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Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation

arXiv:2610.12806v1 Announce Type: cross Abstract: We study byte-level autoregressive language models whose hidden state is a small quantum register, whose transitions are learned token-conditioned qua

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Research

Training User Simulators with Latent Intrinsic Feedback

DGX agent

arXiv:2610.13617v1 Announce Type: new Abstract: User simulators are gaining attention as environments to train LLM assistants that can better cope with the nuances of multi-turn conversations with rea

researcharxiv-cs-cl

12 Oct 2026

Local Ai

Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL

arXiv:2610.13020v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. Yet long-horizon autoregressive rollouts introduc

local-aiarxiv-cs-lg

Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data

arXiv:2610.12747v1 Announce Type: cross Abstract: Ego-trajectory prediction in urban roadway work zones remains an underexplored topic in autonomous-driving research, with limited dedicated data and s

Transformer-based In-Context Score Operator Learning for Diffusion Models

arXiv:2610.13690v1 Announce Type: new Abstract: Diffusion models typically learn the generative dynamics of a single target distribution or a family of distributions indexed by conditioning variables

Tutorials

Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure

arXiv:2610.12544v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor. Their output

tutorialsarxiv-cs-lg

Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents

arXiv:2610.13058v1 Announce Type: new Abstract: Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dea

TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.12828v1 Announce Type: new Abstract: Outcome-supervised group-relative policy optimization (GRPO) assigns the same advantage to every token in an LLM agent trajectory, leaving useful and un

Trusted Data Apps for the Agentic Data Cloud, powered by Looker

model-releasesgoogle-cloud-ai

As a business intelligence user, this probably sounds familiar: Most dashboards only provide you with a standardized canvas and a fixed library of charts, and the “analysis” consists of a CSV export t

TTS-Q: Perception-Guided Post-Training Quantization of Text-to-Speech Diffusion Transformers

arXiv:2610.12474v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) can clone a voice from seconds of audio. Yet the quality comes from billion-parameter diffusion transformers whose size

Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes

arXiv:2610.12850v1 Announce Type: cross Abstract: Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows. We consider learning

U-Space: Uncovering When and Why Uncertainty Arises in Language Models [R][D]

model-releasesr-machinelearning

If you build on open-weight reasoning models, you have probably wanted a 'how sure is the model about this answer?' signal that does not cost 5x sampling, a judge model, or a labeled dataset. Hence, w

U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction

model-releasesarxiv-cs-cl

arXiv:2610.12742v1 Announce Type: new Abstract: This paper details our submission to KnowledgeGraphEval 2026 Subtask 2 on directed Arabic relation extraction. We explored encoder-based and large langu

UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes

arXiv:2610.13783v1 Announce Type: new Abstract: Memes play a central role in modern online communication, especially in contexts of political conflict where visual and textual cues combine to convey p

Understanding Mamba-3 Through Oscillatory Dynamics

arXiv:2610.13341v1 Announce Type: new Abstract: Mamba-3 introduces input-dependent rotations into recurrent state updates, but their contribution beyond state retention remains unclear. We interpret t

Understanding Transformer-based Vision Models through Inversion

arXiv:2412.06534v5 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promisi

researcharxiv-cs-ai

UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning

arXiv:2506.02955v3 Announce Type: replace-cross Abstract: Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation

Universal Bounds for Self-Normalized Kullback-Leibler Estimation

arXiv:2610.12851v1 Announce Type: cross Abstract: This work studies an entropy-based estimator of Kullback-Leibler (KL) divergence constructed from normalized importance weights. We prove a universal

safetyarxiv-cs-lg

Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News

model-releasesarxiv-cs-cl

arXiv:2610.12863v1 Announce Type: new Abstract: Satirical news is a unique form of misinformation: it is intended to be humorous by the original publication source but can be misinterpreted. Explanati

Unsupervised Multimodal Reward Modeling

arXiv:2610.12707v1 Announce Type: cross Abstract: Vision-language reward models are trained on annotated preference pairs, which are expensive to collect, can disagree, and are ranked by benchmark acc

Local Ai

User Trajectory Prediction Unifying Global and Local Temporal Information

arXiv:2508.02161v2 Announce Type: replace Abstract: Trajectory prediction is essential for formulating proactive strategies that anticipate user mobility and support advance preparation. Therefore, ho

local-aiarxiv-cs-lg

v0.40.4

model-releasesollama-releases

What's Changed llama: update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917 server: reuse verified manifests and loaded models in inference routes by @dhiltgen in

Variance-Reduced Policy Gradient is Nearly Minimax-Optimal

arXiv:2610.13692v1 Announce Type: cross Abstract: Standard Policy Gradient (PG) algorithms suffer from high estimation variance because they rely on Monte Carlo updates. While off-policy variance redu

VectorDash: A full Windows search replacement incorporating Embeddinggemma-2

model-releasesr-localllama

Inspired by DigUp for macOS, VectorDash aims to be a full Windows 11 search replacement, powered by EmbeddingGemma 2. Full indexing: search by meaning or exact words across photos, screenshots, audio,

VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails

arXiv:2610.12813v1 Announce Type: new Abstract: LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can create serious safety and secur

Applications

Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph

arXiv:2610.13695v1 Announce Type: new Abstract: Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable. Despite their advantages, current LLM-

applicationsarxiv-cs-cl

Verifiable Sampling for Delegated Generative Quantum Machine Learning

arXiv:2610.13211v1 Announce Type: cross Abstract: Generative machine learning has shown remarkable results across a wide range of interdisciplinary tasks, motivating significant efforts toward Quantum

