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LSearch - Spotlight for windows with embedding

model-releasesr-localllama

I built a spotlight search for windows. It has an embedding model and a MoE models to go with it. The MoE It's 2 Qwen 3.5 4B models, I fine tuned one for coding work and another for agentic work, and

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Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

DGX agent

arXiv:2610.12638v1 Announce Type: cross Abstract: Day-ahead electricity forecasts can help building operators schedule heating, ventilation, and other services around anticipated demand. Choosing a fo

model-releasesarxiv-cs-ai

12 Oct 2026

Local Ai

Machine Unlearning for Federated Socially Fair k-Means Clustering

arXiv:2610.13717v1 Announce Type: new Abstract: Federated clustering can support applications such as patient stratification across hospitals and customer segmentation across distributed financial dat

local-aiarxiv-cs-lg

MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents

model-releasesarxiv-cs-cl

arXiv:2610.12757v1 Announce Type: new Abstract: Task-oriented conversational agents remain fragile under realistic conditions, especially when users exhibit non-cooperative behavior. Existing function

MagicBeanStalk: Escaping the RLVR Zero Advantage Trap with Recursive Data Synthesis

arXiv:2610.12659v1 Announce Type: new Abstract: Data-centric scaling of RLVR has focused on collecting or generating verifiable training problems. However, corpus size alone does not determine whether

MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures

arXiv:2610.13534v1 Announce Type: new Abstract: Long-horizon embodied agents must maintain task-relevant information, coordinate heterogeneous robots, and adapt their decisions as execution unfolds. E

ManeuverFlow: Maneuver Hypothesis Generation with Future Evidence for Trajectory Evaluation and Refinement in End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2610.13409v1 Announce Type: new Abstract: End-to-end planning in complex scenes must model diverse driving intentions and select the most appropriate trajectory using scene context and future ri

Mapless, Stabilizing, and Anti-Stuck Flipper Control for Articulated Tracked Robots

arXiv:2610.13838v1 Announce Type: new Abstract: Nearly every autonomous or semi-autonomous flipper controller assumes access to an elevation map of the terrain under and in front of the robot. The ter

agentsarxiv-cs-ro

MARGoS: A Configuration-Driven Platform for Reproducible MARL Experiments in ARGoS

arXiv:2610.13772v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) has become a prominent research direction, particularly within collective robotics, where the ARGoS simulato

agentsarxiv-cs-ro

Market Impact Estimation via Causal Foundation Models

arXiv:2610.12537v1 Announce Type: new Abstract: When a trader executes a large order, the relevant question is not what will the price be? but what would the price have been had I not traded? Market i

Masked Diffusion Language Agents for Tool-Integrated Chemical Reasoning

arXiv:2610.13330v1 Announce Type: new Abstract: Designing tool-integrated reasoning agent systems for complex chemical tasks remains a fundamental challenge. Although recent chemical agents have demon

Matched-Context Group Policy Optimization for LLM Agent Training

arXiv:2610.13755v1 Announce Type: cross Abstract: Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group o

safetyarxiv-cs-ai

MathVis: Can AI Reason through Drawing? A Benchmark for Mathematical Figure Generation

arXiv:2610.12954v1 Announce Type: new Abstract: This paper introduces MathVis, a benchmark designed to evaluate LLMs' ability to visualize frontier mathematical problems and perform diagram-assisted r

MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents

arXiv:2610.13829v1 Announce Type: cross Abstract: A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one

Measuring Artifact Reliance in Machine-Generated Text Detectors

arXiv:2610.13583v1 Announce Type: new Abstract: Detectors of machine-generated text score almost perfectly on scientific-literature benchmarks, implying we can reliably distinguish with these detector

researcharxiv-cs-cl

MENTOR: Learning from Failure with On-Policy Pedagogical Intervention

arXiv:2610.13594v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) is a key driver for Large Reasoning Models, but it is vulnerable to 'zero-reward collapse', a trai

Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data

arXiv:2610.12618v1 Announce Type: cross Abstract: Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation

researcharxiv-cs-ai

Meta has some Wonder Full new AI models

Meta is making up for lost time in the AI race. After launching Muse Spark, the first model from its revamped Superintelligence Labs division, and buzzy AI agent Muse, it's now launching two new model

agentsthe-verge-ai

Meta introduces Muse Wonder and Muse Wonder Full, saying they deliver state-of-the-art performance on personal and professional agentic tasks (Meta AI Research)

Meta AI Research: Meta introduces Muse Wonder and Muse Wonder Full, saying they deliver state-of-the-art performance on personal and professional agentic tasks — Fourteen months ago, we began a ground

agentstechmeme

Meta-Reflective Evolution Effectively Improves Production AI Systems

arXiv:2610.12794v1 Announce Type: cross Abstract: Large Language Model (LLM)-guided evolutionary optimization has successfully improved prompts, programs, and even entire AI systems. Most optimization

agentsarxiv-cs-ai

Meta releases Muse Wonder AI models built to run personal and professional agents

model-releasessiliconangle

Meta Platforms Inc. today released Muse Wonder and Muse Wonder Full, two artificial intelligence models it says are its most capable yet at running agents that handle personal and professional tasks.

Meta says it plans to power its Muse AI agent with Wonder, starting with subscribers, and will develop specialized models it 'can bring to everyone using Muse' (Robert Hart/The Verge)

Robert Hart / The Verge: Meta says it plans to power its Muse AI agent with Wonder, starting with subscribers, and will develop specialized models it “can bring to everyone using Muse” — Meta says the

agentstechmeme

MetaJoin: Adaptive Join Selections for Heterogeneous Tables

arXiv:2512.02444v2 Announce Type: replace-cross Abstract: Joining heterogeneous tables requires different forms of correspondence, including exact agreement, syntactic transformation, semantic similar

MHR-Router: Multimodal Heterogeneous Retrieval-Enhanced Adaptive Routing for VLM Inference

arXiv:2610.13278v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit complementary capabilities, yet selecting the most suitable expert for a given image-question query remains challe

Microsoft debuts multichiplet Maia 300 inference accelerator

Microsoft Corp. today pulled back the curtains on Maia 300, its newest internally developed artificial intelligence accelerator. The chip is optimized for inference. Microsoft estimates that it can pr

hardwaresiliconangle

Applications

Microsoft unveils its third-gen in-house AI chip, the Maia 300, built on TSMC's N2P process node and aimed at serving as its 'primary AI inference system' (Tom Warren/The Verge)

Tom Warren / The Verge: Microsoft unveils its third-gen in-house AI chip, the Maia 300, built on TSMC's N2P process node and aimed at serving as its “primary AI inference system” — The new Maia 300 ch

applicationstechmeme

Microsoft’s new AI chip makes it less reliant on Nvidia’s GPUs

Microsoft is announcing its third-generation Maia in-house AI chip today, designed to be the 'primary AI inference system' for Microsoft's products and services. Maia 300 not only improves the cost an

hardwarethe-verge-ai

Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing

arXiv:2610.12881v1 Announce Type: new Abstract: Understanding and manipulating spatial relations is fundamental to AI systems that reason about physical environments and construct grounded world model

Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving

arXiv:2610.12575v1 Announce Type: cross Abstract: Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture tempora

safetyarxiv-cs-ro

Minimax H3 Refmod Workflow

hardwarer-stablediffusion

https://preview.redd.it/f7ytj63103vh1.jpg?width=2996&format=pjpg&auto=webp&s=4f5ff97fae270d5b912fcb4193dd75b865604dce Since my last post has been deleted unfortunately (due to a mega-link), here we go

Minimax Rates for Estimating Powered Sliced Wasserstein Functionals

arXiv:2610.12740v1 Announce Type: cross Abstract: We study the two-sample estimation of the powered Sliced Wasserstein functional under projected r-th moment assumptions. Let N=nwedge m, where n and m

Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices

arXiv:2610.12970v1 Announce Type: cross Abstract: The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost,

hardwarearxiv-cs-lg

Industry

Mistlabs takes its Ambi AI system out of beta on Apple devices with $26M in funding

