All entries

Embedded Graph Flows for Categorical Graph Generation

arXiv:2609.05328v1 Announce Type: new Abstract: Generating categorical graphs requires choosing node and edge types that form a coherent structure without depending on node order. Many graph generator

Local Ai

Encore: Infinite Audio-Video Generation with Adaptive Signal Routing

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DGX agent

arXiv:2609.04249v1 Announce Type: cross Abstract: Existing audio-video generation methods produce well-synchronized clips but are limited to short durations, while long-video generation methods extend

local-aiarxiv-cs-cv

7 Sept 2026

Enhancing Affine Maximizer Auctions with Correlation-Aware Payment

arXiv:2602.09455v2 Announce Type: replace-cross Abstract: Affine Maximizer Auctions (AMAs), a generalized mechanism family from VCG, are widely used in automated mechanism design due to their inherent

Enhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion

arXiv:2609.04690v1 Announce Type: cross Abstract: Multimodal emotion recognition has attracted growing interest due to its importance in human-computer interaction, remote education, and healthcare. T

applicationsarxiv-cs-ai

ERPBench: Evaluating LLM Agents for Enterprise Decision-Making Across Competitive Market Ecologies

arXiv:2609.04667v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly proposed for enterprise workflows, yet existing evaluations rarely test whether business-decision con

EuroAlpaca: Task-Preserving Localisation of Instruction Data for European Languages

model-releasesarxiv-cs-cl

arXiv:2609.05043v1 Announce Type: new Abstract: Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages, but it can distort task-critical constra

// Evaluating and Improving LLM Self-Modeling // Really interesting paper. Can a model answer questions about its own behavior? The question…

// Evaluating and Improving LLM Self-Modeling // Really interesting paper. Can a model answer questions about its own behavior? The questions are deliberately verifiable, such as whether a particular

model-releasesdair-ai--x

Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze

researcharxiv-cs-cl

Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robots

arXiv:2507.17049v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA)-enabled robots integrate visual perception, natural language understanding, and action planning to interpret thei

researcharxiv-cs-ro

Evaluation of Phonetic Encoding Algorithms on Transcription Datasets

arXiv:2609.04391v1 Announce Type: new Abstract: In this work, a novel evaluation scheme built on a generalized variant of the Rand Index measure, namely, the Hullermeier-Rifqi Index, is proposed in or

researcharxiv-cs-cl

Safety

Evidence Integration in Large Language Models

arXiv:2609.04290v1 Announce Type: cross Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how

safetyarxiv-cs-ai

EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents

arXiv:2607.09773v2 Announce Type: replace Abstract: Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although

EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?

model-releasesarxiv-cs-cl

arXiv:2609.04280v1 Announce Type: cross Abstract: Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what they can do.

EXAONE Forecast for Finance

arXiv:2609.04239v1 Announce Type: new Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series (TS) foundation model (TSFM) tailored to financial

Excited to build alongside this incredible community @KanishkaNarayan 🇬🇧

Excited to build alongside this incredible community @KanishkaNarayan 🇬🇧 More AI jobs, investment and support, especially for young people not in education, employment and training. Welcome to the UK

applicationsreplit--x

Industry

🚨EXCLUSIVE: David Silver is assembling an absurd reinforcement learning team at Ineffable Intelligence. Six new “cofounders,” including fou…

🚨EXCLUSIVE: David Silver is assembling an absurd reinforcement learning team at Ineffable Intelligence. Six new “cofounders,” including four ex-DeepMind researchers. AlphaStar RL lead Junhyuk Oh will

industrysonya-huang--x

exllamav3 comfortably beats llama.cpp running CPU-offloaded Qwen-3.8-Flash-Next on my setup!

model-releasesr-localllama

I got 2x 20GB RTX 3080s + 128GB of DDR4 2666hz RAM (only 4 of 6 channels populated) + a Xeon 6148 I've always been a llama.cpp person and I've been running Unsloth's Q4_K_XL quant of Qwen 3.8 Flash Ne

ExLlamaV3 is underrated

model-releasesr-localllama

I moght get shit on for posting this but, I feel like i don't see this being talked enough and it feels like such a waste of a good piece of software. Exl3 is incredible, albeit only if you have NVIDI

Explainable Clustering of Mixture Models

arXiv:2411.01576v3 Announce Type: replace Abstract: The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with K leaves

Explainable Multimodal Deep Learning Integrating Imaging and Clinical Data for Oral Potentially Malignant Disorder Detection

arXiv:2609.04512v1 Announce Type: cross Abstract: Oral potentially malignant disorders (OPMDs) are critical precursors to oral cancer, yet clinical detection remains challenging because of substantial

applicationsarxiv-cs-cv

Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving

arXiv:2509.22480v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been widely used for problem-solving tasks. Most recent work improves their performance through supervised f

researcharxiv-cs-ai

Extremely Sparse Supervision Incentivizes Reasoning Ability

arXiv:2609.04565v1 Announce Type: new Abstract: Large language models demonstrate increasingly strong reasoning capabilities through effective post-training. Yet, prevailing post-training methods opti

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

arXiv:2609.04501v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, wher

FailSAE: Towards Interpretable Failure Prediction for Vision-Language Models via Sparse Autoencoders

arXiv:2609.04276v1 Announce Type: new Abstract: Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by aligning visual and textual representations in

FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models

arXiv:2609.04277v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably duri

Safety

Fascinating paper from Anthropic and colleagues. They study whether models can tell when they are being tested. It turns out that capable mo…

Fascinating paper from Anthropic and colleagues. They study whether models can tell when they are being tested. It turns out that capable models can tell when they are being tested rather than deploye

safetydair-ai--x

Hardware

Fast Gauss Sums via Flash Attention

arXiv:2609.04910v1 Announce Type: new Abstract: Gaussian kernel sums are the computational core of maximum mean discrepancies (MMDs), kernel gradient flows, Stein variational gradient descent (SVGD),

hardwarearxiv-cs-lg

Fast Surrogate Modeling of Excitable and Oscillatory FitzHugh-Nagumo Dynamics with Parametric Neural Operators

model-releasesarxiv-cs-lg

arXiv:2609.04549v1 Announce Type: new Abstract: The FitzHugh-Nagumo (FHN) system serves as a simplified model of neuronal voltage dynamics, capturing the activator-inhibitor structure behind both isol

Local Ai

FAVE: Foveated Adaptive Visual Encoding for Efficient Fine-Grained Visual Understanding

arXiv:2609.04392v1 Announce Type: new Abstract: Fine-grained visual understanding depends on local detail, yet visual encoders face a trade-off between costly full-image high-resolution processing and

local-aiarxiv-cs-cv

Local Ai

FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification

arXiv:2609.05223v1 Announce Type: new Abstract: Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased data, which in medicine are distributed a

local-aiarxiv-cs-lg

Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates

arXiv:2609.04815v1 Announce Type: new Abstract: Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence

Safety

Few-Shot Video Recognition via Hierarchical Metric Learning

arXiv:2609.05242v1 Announce Type: new Abstract: Few-shot action recognition (FSAR) aims to recognize unseen action categories with only a small number of annotated video samples. Recent works typicall

safetyarxiv-cs-cv

FinalityBench: An Effect-Level Benchmark for Agent Decisions Under Delayed and Conflicting Financial Finality

arXiv:2609.04706v1 Announce Type: new Abstract: A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated, dropped and reordered, so for minutes at

FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement

arXiv:2609.05325v1 Announce Type: new Abstract: Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging. Dense smoke and

applicationsarxiv-cs-ro

First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-Haves

arXiv:2609.05224v1 Announce Type: new Abstract: Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-wo

agentsarxiv-cs-cv

FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search

arXiv:2609.05207v1 Announce Type: cross Abstract: Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. H

Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks

arXiv:2512.14779v2 Announce Type: replace Abstract: Standard weather forecast evaluations focus on the forecaster's perspective and on a statistical assessment comparing forecasts and observations. In

Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents

arXiv:2609.04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every

agentsarxiv-cs-ai

Fractal and Chaotic Activation Functions in Echo State Networks: Preprocessing Topology Governs the Echo State Property

model-releasesarxiv-cs-lg

arXiv:2512.14675v2 Announce Type: replace Abstract: Contemporary reservoir computing relies heavily on globally Lipschitz, well-behaved activation functions, limiting applications in defense, disaster

Fractal basins trap latent reasoning

arXiv:2609.04963v1 Announce Type: new Abstract: Reasoning allows artificial intelligence models to revisit and correct their mistakes, enabling recent frontier advances in mathematical theorem solving

agentsarxiv-cs-lg

Fractional-Order Adaptive Motion Magnification: Phase-Reliability Weighting for Noise-Constrained Video Amplification

arXiv:2609.04502v1 Announce Type: cross Abstract: Eulerian video amplification boosts sub-pixel motion by band-pass filtering per-pixel intensity traces and applying a uniform gain. That gain ignores

Hardware

From 80x to 385x: A Best-Matching-Unit Search at the L2 Roof, Measured Against a Symmetrically Tuned Baseline

arXiv:2609.05138v1 Announce Type: new Abstract: Comparisons between GPU implementations are usually asymmetric: one side is tuned by its author, the other is run as found. I report a programme that tu

hardwarearxiv-cs-lg

From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs

arXiv:2609.04543v1 Announce Type: new Abstract: A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the m

researcharxiv-cs-ai

From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy

model-releasesarxiv-cs-lg

arXiv:2609.04881v1 Announce Type: new Abstract: Although Deep Neural Networks have become foundational in many areas of Machine Learning, high computational demands limit their application in resource

From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents

arXiv:2609.04869v1 Announce Type: new Abstract: Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction experience is typically transient: procedural

From Interpretability Methods to Interpretable Models

arXiv:2609.05399v1 Announce Type: new Abstract: More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, feature visualization, concept-based, and c

// From Language Models to World-Acting Systems // A critical review of agentic AI, and a framework that is genuinely useful for deciding ho…

// From Language Models to World-Acting Systems // A critical review of agentic AI, and a framework that is genuinely useful for deciding how much authority to hand an agent. Here is how it works. The

agentsdair-ai--x

From Language Models to World-Acting Systems: Progress and Limits of Agentic AI across Digital, Social, Virtual, and Physical Environments

arXiv:2609.04894v1 Announce Type: new Abstract: Large language models become consequential agents when surrounding systems let outputs change external state. Models now call tools, operate interfaces,

agentsarxiv-cs-ai