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arXiv:2608.15605v1 Announce Type: new Abstract: This study investigates the challenge of ambiguity faced by Vision-Language Models (VLMs) in understanding spatial semantics. Spatial cognition, shaped

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Arm-Aware Guided Dexterous Grasp Generation with Arm-Agnostic Grasp Models

DGX agent

arXiv:2608.16351v1 Announce Type: new Abstract: Dexterous grasp generation that considers arm-related constraints is crucial in real-world scenarios involving arm environment collision avoidance, work

applicationsarxiv-cs-ro

18 Aug 2026

b10481: CUDA: MMVQ nwarps=8 for bs=1 for dense models on DGX Spark (#26843)

local-aillama-cpp-releases

CUDA: MMVQ nwarps=8 for bs=1 for dense models on DGX Spark Signed-off-by: ynankani ynankani@nvidia.com skip moe experts and allow others based on k geometry (allow only small idle tail) Signed-off-by:

Doubly robust nearest neighbors in factor models

arXiv:2211.14297v4 Announce Type: replace-cross Abstract: We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models. We consider a

Dynamic Multi-Byte Prediction With Hierarchical Language Models

arXiv:2608.15454v1 Announce Type: new Abstract: Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization.

Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models

arXiv:2608.14822v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have improved the flexibility and generality of robotic manipulation, yet they remain fragile to online disruption

Macroeconomic Forecasting with Large Language Models

arXiv:2407.00890v5 Announce Type: replace-cross Abstract: This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forec

applicationsarxiv-cs-cl

PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

arXiv:2505.23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dyna

Reminder that the data that feeds large language models – and thereby influences the world - can change at any moment, without warning.

Reminder that the data that feeds large language models – and thereby influences the world - can change at any moment, without warning. looks like reddit is almost wiped from chatgpt sources the query

safetygary-marcus--x

UC-VLM: Consistency-Driven Learning for AI-Generated Image Detection with Vision-Language Large Models

arXiv:2608.15238v1 Announce Type: new Abstract: Vision-Language Large Models (VLLMs) are promising for AI-generated image (AIGI) detection because they can produce both a prediction and a natural-lang

Modular Cognitive Architecture Emerges in Large Language Models

arXiv:2608.13567v1 Announce Type: new Abstract: The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about o

17 Aug 2026

JUST IN: OpenAI shuts down team responsible for assessing whether its AI models could pose 'catastrophic risks,' FT reports.

OpenAI has shut down the internal team that evaluated whether its AI models could pose “catastrophic risks,” a decision reported by the Financial Times on August 16, 2026. The move was announced via a

safetygary-marcus--x

16 Aug 2026

Federated Compositional Muon Optimizer for Matrix-Wise Models

arXiv:2608.12710v1 Announce Type: new Abstract: Muon, a more recently developed optimizer, is useful for matrix-wise models in AI areas. Although many works have studied Muon and its variants, these m

14 Aug 2026

Large Language Models Persuade Without Planning Theory of Mind

arXiv:2602.17045v2 Announce Type: replace Abstract: A growing body of work attempts to evaluate the theory of mind (ToM) abilities of humans and large language models (LLMs) using static, non-interact

safetyarxiv-cs-cl

14 Aug 2026

The AI Accountability Ecosystem in the Era of Language Models

arXiv:2608.12320v1 Announce Type: cross Abstract: This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the

14 Aug 2026

A consequence of how frontier models are trained is motivated reasoning, a phenomenon well studied in humans and discussed in this podcast f…

applicationsyoshua-bengio--x

A consequence of how frontier models are trained is motivated reasoning, a phenomenon well studied in humans and discussed in this podcast from @PalisadeAI. Our thoughts, beliefs and reasonings tend t

13 Aug 2026

A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa

arXiv:2608.11053v1 Announce Type: cross Abstract: The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming. However, many e

A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models

arXiv:2608.10939v1 Announce Type: cross Abstract: Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet

Evaluation-Conditioned Training: Teaching Models to Generalize to Stronger Oversight Regimes

arXiv:2608.10209v1 Announce Type: new Abstract: Feedback signals used to train Large Language Models (LLMs) are the primary driver of their behavior and our main lever for instilling alignment with hu

safetyarxiv-cs-ai

Expedia recently moved its ranking models to a state-of-the-art Keras 3 setup. Results: 30% faster training, and inference latency decreased…

researchfrancois-chollet--x

Expedia recently moved its ranking models to a state-of-the-art Keras 3 setup. Results: 30% faster training, and inference latency decreased by 70%. Read their writeup about the upgrade: https://mediu

Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models

arXiv:2608.10484v1 Announce Type: cross Abstract: Action verbs describe not only the physical outcomes of actions, but also how those actions are performed. Yet action representations in vision-langua

A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models

arXiv:2608.08770v1 Announce Type: cross Abstract: Diffusion models are increasingly used as controllable samplers, whose generations can be steered at inference time according to a chosen reward funct

Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Models

arXiv:2510.13643v2 Announce Type: replace Abstract: Vision foundation models such as DINOv2 enable strong few-shot anomaly detection (FSAD) through simple non-parametric k-nearest-neighbor (k-NN) scor

