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arXiv:2610.07298v1 Announce Type: cross Abstract: Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources.

applicationsarxiv-cs-ai

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Safety

POLAR: Ontology-Guided Risk Prevention for Tool-Calling LLM Agents

DGX agent

arXiv:2610.08082v1 Announce Type: new Abstract: LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors m

safetyarxiv-cs-ai

7 Oct 2026

PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation

model-releasesarxiv-cs-cv

arXiv:2610.08346v1 Announce Type: new Abstract: Polarization imaging provides physical cues beyond intensity imaging but typically requires specialized hardware. Recent methods infer polarization from

Porting LIBERO to MuJoCo Warp: 130 robot manipulation tasks on one $700 AMD GPU

model-releasesr-localllama

LIBERO is a robot manipulation benchmark: 130 tasks across five suites (spatial, object, goal, scene10, scene90), each with human demos and a language goal. It is the standard testbed for language-con

Post-Grasp Kinematic Repair for Robotic Insertion via Object-in-Gripper Reorientation

arXiv:2610.08421v1 Announce Type: new Abstract: A stable grasp does not guarantee kinematically feasible robotic insertion because the object-in-gripper transform may force the robot towards singulari

researcharxiv-cs-ro

Post-Training Semantic Lifting for 3D Gaussian Splatting: Separating Detector, Lifting and Representation Error

arXiv:2610.08756v1 Announce Type: new Abstract: The same Gaussian of a 3D Gaussian Splatting model is seen from many views, and these views do not always agree on the class it belongs to. The Gaussian

researcharxiv-cs-cv

Applications

Practical Feasibility of Gradient Inversion Attacks in Federated Learning

arXiv:2508.19819v3 Announce Type: replace-cross Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly stro

applicationsarxiv-cs-ai

Tutorials

Predicting kernel regression learning curves from only raw data statistics

arXiv:2510.14878v3 Announce Type: replace-cross Abstract: We study kernel regression with common rotation-invariant kernels on real datasets including CIFAR-5m, SVHN, and ImageNet. We give a theoretic

tutorialsarxiv-cs-ai

Prediction-powered inference for time series across space

arXiv:2610.08715v1 Announce Type: cross Abstract: The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time

safetyarxiv-cs-lg

Preserving Unstable Modes Through Inverse Dynamics in JEPA World Models

arXiv:2610.07540v1 Announce Type: new Abstract: Robotic systems often exhibit unstable modes, along which small perturbations and disturbances can cause unbounded growth unless corrected through feedb

Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral Judgment

arXiv:2610.08670v1 Announce Type: cross Abstract: Language models increasingly act as agents. An agent that says an action is wrong and then takes it anyway is a different failure from one that does n

Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction

arXiv:2610.06964v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experi

Privileged Context as Drift in On-Policy Self-Distillation

model-releasesarxiv-cs-lg

arXiv:2610.07842v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a language model to match a copy of itself conditioned on privileged context. Existing work varies what privil

Local Ai

ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration

arXiv:2610.06999v1 Announce Type: new Abstract: Rapid adaptation to a new environment requires a robot to acquire useful knowledge about local objects, states, and interactions from limited experience

local-aiarxiv-cs-ro

Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models

arXiv:2610.08689v1 Announce Type: new Abstract: Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiti

Probabilistic Truly Unordered Rule Sets

model-releasesarxiv-cs-lg

arXiv:2401.09918v2 Announce Type: replace Abstract: Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First,

ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection

arXiv:2610.07515v1 Announce Type: new Abstract: Accurately modeling and tracking the deformation of soft tissue is critical for a wide range of interventional and surgical procedures. However, current

agentsarxiv-cs-ro

Hardware

Project Suncatcher is our moonshot exploring whether we can one day host machine learning infrastructure in space 🚀 To do so, we need to kn…

Project Suncatcher is our moonshot exploring whether we can one day host machine learning infrastructure in space 🚀 To do so, we need to know whether our AI hardware can operate in orbit. Can our TPUs

hardwareelon-musk--x

Pronunciation Editing for Finnish Speech using Phonetic Posteriorgrams

arXiv:2507.02115v3 Announce Type: cross Abstract: Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L2

safetyarxiv-cs-cl

Protective Perturbations Must Survive the Resize: Scale-Robust Image Immunization against Malicious Editing

arXiv:2610.07464v1 Announce Type: new Abstract: Protective perturbations aim to stop malicious instruction-guided editing of personal photos, but they are optimized and evaluated at the editor's worki

researcharxiv-cs-cv

ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding

arXiv:2610.08078v1 Announce Type: cross Abstract: Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal i

PRUE: A Practical Recipe for Field Boundary Segmentation at Scale

model-releasesarxiv-cs-cv

arXiv:2603.27101v2 Announce Type: replace Abstract: Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field ma

Pseudowords as probes: Large Language Models show little of the sublexical sensitivity that governs human pseudoword processing

arXiv:2610.07936v1 Announce Type: new Abstract: Systematicity, the probabilistic mapping of form to meaning, permeates language at all levels, and sublexical cues have been shown to govern human pseud

safetyarxiv-cs-cl

PsyCIDRA: A Dual-Agent Framework for Psychiatric Interviewing and Diagnostic Reasoning

arXiv:2610.07473v1 Announce Type: new Abstract: Large language models show promise in clinical reasoning, but psychiatric interviewing requires guiding an evolving conversation. Their ability to carry

safetyarxiv-cs-ai

PuzzleJAX: A Benchmark for Reasoning and Learning

arXiv:2508.16821v2 Announce Type: replace Abstract: We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinfor

QF3: Fast Flow RL with Filtered Q-Gradients

arXiv:2610.08789v1 Announce Type: cross Abstract: Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for i

safetyarxiv-cs-lg

Applications

QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting

arXiv:2610.06936v1 Announce Type: new Abstract: Irregular multivariate time series forecasting is a challenging yet important problem in real-world applications, where observations are often irregular

applicationsarxiv-cs-lg

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models

arXiv:2511.00053v2 Announce Type: replace-cross Abstract: The design of learning objectives is central to training time-series forecasting models. Existing learning objectives such as mean squared err

researcharxiv-cs-ai

Quality-Aware Self-Correcting Speech Translation on an Edge Device

arXiv:2610.07545v1 Announce Type: new Abstract: We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retrai

researcharxiv-cs-cl

Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks

arXiv:2603.08231v2 Announce Type: replace-cross Abstract: Paralinguistic speech tasks are often considered relatively language-agnostic, as they rely on extralinguistic acoustic cues rather than lexic

researcharxiv-cs-cl

Quantifying the Gap between Understanding and Generation within Unified Multimodal Models

model-releasesarxiv-cs-cl

arXiv:2602.02140v2 Announce Type: replace Abstract: Recent advances in unified multimodal models (UMM) have demonstrated remarkable progress in both understanding and generation tasks. However, whethe

Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs

arXiv:2610.07781v1 Announce Type: new Abstract: Post-training quantization reduces the cost of deploying language-model agents, but its effect on recovery from temporary tool failures can depend on ho

Quantum Entangled Multimodal Fusion Networks (QEMFN): Resource-Aware Hybrid Vision-Language Fusion via Trainable Entanglement

arXiv:2610.08216v1 Announce Type: new Abstract: Multimodal vision-language systems typically fuse image and text embeddings through classical operators such as concatenation, attention, bilinear pooli

Qubit-centric Transformer for Surface Code Decoding

arXiv:2510.11593v3 Announce Type: replace-cross Abstract: For reliable large-scale quantum computation, quantum error correction (QEC) is essential to protect logical information distributed across mu

researcharxiv-cs-ai

Tools

Quoting Ben Affleck

I've always been kind of into computers since I was young. And then when film started to move from analog film to digital, I became more interested in that aspect of it. And the visual effects workflo

toolssimon-willison

Tools

Quoting Jake Boggan

I was a graph theory junkie long ago and even moved to Budapest for awhile to study among the greats. While I was there I started working on Barnette's Conjecture which came to occupy my thoughts over

toolssimon-willison

Qwen 3.6 35B and indentation

Seems like a bit of a long shot, but since it’s so mystifying I figured I might as well ask: I have been trying to get Qwen 3.6 35B to function as a local implementation agent in my subagent driven de

model-releasesr-ollama

R2RI: A Multi-View Event and RGB Dataset for Robot-to-Robot Interaction

arXiv:2610.07117v1 Announce Type: new Abstract: Understanding and modeling interactions between autonomous agents is a fundamental challenge in robotics, with broad implications for collaborative syst

agentsarxiv-cs-cv

RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation

arXiv:2610.07851v1 Announce Type: new Abstract: Agentic workflows enable large language models (LLMs) to solve complex tasks by coordinating reasoning, tool use, and verification. However, a workflow

agentsarxiv-cs-ai

RACE-FPP: A Robust AI-assisted Characterisation Enhancement for Fringe Projection Profilometry

arXiv:2610.08213v1 Announce Type: new Abstract: Fringe Projection Profilometry (FPP) requires precise system characterisation to achieve reliable three-dimensional (3D) reconstructions; however, chara

researcharxiv-cs-cv

RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing

arXiv:2610.07409v1 Announce Type: new Abstract: We present RACER, a hierarchical control framework for wheel-based quadruped racing that combines an MPPI planner with a learned residual dynamics model

safetyarxiv-cs-ro

RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation

arXiv:2610.06932v1 Announce Type: new Abstract: Cache-based test-time adaptation (TTA) for vision-language models is often hindered by background bias in global representations and unreliable entropy-

safetyarxiv-cs-cv

RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway

arXiv:2610.06923v1 Announce Type: new Abstract: Artificial intelligence has advanced individual radiotherapy tasks, yet these capabilities remain separated across clinical stages, software environment

agentsarxiv-cs-ai

RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems

arXiv:2610.08571v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a ben

RAM-Net: Linear-Time Sequence Modeling with Sparsely Addressable State

arXiv:2602.11958v2 Announce Type: replace-cross Abstract: Linear attention offers an efficient alternative to full attention with a fixed-size recurrent state. However, this state is shared by all tok

researcharxiv-cs-cl

Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty

arXiv:2610.08578v1 Announce Type: new Abstract: Transformers provide a state-of-the-art modeling framework, yet poor calibration limits their reliability in safety-critical applications. A promising d

safetyarxiv-cs-lg

Rapid Fredholm stabilization of the Kuramoto--Sivashinsky equation with unrestricted, spatially-varying anti-diffusion

arXiv:2610.08764v1 Announce Type: cross Abstract: We develop the first feedback design for rapid stabilization of the Kuramoto--Sivashinsky equation with a spatially varying anti-diffusion coefficient

Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding

arXiv:2610.07342v1 Announce Type: new Abstract: On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectivenes

safetyarxiv-cs-ai