Ambient intelligence startup Mistlabs Ltd. today brought its Ambi artificial intelligence system out of beta test on Apple Inc. devices, backed by 26 million in new funding. Ambi is designed to act wi

industrysiliconangle

Mitigating Capability Degradation in LLM Self-Improvement via Representation Geometry Regularization

arXiv:2610.12855v1 Announce Type: new Abstract: On-Policy Self-distillation is a self-improvement paradigm that enables large language models (LLMs) to improve their capabilities by leveraging the sam

safetyarxiv-cs-ai

Mixture of Many Zero-Compute Experts: A High-Rate Quantization Theory Perspective

arXiv:2510.03151v2 Announce Type: replace Abstract: This paper uses classical high-rate quantization theory to provide new insights into mixture-of-experts (MoE) models for regression tasks. Our MoE i

Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data

arXiv:2610.13637v1 Announce Type: cross Abstract: Machine learning models are increasingly used for automated feature extraction from weakly annotated bioimaging data, such as microscopy and cytomorph

researcharxiv-cs-ai

Model-free Bayesian Clustering and Multimodal Posterior Summarisation

arXiv:2610.13397v1 Announce Type: cross Abstract: We develop a model-free, noise-aware Bayesian clustering framework that constructs a martingale posterior over clusterings from a predictive Bayesian

MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?

arXiv:2610.12522v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations.

Monarch-MoE: Structured Experts for Multimodal Continual Instruction Tuning

arXiv:2610.13215v1 Announce Type: new Abstract: Learning new tasks in multimodal continual instruction tuning can cause catastrophic forgetting of previously acquired knowledge. Although multi-expert

MoPrO-SD: Modular Prompt Optimization for Stance Detection

model-releasesarxiv-cs-cl

arXiv:2610.13386v1 Announce Type: new Abstract: Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Al

MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design

arXiv:2610.13305v1 Announce Type: new Abstract: We introduce MORAD, an online reinforcement learning framework for backbone-conditioned RNA diffusion models that trains a single shared policy across d

safetyarxiv-cs-lg

MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing

arXiv:2610.12963v1 Announce Type: cross Abstract: Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Bey

safetyarxiv-cs-ai

MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution

arXiv:2609.35664v2 Announce Type: replace-cross Abstract: Gated Linear Attention (GLA) Transformers advance linear recurrent models through data-dependent gating, but face a core limitation: the fixed

Multi-Agent Self-Supervision: Recursive self-improvement through collective intelligence Blog post: https://pub.sakana.ai/mass/ ⚖️

The blog post “Multi‑Agent Self‑Supervision: Recursive self‑improvement through collective intelligence” by @hardmaru describes how multiple AI agents can jointly supervise and refine each other’s lea

agentsdavid-ha--x

Multi-Stream Attention Residuals

arXiv:2610.13630v1 Announce Type: new Abstract: Attention Residuals (AttnRes) enables content-dependent retrieval over network depth, which can be viewed as single-stream softmax attention along the d

researcharxiv-cs-cl

Multidimensional Bayesian Active Machine Learning of Working Memory Task Performance

arXiv:2510.00375v3 Announce Type: replace Abstract: While adaptive experimental design has outgrown one-dimensional, staircase-based adaptations, most cognitive experiments still control a single fact

Multimodal Image-based Robotic Assistance and Guidance Environment (MIRAGE): A Novel Unified Framework for Surgical Navigation and Visualization

arXiv:2610.12780v1 Announce Type: new Abstract: This paper presents the computational architecture and quantitative preclinical evaluation of MIRAGE, a multimodal image-guided robotic surgery framewor

researcharxiv-cs-ro

Must-read paper on the effectiveness of multi-agent systems and persistent memory. We have recently seen many viral examples of multi-agent …

Must-read paper on the effectiveness of multi-agent systems and persistent memory. We have recently seen many viral examples of multi-agent systems scaled via persistent memory. This work takes a clos

agentsdair-ai--x