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DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models

arXiv:2609.02685v1 Announce Type: cross Abstract: RAG has become the de facto method for incorporating new, corpus-specific knowledge into an instruction following LLM (Instruct LLM). Although RAG-bas

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Do Large Language Models Always Tell The Same Stories?

DGX agent

arXiv:2606.17350v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet whether these models are capable of gen

researcharxiv-cs-ai

3 Sept 2026

Do Large Language Models Capture the Diversity in their Training Data?

arXiv:2609.02275v1 Announce Type: cross Abstract: Large language models are trained to model conditional distributions over text, yet it remains inadequately understood whether they capture the full d

Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping

arXiv:2408.02932v3 Announce Type: replace-cross Abstract: Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within t

Dutch Books for Language Models

arXiv:2609.02797v1 Announce Type: cross Abstract: People increasingly use language models to support life decisions. Many such decisions involve a probabilistic forecast: How likely is a major life ev

DWFF-Net: A Multi-Scale Farmland System Habitat Identification Method with Adaptive Dynamic Weight Feature Fusion

arXiv:2511.11659v3 Announce Type: replace Abstract: To address insufficient accuracy in multi-scale segmentation for agricultural habitat recognition, this study proposes a Dynamic Weighted Feature Fu

DynaTokens: Controlling Token Dynamics for Continual Video-Language Understanding

arXiv:2603.06662v3 Announce Type: replace Abstract: Continual VideoQA with multimodal LLMs remains challenging because sequential adaptation induces task interference, while storing task-specific prom

Edit Knowledge, Not Just Facts via Multi-Step Reasoning over Background Stories

arXiv:2602.02028v3 Announce Type: replace Abstract: Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a c

Efficient All-in-One Weather Restoration using Spectral Harmonization

arXiv:2609.02839v1 Announce Type: new Abstract: Adverse weather conditions such as rain, haze, and snow significantly degrade image quality, posing challenges for both human perception and physical AI

Elite political incivility is rising across democracies

arXiv:2503.22411v2 Announce Type: replace Abstract: Is political incivility rising across democracies - and if so, why? Analysing approximately 13.8 million tweets from parliamentarians in 26 countrie

Emotional regulation improves deep learning-based image classification

arXiv:2606.13081v2 Announce Type: replace-cross Abstract: Emotion significantly influences cognition, enhancing memory and learning under certain conditions. Drawing on this principle, emotion-augment

Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps

arXiv:2412.01865v5 Announce Type: replace-cross Abstract: BrainAGE is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-

Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection

arXiv:2510.07277v2 Announce Type: replace Abstract: Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promisin

Evidential Deep Learning for Multi-Modal Anti-UAV Detection

arXiv:2609.01742v1 Announce Type: new Abstract: Anti-UAV systems increasingly fuse multiple sensors, yet their detection heads provide no per-modality reliability signal. This study evaluates whether

Exact Limits of Random Projections for Preserving Geometry: Distance Recovery, Nearest-Neighbor Rankings, and Covariance Shape in Gaussian Models

arXiv:2609.02155v1 Announce Type: new Abstract: The Johnson-Lindenstrauss (JL) lemma guarantees that a random projection of n points to m=O(arepsilon^{-2}log n) dimensions preserves pairwise squared d

FOCUS: Foot Observation Confidence for Robust Humanoid Proprioceptive Odometry

arXiv:2609.02222v1 Announce Type: new Abstract: Foot forward kinematics (FK) is widely used to improve proprioceptive legged odometry by providing reliable velocity constraints during foot support. Ex

researcharxiv-cs-ro

From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X

arXiv:2609.02262v1 Announce Type: cross Abstract: Ragebait refers to online content intentionally designed to provoke anger or outrage and thereby increase attention and engagement. However, reliable

From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images

arXiv:2609.02640v1 Announce Type: new Abstract: The rapid advancement of generative models has significantly worsened the problem of manipulated image detection, as these methods are capable of produc

From Feature Interaction to Feature Transport - A Unified Block for Scalable Recommendation Models

arXiv:2609.01655v1 Announce Type: cross Abstract: Unified recommendation models aim to jointly model non-sequential multi-field features and sequential user behaviors, but existing interaction-centric

From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs

arXiv:2609.02679v1 Announce Type: cross Abstract: When LLMs support public-facing or high-stakes workflows, missed fabrications can harm users and institutions, while false alarms consume limited huma

FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection

arXiv:2609.02212v1 Announce Type: new Abstract: Ensuring the reliability of deep learning models in real-time industrial defect detection is critical for high-stakes quality inspection. To mine uncert

GDB-Reward: From Evaluation Metrics to Training Rewards for Graphic Design

arXiv:2609.02813v1 Announce Type: new Abstract: Text-to-image models excel at natural image synthesis but struggle with graphic design, where success depends on satisfying precise constraints on typog

Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations

arXiv:2609.02600v1 Announce Type: new Abstract: This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentation

Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors

arXiv:2309.06349v2 Announce Type: replace-cross Abstract: Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a varia

Glass Segmentation with Fusion of Learned and General Visual Features

arXiv:2603.03718v2 Announce Type: replace Abstract: Glass surface segmentation from RGB images is a challenging task, with a number of applications in robotics and scene understanding. As glass lacks

GMTRouter: Personalized LLM Router over Multi-turn User Interactions

arXiv:2511.08590v2 Announce Type: replace Abstract: Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit divers

GPT-6 Astra by @OpenAI achieves SOTA on ARC-AGI: - Astra scores 63% on ARC-AGI-3, 99% via a new provider adapter harness - It surpasses huma…

researchfrancois-chollet--x

GPT-6 Astra by @OpenAI achieves SOTA on ARC-AGI: - Astra scores 63% on ARC-AGI-3, 99% via a new provider adapter harness - It surpasses human performance on 96% of ARC-AGI-3 levels - It builds the mos

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes

arXiv:2406.05033v3 Announce Type: replace Abstract: We study gradient descent (GD) dynamics on logistic regression problems with large, constant step sizes. For linearly-separable data, it is known th

Graph Machine: Towards Better Pretraining via Edges

arXiv:2609.02881v1 Announce Type: new Abstract: We introduce the Graph Machine (GM), an architecture that maintains an O(n)-sized state and accesses it through sparse, dynamic routing. Unlike methods

Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction

arXiv:2609.02251v1 Announce Type: new Abstract: Handwriting trajectory recovery aims to infer the dynamic writing process hidden behind a static handwritten image. Since offline handwriting preserves

HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation

arXiv:2609.01730v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) allows a server to run a language model directly on encrypted user prompts, but current approaches remain prohibiti

Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models

arXiv:2607.01535v2 Announce Type: replace Abstract: Despite the intense engagement surrounding low-level vision generalist models, their effectiveness in zero/few-shot scenarios beyond learned tasks r

HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design

arXiv:2609.02746v1 Announce Type: cross Abstract: Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made signi

hLLM: Single Pass Decoding for Generative Reranking

arXiv:2609.01807v1 Announce Type: cross Abstract: Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive dec

How Output Format Confounds Data Quality and Capability in Instruction Tuning

arXiv:2609.02015v1 Announce Type: new Abstract: Instruction-tuning data are judged by quality metrics, and tuned models are judged by benchmarks, but both judgments pass through an output interface: t

HyperMC: Multi-Fidelity Hyperparameter Tuning for Stochastic Gradient MCMC

arXiv:2609.02138v1 Announce Type: cross Abstract: Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their performance depends strongly on hyperparam

ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs

arXiv:2603.18579v2 Announce Type: replace-cross Abstract: Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions w

Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication

arXiv:2606.17372v3 Announce Type: replace-cross Abstract: Two recent studies itep{jones2026llms, zeng2026lvlms} reach apparently contradictory conclusions about whether large vision-language models (L

Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks

arXiv:2609.01800v1 Announce Type: new Abstract: Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use

Improved Gradient Descent Lower Bounds Beyond Nesterov

arXiv:2609.02855v1 Announce Type: cross Abstract: We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimization. Going beyond the classical Omega(n

InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation

arXiv:2609.02004v1 Announce Type: cross Abstract: Accurate segmentation of polyps and skin lesions is pivotal for clinical diagnosis, yet existing methods struggle with low contrast, ambiguous boundar

It is inevitable that all AI will converge towards symbolic learning (i.e. modeling data by finding the shortest symbolic program that expla…

researchfrancois-chollet--x

It is inevitable that all AI will converge towards symbolic learning (i.e. modeling data by finding the shortest symbolic program that explains it), since that is the optimally efficient form of AI. B

Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight

arXiv:2609.01976v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such f

Koopman-Based Robust Model Predictive Control for Nonlinear Systems with Stochastic Intermittent Measurements

arXiv:2609.02079v1 Announce Type: new Abstract: Intermittent state measurements pose fundamental challenges to model predictive control of constrained nonlinear systems because prediction uncertainty

researcharxiv-cs-ro

Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors

arXiv:2603.18593v2 Announce Type: replace Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing the

Latent Cluster Analysis for Vision-Language-Action Models

arXiv:2609.02634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal

researcharxiv-cs-ro

Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds

arXiv:2609.02568v1 Announce Type: cross Abstract: Volumetric video based on point cloud representations enables immersive virtual and augmented reality applications but introduces significant challeng

Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA

arXiv:2609.01687v1 Announce Type: new Abstract: Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult be