ICL) uses labeled support examples and their linked relational context to predict labels for new queries. This creates a

applicationsarxiv-cs-ai

Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Impact, Detection, and Mitigation

arXiv:2609.36384v1 Announce Type: new Abstract: Relational in-context learning (ICL) conditions predictions on the labeled support examples and their linked tables, creating a failure mode when the su

researcharxiv-cs-ai

Suppression Is Not Forgetting: Residual Recoverability in Visual Concept Unlearning for VLMs

arXiv:2604.03114v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy

Surgical Master Console Using General-Purpose Robot Arms and a Separable Articulated Distal Interface: Porcine In-Vivo Evaluation

arXiv:2609.37455v1 Announce Type: new Abstract: High-performance surgical master consoles offer intuitive articulated manipulation but are expensive and difficult to reproduce, while accessible commer

researcharxiv-cs-ro

Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs

arXiv:2509.21305v4 Announce Type: replace Abstract: Large language models (LLMs) often exhibit sycophantic behaviors -- such as excessive agreement with or flattery of the user -- but it is unclear wh

researcharxiv-cs-cl

Tutorials

SYNCR: Diagnosing and Learning Cross-Video Reasoning from Simulation

arXiv:2609.37918v1 Announce Type: new Abstract: Reasoning across videos requires aligning events, matching identities, comparing motion, and integrating partial observations. Evaluating these capabili

tutorialsarxiv-cs-cv

System one models are extremely useful for a variety of document tasks that require fast decisions: * orientation detection * language detec…

model-releasesjerry-liu--x

System one models are extremely useful for a variety of document tasks that require fast decisions: * orientation detection * language detection * classification * splitting We benchmarked Jev with ot

Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method

arXiv:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require te

T^2Mem: Learning Test-Time Memory for Robotics

model-releasesarxiv-cs-ro

arXiv:2609.36720v1 Announce Type: new Abstract: Memory-dependent robotic manipulation requires policies to use information that is no longer available in the current observation. Retaining history alo

TabFM: A Zero-Shot Foundation Model for Tabular Data

model-releasesarxiv-cs-lg

arXiv:2609.37959v1 Announce Type: new Abstract: Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We pr

TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2609.37989v1 Announce Type: new Abstract: Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, tas