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Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction

arXiv:2610.07712v1 Announce Type: cross Abstract: Existing molecular and materials learning approaches often rely on a limited set of structural representations, which may capture only selected aspect

researcharxiv-cs-lg

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Human DGX agent

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Mathematical Proof Assistants for Teaching Logic: The LogiKEy Methodology

DGX agent

arXiv:2610.08214v1 Announce Type: new Abstract: We report on an approach to teaching logic to mixed groups of computer science, mathematics, and philosophy students, based on the logico-pluralistic Lo

tutorialsarxiv-cs-ai

7 Oct 2026

Measuring climate backlash in Twitter and Reddit archives: Lexical definitions, recorded responses and participant turnover

arXiv:2610.07634v1 Announce Type: new Abstract: Social media archives are often used to study resistance to climate action, but words, response counters and observed participants do not measure the sa

Mechanistic Interpretability of Atmospheric Rivers in GraphCast

arXiv:2610.07583v1 Announce Type: cross Abstract: While AI weather models now rival operational forecasts, how they represent the atmosphere internally remains an open question: feature attribution re

MedCORE: Criteria-Grounded Clinical Reasoning for Interpretable Medical Image Diagnosis

arXiv:2610.08528v1 Announce Type: cross Abstract: Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image featur

MedHorizon: Towards Long-context Medical Video Understanding in the Wild

model-releasesarxiv-cs-cv

arXiv:2605.06537v2 Announce Type: replace Abstract: Medical multimodal large language models (MLLMs) have advanced image understanding and short-video analysis, but real clinical review often requires

Medical Image Alignment Assessment as a Test of Generalist Visual Reasoning in Frontier Multimodal Models

model-releasesarxiv-cs-cv

arXiv:2610.06896v1 Announce Type: new Abstract: Frontier multimodal large language models (MLLMs) are increasingly positioned as general purpose visual reasoners as part of the quest for artificial ge

MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation

arXiv:2610.08327v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise in medical question answering and clinical reasoning, yet their improvement remains constrained by stati

Agents

MemCo: Memory-Centric Collaboration for Generalizing LLM Agents to Unseen Environments

arXiv:2610.07376v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate in interactive environments, where they need to make sequential decisions through observation, ac

agentsarxiv-cs-ai

MemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge

arXiv:2610.08669v1 Announce Type: cross Abstract: On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficient

MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding Agents

arXiv:2610.07257v1 Announce Type: new Abstract: Developers increasingly run a fleet of coding agents side by side on one workstation. The tools they reach for, terminal multiplexers like tmux and a ne

Applications

Memory Depth and Reconstructed Context Width: A Controlled Evaluation of Hierarchical Retrieval

arXiv:2610.08300v1 Announce Type: new Abstract: Long-term conversational memory is becoming an integral component of modern LLM systems. Proposed architectures group records by topics and events, cons

applicationsarxiv-cs-cl

Local Ai

Memory-Efficient Expert Routing for Distributed MoE Training

arXiv:2610.07333v1 Announce Type: cross Abstract: As Mixture-of-Experts (MoE) models scale toward hundreds of experts and higher top-k routing, memory efficiency in distributed training becomes a crit

local-aiarxiv-cs-ai

Memory Prediction Excess: A Probabilistic Quantity for Predictive Gain and Memory Length in Stochastic Processes

arXiv:2610.06894v1 Announce Type: cross Abstract: A central question in the prediction of stochastic processes is the extent to which past information can improve the probability of correctly predicti

researcharxiv-cs-lg

Tutorials

MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata

arXiv:2610.08479v1 Announce Type: cross Abstract: Few-shot meta-learning traditionally formulates task adaptation either as analytical gradient descent through unrolled computational graphs or as metr

tutorialsarxiv-cs-ai

Metonymic Circuits for Abstract Concept Grounding in Vision Transformers

arXiv:2610.06928v1 Announce Type: new Abstract: We study how Vision Transformers ground abstract concepts (e.g., angry) when training data provide limited direct referential evidence. We hypothesize a

