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MVDG: Efficient Multi-view 3D Disambiguation on Unconstrained Real-World Images

arXiv:2610.01098v1 Announce Type: new Abstract: Illusory matches between distinct yet visually similar 3D surfaces--doppelgangers--remain a fundamental obstacle for large-scale, in-the-wild 3D reconst

local-aiarxiv-cs-cv

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MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs

DGX agent

arXiv:2610.01434v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences. While existing operation com

researcharxiv-cs-ai

2 Oct 2026

My agent could do everything except get past a login page, so I built it a browser that hands the login to my phone

I wanted my agent to do boring web chores: check an order, star a repo, fill in a form. Every time, it hit a login page and stopped dead. My options were to give it my password and cookies, or sit the

local-air-ollama

NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields

arXiv:2610.00981v1 Announce Type: cross Abstract: We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric repre

applicationsarxiv-cs-cv

Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)

arXiv:2610.01533v1 Announce Type: new Abstract: Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a qua

applicationsarxiv-cs-ai

NetApp hands storage operations to AI agents, but humans still draw the boundaries

Artificial intelligence is turning nearly every enterprise workload into a data workload, and data governance is becoming the test of whether companies can trust autonomous systems with the infrastruc

agentssiliconangle

Network World Models as Environments for Algorithm Design on Complex Systems

arXiv:2610.01048v1 Announce Type: new Abstract: World models, which simulate an environment and predict how it changes under actions, are increasingly used in real-world applications such as robotics.

Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks

arXiv:2610.00841v1 Announce Type: cross Abstract: For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum

Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation

arXiv:2511.15445v3 Announce Type: replace-cross Abstract: Accurately simulating wave propagation is crucial in fields such as acoustics, electromagnetism, and seismic analysis. Traditional numerical m

Neural scaling laws and evolution of learnable activation functions of Kolmogorov-Arnold networks

arXiv:2610.00985v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) represent a compelling alternative to traditional Multi-Layer Perceptron (MLP)-based neural networks. By employing act

NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching

arXiv:2610.00397v1 Announce Type: new Abstract: Identifying which speaker a listener is attending to in a noisy room -- the cocktail-party problem -- is the missing ingredient for next-generation hear

New Architecture from Percepta: Spotlight — separating intelligence from memory, allowing knowledge and skills to grow without changing the model's weights.

https://www.percepta.ai/blog/can-llms-grow-their-own-capabilities https://www.percepta.ai/blog/spotlight-memory 'Our new architecture, Spotlight, replaces attention with a memory that escapes this tra

local-air-localllama

New benchmark on LMs fixing bugs before users run into them

model-releasesr-localllama

Hi! This is a new benchmark that I created together with other researchers at Meta, Stanford, Harvard, UW. Most benchmarks these days seem to test models to just fix a bug that I as a user already enc

New paper from Microsoft on compressing agent context at test time. If your agent gets worse as its interaction history grows, this method i…

New paper from Microsoft on compressing agent context at test time. If your agent gets worse as its interaction history grows, this method is worth a look. FOCUS asks which past interactions the agent

agentsdair-ai--x

New Snake-in-the-Box Records via Snakepit Surgery and Learned Construction

arXiv:2607.15270v3 Announce Type: cross Abstract: The snake-in-the-box problem asks for a longest induced path in the hypercube graph Q_n. We find a length-191 snake in dimension n=9, the lowest dimen

NextMe-800: Anticipating Personal Behavior from Months of Egocentric Video

arXiv:2610.01461v1 Announce Type: new Abstract: We often plan ambitiously yet act habitually and wonder, in retrospect, whether we would have planned differently had we known what we would actually do

No Model Required: Text Entropy Rate Filtering Mitigates Iterative Fine-Tuning Collapse

arXiv:2610.01493v1 Announce Type: cross Abstract: Iterative fine-tuning on synthetic data causes model collapse: output diversity narrows as rare patterns are progressively lost, a signature most visi

No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents

arXiv:2610.00557v1 Announce Type: cross Abstract: Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement lea

safetyarxiv-cs-ai

Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation

arXiv:2610.00678v1 Announce Type: cross Abstract: Histological whole-slide images (WSIs) are central to computational pathology but pose severe computational challenges due to their extremely high res

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

arXiv:2610.01640v1 Announce Type: cross Abstract: Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger

applicationsarxiv-cs-ai

Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents

arXiv:2610.01787v1 Announce Type: new Abstract: Self-improving GUI agents keep the trajectories they produce and return them to the agent, by fine-tuning or by retrieval into the prompt, and studies t

Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders

arXiv:2610.01270v1 Announce Type: new Abstract: Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head ope

Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning

arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how t

Nous: Learning and Certifying Memory Decisions Before Source Calibration

arXiv:2610.00094v1 Announce Type: cross Abstract: Belief-based agent memory needs reliable decisions about current state, yet its evidence may be noisy, copied, or stale. Must a memory calibrate its s

safetyarxiv-cs-ai

Nvidia announces a version of DGX Spark with 64 GB of unified memory for 4,999, or 1,000 more than the 128 GB version at launch (Michael Kan/PCMag)

Michael Kan / PCMag: Nvidia announces a version of DGX Spark with 64 GB of unified memory for 4,999, or 1,000 more than the 128 GB version at launch — The ongoing memory shortage has prompted Nvidia t

local-aitechmeme

NVIDIA DGX Spark 64GB Gives Developers More Ways to Build and Scale Local AI

Local AI is becoming more useful by the token. As AI agents move from experiments into everyday development, increasingly capable open models are shrinking to fit on more devices, giving builders more

local-ainvidia-blog

ODDR: One-Step Deshadow Diffusion via Reward Guidance

arXiv:2610.01291v1 Announce Type: new Abstract: Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world p

applicationsarxiv-cs-cv

Ollama does not remember datasets

I am trying to import two large datasets to enable statistical analysis. After importing the second dataset, Ollama tells me it has no record of that dataset. Intel i9-11900KF Nvidia 3080Ti 12GB Ubunt

local-air-ollama

Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation

model-releasesarxiv-cs-cv

arXiv:2610.02148v1 Announce Type: new Abstract: Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-bi

OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions via System One

arXiv:2610.00381v1 Announce Type: new Abstract: Medical models are judged not only on correctness, but on whether reported confidence matches actual accuracy. Generalist multimodal medical models have

OmniSeek: Native Tool Integration for Multi-turn Audio-Visual Reasoning

arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with native

safetyarxiv-cs-cv

On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidence

arXiv:2610.00007v1 Announce Type: cross Abstract: Named-entity recognition (NER) is increasingly wanted on-device (no API, low latency, data kept local). The practitioner's question is not the leaderb

On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection

arXiv:2610.00649v1 Announce Type: cross Abstract: Synthetic speech detection is critical for audio security, but performance can degrade when labeled data are scarce and evaluation conditions differ f

safetyarxiv-cs-lg

On Language Drift during RLVR Post-Training

arXiv:2610.02015v1 Announce Type: cross Abstract: Recent advances in LLM reasoning models---driven primarily by the paradigm of post-training via reinforcement learning with verifiable reward (RLVR)--

researcharxiv-cs-ai

On the Divergence of Accuracy and Mechanism Consistency in Time Series World Models

arXiv:2610.01842v1 Announce Type: new Abstract: A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approa

On the Escaping Efficiency of Distributed Adversarial Training Algorithms

arXiv:2509.11337v2 Announce Type: replace Abstract: Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper f

agentsarxiv-cs-lg

On-the-fly Weight Generation: A Hypernetwork Proof of Concept on ARC-1D

arXiv:2610.00820v1 Announce Type: cross Abstract: General-purpose models can adapt to many tasks from context, while specialised models can execute individual functions with less capacity. Yet obtaini

researcharxiv-cs-ai

Tutorials

On the Relationship between Model Quantization and Model Inversion Attacks

arXiv:2610.00382v1 Announce Type: cross Abstract: Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs. Model inversion

tutorialsarxiv-cs-lg

One Basis to Animate Them All: Gaussian Blendshape Distillation for Real-Time Avatars

arXiv:2610.02207v1 Announce Type: cross Abstract: 3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bot

local-aiarxiv-cs-ai

One Mastery Threshold Does Not Fit All Knowledge Tracing Models

arXiv:2610.00095v1 Announce Type: new Abstract: Tutoring systems use mastery thresholds to decide when students can stop practicing and advance, but the same numerical threshold can lead to very diffe

one of the most challenging tasks for frontier models is being able to extract thousands of values from extremely dense tables in documents.…

model-releasesjerry-liu--x

one of the most challenging tasks for frontier models is being able to extract thousands of values from extremely dense tables in documents. our new Extract v2.5 agents are able to get 93%-96%+ on lon

One pool, many targets: a conservation layer and what archival data can identify

arXiv:2610.00445v1 Announce Type: new Abstract: Pairwise guide--transcript scores do not enforce conservation of a finite guide-loaded RISC pool when they are interpreted independently as occupancies.

One-Step Generative Modeling via Training Dynamics Action

arXiv:2610.00518v1 Announce Type: new Abstract: One-step generative models construct a static generator through iterative training-time transport. Existing transport objectives primarily assess distri

Oneira: From Open-Ended Generation to Open-World Interaction in Video World Models

arXiv:2610.01614v1 Announce Type: new Abstract: Generative video world models can now synthesize open-ended environments that agents can navigate and interact with in simple ways. Yet open-ended gener

agentsarxiv-cs-cv

OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video Interaction

arXiv:2610.01762v1 Announce Type: new Abstract: Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The chal

local-aiarxiv-cs-cv

Online Generalized-Mean Welfare Maximization: Achieving Near-Optimal Regret from Samples

arXiv:2602.10469v2 Announce Type: replace-cross Abstract: We study online fair allocation of T sequentially arriving items among n agents with heterogeneous preferences, with the objective of maximizi

Online Planning for Sparse Ground Target Search from a High-Altitude UAV under Partial Observability

arXiv:2610.01067v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) searching for ground targets from a high altitude face a unique challenge, particularly when the target is already withi

agentsarxiv-cs-ro

Ontology-Based Contextual AI Evaluations (OB-CAIE) Methodology

arXiv:2610.00529v1 Announce Type: new Abstract: The ontology-based contextual AI evaluation (OB-CAIE) methodology was developed to address a lack of scientific rigor that arises from unclear testing c

researcharxiv-cs-ai