Verifier-Mediated Multi-LLM Coordination for Multi-Robot Path Planning

arXiv:2610.12761v1 Announce Type: new Abstract: Foundation models are increasingly used for high-level multi-robot task planning, yet independently generated symbolic plans can become jointly infeasib

researcharxiv-cs-ro

VETR: Execution-Grounded Self-Improving Agent for Symbolic Graphics Reasoning

arXiv:2610.13098v1 Announce Type: new Abstract: Visual agents for symbolic graphics reasoning depend on executable world representations, yet continued improvement remains bottlenecked by ambiguous su

Tutorials

VICAR: Video Imitation with Contact-Aware Retargeting for Humanoid Interactive Skills

arXiv:2610.13079v1 Announce Type: new Abstract: Third-person human videos offer a scalable alternative to task-specific reward engineering, teleoperation, and manually scripted demonstrations for huma

tutorialsarxiv-cs-ro

VIGOR: Verifiable Industrial Geometry Optimization and Repair in Scene Programs

arXiv:2610.13145v1 Announce Type: new Abstract: Large language models (LLMs) increasingly generate structured layouts and executable scene programs from natural-language instructions. However, executa

researcharxiv-cs-ai

Applications

Vision for engineering, manufacturing, and earth observation. Checks mechanical parts, pulls evidence from PDFs, scans gigapixel satellite i…

applicationsmistral-ai--x

Mistral AI outlines its vision for engineering, manufacturing, and earth observation by demonstrating how the model can inspect mechanical components, extract evidence from PDFs, and analyze gigapixel

Vision Transformers Learn Word Meaning from Images Alone

arXiv:2610.13252v1 Announce Type: cross Abstract: Can a vision model learn what words mean from pixels alone? We show that the two largest DINOv3 vision transformers organize words rendered in images

safetyarxiv-cs-cl

Visual-Wi-Fi Navigation to Signal-Emitting Devices

arXiv:2610.12733v1 Announce Type: new Abstract: Indoor navigation to task-relevant targets remains difficult when the target is small, occluded, or visually indistinct. Meanwhile, smart appliances, mo

safetyarxiv-cs-ro

VLMs are Native Action Models: Unleashing the Manipulation Capabilities of VLMs with Explicit Action-Space Grounding

arXiv:2610.12832v1 Announce Type: new Abstract: Pretrained vision-language models (VLMs) already exhibit strong visual, spatial and reasoning capabilities relevant to robotic manipulation. The key cha

safetyarxiv-cs-ro

Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training

arXiv:2610.13608v1 Announce Type: cross Abstract: Vocal intensity conversion renders a recorded utterance at a different vocal intensity, rather than simply scaling its amplitude, while preserving its

VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs

arXiv:2610.13689v1 Announce Type: new Abstract: Learning how local perturbations affect a network is important for identifying which components drive or absorb a system's response. We formulate this a

Agents

Vultr at NetApp INSIGHT 2026: Beyond Hyperscale and Into a New Era of Enterprise Infrastructure

At NetApp INSIGHT 2026, Vultr highlighted the emerging “beyond hyperscale” era of enterprise infrastructure, emphasizing that agentic AI and modernized applications require more than compute and stora

agentsvultr

WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning

arXiv:2610.13186v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without exposing raw data, yet remains vulnerable to malicious clients injecting poisoned d

We need a name for the kind of people that conflate 2022 pure LLMs that were in fact a tiny bit like stochastic parrots (not my phrase, and …

We need a name for the kind of people that conflate 2022 pure LLMs that were in fact a tiny bit like stochastic parrots (not my phrase, and not one I ever endorsed) with 2026 systems that couple those

safetygary-marcus--x

Agents

web-edit 0.1a0

Release: web-edit 0.1a0 I was working on a project from my phone using Codex Remote when I found myself wanting to edit a credentials file without round-tripping those credentials through the LLM itse

agentssimon-willison

Agents

Web$hell: A Unified Interface for Agentic Web Access

arXiv:2610.13416v1 Announce Type: new Abstract: Large language model (LLM) agents are becoming direct users of the open web. As they take on increasingly complex and information-intensive tasks, effec

agentsarxiv-cs-ai

Weight Clustering Reveals the Importance of Relative Rank in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2603.17917v2 Announce Type: replace-cross Abstract: Large language models (LLMs) contain billions of parameters, but many exact weight values are not essential. We apply scalar K-means clusterin

What are you doing for a reliable, well designed local model based AI search?

model-releasesr-localllama

Ive been using OpenWebUI's inbuilt web search and I can't put my finger on it, but it's not quite as polished, reliable, seamless as using Claude or Gemini. And its often slow beyond a limit that make

What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings

arXiv:2610.13247v1 Announce Type: cross Abstract: Open audio-language models are used as judges of generated music: given a clip and its text prompt, the model rates the clip, and the judge is validat

safetyarxiv-cs-cl

Applications

What is the strongest mechanistic argument that next token prediction plus reinforcement learning can produce open-ended general intelligence? [D]

applicationsr-machinelearning

Most conversations I see on various AI subreddits start from the assumption that AGI/RSI/ASI is possible, we're on the way right now, and that things will be incredibly different afterward. The only q

What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents

model-releasesarxiv-cs-ro

arXiv:2610.12561v1 Announce Type: new Abstract: Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests. For embodied agent

What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training

arXiv:2610.12995v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introdu