Micro Neural Policies for Safe Real-Time Robotic Control

arXiv:2610.08541v1 Announce Type: cross Abstract: In this paper, we investigate the synthesis of Micro Neural Policies (MNP) to enable safe and robust real-time robotic control on computationally cons

safetyarxiv-cs-ai

Applications

MIM-VLA: Learning Physical Interaction Representations from Gripper Motor Feedback

arXiv:2610.08425v1 Announce Type: new Abstract: Vision-language-action (VLA) policies infer grasp actions primarily from visual observations and robot state, but do not explicitly represent the physic

applicationsarxiv-cs-ro

MINDSET: Energy-based Schema Evolution for Long Conversational Agent Memory

arXiv:2610.08586v1 Announce Type: new Abstract: Long conversational agents have become essential in our daily lives. They must remember what was said long back in order to help us efficiently complete

Minimal Witness Reinforcement Learning

arXiv:2610.07226v1 Announce Type: cross Abstract: ``What are the irreducible conditions that are sufficient to produce an outcome?'' is one of the most common questions that recur across computation a

safetyarxiv-cs-ai

MiniScope: Authorizing Agents with Least-Privilege Permissions

arXiv:2512.11147v2 Announce Type: replace-cross Abstract: AI agents are increasingly granted autonomous access to sensitive user data and third-party services, making effective permission management a

Mission-Aware Attestation Envelopes for Time-Critical Autonomous Action: A Hardware-in-the-Loop V2I Study

model-releasesarxiv-cs-ro

arXiv:2610.08771v1 Announce Type: cross Abstract: An autonomous system that asks for a privileged physical action is usually gated on integrity evidence: a platform proves what it is running, and the

Agents

Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data

arXiv:2610.08297v1 Announce Type: cross Abstract: Teleoperation serves as the fallback solution to autonomous driving but reliable functions of the teleoperation require a certain amount of mobile net

agentsarxiv-cs-ai

MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation

arXiv:2610.07511v1 Announce Type: cross Abstract: Mobile manipulators such as humanoid robots are increasingly deployed in dynamic, unstructured environments to perform dexterous manipulation tasks. H

researcharxiv-cs-cv

Model-Based Geometry-Aware Generative Optimization for Constrained Locomotion Planning

arXiv:2610.07772v1 Announce Type: new Abstract: Constrained Locomotion Planning (CLP) for quadrupeds and humanoids, where robots must satisfy collision avoidance, contact consistency, kinematic feasib

researcharxiv-cs-ro

Modeling Latent Disturbances for Robust Decision-Making in World Models

arXiv:2610.07599v1 Announce Type: cross Abstract: In this paper, we study robust decision-making in the latent space of world models (WMs). Robust optimization is a mathematical framework where, given

safetyarxiv-cs-ai

Modeling Time-Dependent Responses of Optical Compressors with Selective State Space Models

arXiv:2408.12549v4 Announce Type: replace-cross Abstract: This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. The

MoF: Preference-Aware Mixture Modeling for Black-Box LLM Personalization

arXiv:2610.08330v1 Announce Type: new Abstract: Proprietary Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet aligning their outputs with diverse

Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter

arXiv:2610.07231v1 Announce Type: cross Abstract: This work presents a novel learning-based pipeline for pose estimation of unknown spacecraft using only monocular images from a single servicer. The a

researcharxiv-cs-cv

Monte Carlo Estimation for KV Cache Eviction

arXiv:2610.07643v1 Announce Type: cross Abstract: Most KV-cache eviction methods ask, in effect, which memory appeared important while reading the prompt? We instead ask, which memory will matter whil

MoonGS: High-quality Representation of the Lunar Surface via Gaussian Splatting Using Robust Depth Features from Image Pairs

model-releasesarxiv-cs-cv

arXiv:2610.07110v1 Announce Type: new Abstract: High-quality 3D reconstruction of lunar terrain from sparse rover images is indispensable for autonomous lunar exploration, but remains challenging beca

More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding

arXiv:2610.04753v2 Announce Type: replace-cross Abstract: Autoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms. Spar