Archer: Adaptive Reuse of Cached Hidden States for Efficient Rollback in Diffusion Language Models

arXiv:2608.08086v1 Announce Type: new Abstract: Diffusion language models (DLMs) iteratively refine a sequence, allowing earlier predictions to be revised as context evolves. This rollback capability

researcharxiv-cs-cl

eBIRD: Event-based Intensity Image Reconstruction Using Controllable Diffusion Models

arXiv:2608.08519v1 Announce Type: new Abstract: Intensity-image reconstruction from event streams remains a challenging problem due to the binary, sparse, and asynchronous nature of event data. This w

EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

arXiv:2608.08138v1 Announce Type: new Abstract: Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increa

Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach

arXiv:2608.08636v1 Announce Type: cross Abstract: Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, l

Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data

arXiv:2608.07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilize

LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection

arXiv:2608.09633v1 Announce Type: new Abstract: Face presentation attack detection (PAD) aims to reliably detect a wide range of presentation attacks. While PAD methods achieve strong performance with

Multilingual Emotion Neurons in Large Audio-Language Models

arXiv:2608.08772v1 Announce Type: new Abstract: Emotion is central to human communication, and its expression varies across languages. Large audio-language models (LALMs) achieve strong performance on

researcharxiv-cs-cl

RAVEN-Eval: Rubric-Guided Automatic Evaluation for AI Video Generation Models Based on LMM Preference Judgement

arXiv:2608.09111v1 Announce Type: new Abstract: AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-a

Rethink Before You Execute: Adaptive Execution for World Action Models

arXiv:2608.09492v1 Announce Type: new Abstract: World Action Models (WAMs) jointly predict future actions and the evolution of the environment. At each inference, a WAM generates a chunk of actions an

applicationsarxiv-cs-ro

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models

arXiv:2508.16406v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks, which attempt to elicit harmful responses from LLMs. The evolving nature

safetyarxiv-cs-cl

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models

arXiv:2608.08622v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated strong performance in open-ended video understanding, yet they remain prone to fluent responses u

Towards a Theoretical Understanding of Two Tower Recommendation Models

arXiv:2403.00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Ama

10 Aug 2026

Quick survey (2 min) on trust in hardware specs for open-source models

Hi everyone, I'm a systems analysis student researching a problem a lot of you probably know well: how much you actually trust the published VRAM/RAM requirements for open-source models before trying

local-air-ollama

8 Aug 2026

Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles

arXiv:2608.05761v1 Announce Type: new Abstract: The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and

applicationsarxiv-cs-lg

Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies

arXiv:2608.05993v1 Announce Type: new Abstract: Much clinical value is conveyed not through structured records but through communication: exchanges in which patients describe symptoms, clinicians reas

safetyarxiv-cs-cl

Hierarchical Latent Prediction for Language Models

arXiv:2608.05806v1 Announce Type: cross Abstract: While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optima

Respect Your Zero-Shot Uncertainty: Conservative Calibration for Test-Time-Adapted Vision-Language Models

arXiv:2608.05945v1 Announce Type: new Abstract: Test-time adaptation (TTA) can improve the recognition accuracy of vision-language models under distribution shift, but often degrades calibration, maki

SAGA: Score-Weighted Adaptive Generation Alignment for Low-Resource Nordic Language Models

arXiv:2608.06179v1 Announce Type: new Abstract: Preference optimisation has proven effective for improving large language models but typically relies on costly human preference annotations. Extending

safetyarxiv-cs-lg

Topometric Autonomous Vehicle Localization by Combining Visual Embeddings and Feed-Forward 3D Models

arXiv:2608.06021v1 Announce Type: new Abstract: Effective Visual Localization (VL) requires a map of the environment that combines compactness for efficient scalability with robustness against visual

local-aiarxiv-cs-ro

Wan-Animate-2: Pushing the Application Boundaries of Character Animation Models

📝 Introduction We present Wan-Animate-2, a novel end-to-end character animation framework that directly consumes driving videos in a redesigned Diffusion Transformer, which achieves high-fidelity moti

local-air-localllama

Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation

arXiv:2608.05576v1 Announce Type: new Abstract: When a large language model (LLM) writes Harry Potter fanfiction, it reliably produces fundamental elements of the Hogwarts universe, such as recognizab

researcharxiv-cs-cl

A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Models

arXiv:2608.04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times. We evaluate fixed-cutoff drcosa

An Inline Control Architecture for Language Models in Intelligent Transportation Systems

arXiv:2608.04065v1 Announce Type: cross Abstract: Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator a

safetyarxiv-cs-ai

CARGO-VL: Counterfactual Arbitration with Risk-Constrained Group Optimization for Vision-Language Models

arXiv:2608.04509v1 Announce Type: new Abstract: Vision-language systems combine images with retrieved text, but these sources can disagree or jointly fail to support an answer. Reliable models must id

safetyarxiv-cs-ai

CofactVLA: Deconfounding Vision-Language-Action Models via Counterfactual Intervention

arXiv:2608.04396v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have driven significant progress in robotic manipulation, yet they fundamentally struggle with the vision-override p