MRPilot: Supervising and Intervening LLM-Based Multi-Robot Teams through Mixed Reality

arXiv:2610.07477v1 Announce Type: cross Abstract: Large language models (LLMs) let users direct heterogeneous multi-robot systems (MRS) through natural language, but make task interpretation, robot as

researcharxiv-cs-ro

MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning

arXiv:2610.07662v1 Announce Type: new Abstract: Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation

safetyarxiv-cs-lg

Tutorials

Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors

arXiv:2610.08131v1 Announce Type: cross Abstract: Achieving compositional concept generalization (CoCoGen), the ability to understand novel situations by recombining learned primitives, remains a fund

tutorialsarxiv-cs-cv

Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology

arXiv:2610.08086v1 Announce Type: new Abstract: The clinical integration of AI systems in digital dermatology relies heavily on human trust. Clinically interpretable visual concepts can act as interme

researcharxiv-cs-cv

Multi-Label Perceptual Bug Detection in Video Games using Deep Learning on Gameplay Footage

model-releasesarxiv-cs-lg

arXiv:2610.08593v1 Announce Type: new Abstract: Traditional approaches for automated bug detection in video games, such as manual testing, can be beneficial for the improvement of quality assurance, b

Multi-Robot Multi-Goal Motion Planning with Stochastic Skills

arXiv:2610.07784v1 Announce Type: new Abstract: As robots are increasingly deployed in groups and share workspaces to execute real-world tasks, planning their concurrent motions around complex manipul

safetyarxiv-cs-ro

Multigroup Fairness and Omniprediction: Separations and Equivalences

model-releasesarxiv-cs-lg

arXiv:2610.07374v1 Announce Type: new Abstract: Omniprediction is a learning guarantee which requires a single predictor to be competitive relative to the best hypothesis from a benchmark class for an

Multimodal Knowledge Distillation for Gastric Adenocarcinoma Classification from Whole-Slide Images

arXiv:2610.07913v1 Announce Type: cross Abstract: Gastric adenocarcinoma (GA) is a leading cause of cancer-related mortality worldwide, and accurate histopathological subtype classification from whole

Muon Is Theoretically Wrong For Convolutions, But Empirically Effective

arXiv:2610.07103v1 Announce Type: cross Abstract: Muon, an optimizer known for its efficiency, has a clear interpretation for matrix-valued updates, but convolutional kernels are stored as four-dimens

Agents

nanoMuse: An Open-Source Personal Agent for Every Device You Own

arXiv:2610.08699v1 Announce Type: new Abstract: Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, wit

agentsarxiv-cs-ai

Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret

arXiv:2610.07737v1 Announce Type: cross Abstract: We study fair multi-armed bandits under the Nash Social Welfare (NSW) objective, which measures performance via the geometric mean of accumulated rewa

safetyarxiv-cs-lg

Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing

arXiv:2610.08095v1 Announce Type: new Abstract: Natural-language access to RDF knowledge graphs is a core Semantic Web ambition. Large language models (LLMs) have advanced Text-to-SPARQL, yet on unfam

Navier-Stokes lost in translation: Why Lean verification of AI autoformalisation does not guarantee correct natural language proofs

arXiv:2610.08144v1 Announce Type: cross Abstract: Autoformalisation is increasingly used to verify mathematical texts, including those generated by AI, as in OpenAI's announced proof of blow-up of sol

Applications

Navigating Route Latent Space for Synthesizable Molecular Design

arXiv:2610.07560v1 Announce Type: new Abstract: Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, limi

applicationsarxiv-cs-ai

Navigation with RF Cues: Embodied Perception Action under Multipath Uncertainty

model-releasesarxiv-cs-ro

arXiv:2610.08105v1 Announce Type: new Abstract: Smart factory inspection requires robots to reach connected equipment without a prior map or known target coordinates. Radio frequency (RF) signals from

Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning with a Generative Model

model-releasesarxiv-cs-lg

arXiv:2610.06931v1 Announce Type: new Abstract: In this paper, we study the sample complexities of value and policy learning in finite discounted Markov decision processes (MDPs) under recursive entro