Search: “benchmark”

Manifold Drift in Flow Preference Optimization: A Root Cause of Reward Hacking

arXiv:2608.20011v1 Announce Type: new Abstract: Preference optimization is a standard alignment method for generative models, yet extending it to continuous-time dynamics remains non-trivial. In flow

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MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities

DGX agent

arXiv:2608.19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy

safetyarxiv-cs-cv

21 Aug 2026

Multi-Source Wasserstein Distributionally Robust Graph Learning

arXiv:2608.19914v1 Announce Type: new Abstract: Network topology inference from graph signals is central to graph signal processing with applications in neuroscience, sensor, and social networks. In p

safetyarxiv-cs-lg

Multimodal API support 🔌 🔹 Set model='deepseek-v4-flash-vision-exp' 🔹 Images are tokenized for billing: up to 384 tokens each, at V4-Flas…

model-releasesdeepseek--x

Multimodal API support 🔌 🔹 Set model='deepseek-v4-flash-vision-exp' 🔹 Images are tokenized for billing: up to 384 tokens each, at V4-Flash pricing 🔹 Supports Chat Completions, Messages & Responses 🔹 S

Hardware

NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents

NVIDIA’s Agentic Variation Operators (AVO) architecture delivers a frontier‑level general‑purpose agent by integrating persistent memory, supervision, and tool use within the system harness rather tha

hardwarenvidia-developer

Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation

arXiv:2608.20316v1 Announce Type: new Abstract: Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficiency by routing

PEA-DPO: Perception-Enhanced Alignment Direct Preference Optimization for MLLMs Alignment

arXiv:2608.19598v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, i

safetyarxiv-cs-ai

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

arXiv:2608.19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP

Local Ai

Prompt-Conditioned Channel Attention for Hierarchical Feature Modulation toward Anatomy-Agnostic Segmentation

arXiv:2608.20229v1 Announce Type: cross Abstract: Anatomically plausible segmentation remains challenging because of low contrast, ambiguous boundaries, and modality-specific artifacts. Interactive se

local-aiarxiv-cs-ai

ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning

arXiv:2506.07459v4 Announce Type: replace Abstract: Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequenc

Quantum Gaussian processes for prediction of channel observations

arXiv:2608.19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolut

Reading Between the Pixels: An Inscriptive Jailbreak Attack on Text-to-Image Models

arXiv:2604.05853v3 Announce Type: replace Abstract: Modern text-to-image (T2I) models can now render legible, paragraph-length text, enabling a fundamentally new class of misuse. We identify and forma

ReCache: Efficient KV Cache Reuse and Compression for Tool-Augmented LLM Agents

arXiv:2608.19662v1 Announce Type: new Abstract: Agentic language models repeatedly encode tool and skill schemas that recur across requests in different combinations and orders, preventing standard pr

RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations

arXiv:2608.19735v1 Announce Type: new Abstract: We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation. RecPFN is pretrained entirely on synthetic cli

researcharxiv-cs-lg

Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation

arXiv:2608.19890v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains. However, traditional TTA methods become ineffecti

researcharxiv-cs-lg

Rethinking the Evaluation and Optimization of LLM-Based Social Simulation

arXiv:2608.19689v1 Announce Type: new Abstract: LLM-based social simulation is a promising complement to traditional methods such as surveys and behavioral experiments. A core question is how to evalu

ReynoldsFlow: Physics-Inspired Spatiotemporal Flow Representation for Video Understanding

arXiv:2503.04500v3 Announce Type: replace-cross Abstract: Video understanding has largely relied on deep spatiotemporal architectures, including 3D convolutional networks and optical flow (OF) based m

researcharxiv-cs-ai

Tutorials

RIPE++: Reinforced Keypoint Learning from Positive Pairs Only

arXiv:2608.19693v1 Announce Type: new Abstract: Sparse keypoint extraction and matching underpin core tasks in geometric computer vision, including structure-from-motion, visual SLAM, augmented realit

tutorialsarxiv-cs-cv

ROBOSHACKLES: A Safety Dataset for Human-Injury Prevention in Embodied Foundation Models

model-releasesarxiv-cs-ro

arXiv:2606.18632v2 Announce Type: replace Abstract: Embodied Foundation Models (EFMs) integrate multimodal understanding, future-state reasoning, and executable robot actions. Yet their safety alignme

RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation

arXiv:2608.20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusio

Scaffolding Minds: Optimizing Latent Visual Target Representations for Multimodal Reasoning

arXiv:2608.19669v1 Announce Type: new Abstract: Latent reasoning has advanced multimodal reasoning through a two-stage training paradigm: (1) a helper image is encoded into latent tokens to teach visu

Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking

arXiv:2608.20005v1 Announce Type: new Abstract: Pretraining time series foundation models across heterogeneous datasets necessitates effective handling of varying sampling frequencies. Current methods

safetyarxiv-cs-lg

Scale-Separated Conditioning for Style-Encoder-Free Diffusion Stylization

arXiv:2608.19719v1 Announce Type: cross Abstract: Reference-based diffusion stylization requires separating target geometry from transferable appearance. Existing tuning-based methods often rely on al

Separating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition

arXiv:2608.19885v1 Announce Type: cross Abstract: Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn a new one,

Spike-based Belief Propagation in Nonlinear Dynamical Systems

arXiv:2608.19907v1 Announce Type: new Abstract: This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian infere

Local Ai

SPyCE: Skill-Policy Co-evolution for Multimodal Agents

arXiv:2607.13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps. Existing reinforcement learning

local-aiarxiv-cs-cl

SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?

arXiv:2608.19799v1 Announce Type: new Abstract: Software increasingly functions as part of the scientific instrument itself, making failures in scientific code capable of compromising not only program

Applications

TESTNAV: Pareto-Guided Search for Compositional Robustness Testing

arXiv:2608.19882v1 Announce Type: new Abstract: Deep learning models remain vulnerable to real-world input perturbations, especially when multiple corruptions co-occur in the same input (e.g., brightn

applicationsarxiv-cs-ai

The impact of feature engineering and an optimisation framework for ocean colour machine learning

arXiv:2608.19899v1 Announce Type: cross Abstract: Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparam

This is very nice work from NVIDIA. Like all high-performing approaches on ARC-AGI-3, it uses deep learning-guided on-the-fly synthesis of s…

model-releasesfrancois-chollet--x

This is very nice work from NVIDIA. Like all high-performing approaches on ARC-AGI-3, it uses deep learning-guided on-the-fly synthesis of symbolic world models, i.e. navigating the world by generatin

Ultra-High-Definition Restoration Transformers with Correlation Matching Transformation

arXiv:2608.20263v1 Announce Type: new Abstract: We propose UHDformer++, a general Transformer-based framework to solve numerous Ultra-High-Definition (UHD) image restoration tasks. UHDformer++ operate

Ultrafast Qwen3-TTS at 34 ms Time-to-First-Audio, Handling 10 Requests Per Second [OSS]

model-releasesr-localllama

Hey locallama! We recently open sourced a Qwen3-TTS 1.7B implementation that achieves 10 requests per second (RPS) and sub-50 ms p95 time-to-first-audio (TTFA) while maintaining real-time playback on

VGI-BENCH: Probing Visual Intelligence in Video Generation Models

arXiv:2608.19583v1 Announce Type: cross Abstract: Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable eva

researcharxiv-cs-ai

Video Evidence to Reasoning Efficient Video Understanding via Explicit Evidence Grounding

arXiv:2601.07761v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of v

What Matters for Latent Actions in Robot Learning

arXiv:2608.19613v1 Announce Type: cross Abstract: Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent a

When Automata Meet Streams: Temporal Logic Compilation for Stream-Based Robotics Task and Motion Planning

arXiv:2608.19453v1 Announce Type: new Abstract: Stream-based robotics Task and Motion Planning (TAMP) integrates discrete symbolic planning with dynamically generated continuous geometric parameters,

safetyarxiv-cs-ro

When Contextual Inference Fails: Cancelability in Interactive Instruction Following

arXiv:2603.19997v2 Announce Type: replace Abstract: We investigate the separation of literal interpretation from contextual inference in a collaborative block-building tasks, where an agent must resol

When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models

arXiv:2608.19208v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains und

safetyarxiv-cs-cl

When Text and Numbers Disagree: Evidence Arbitration in Large Language Models

arXiv:2608.20116v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in settings where textual summaries, numerical observations, and external tool outputs may provide co

WithEveryone: Unified Planning and Identity Grounding for Group Image Generation

arXiv:2608.20336v1 Announce Type: new Abstract: Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, th

World-Model-Grounded LLM Planning for AUV and ASV Navigation Near Offshore Wind Farms

model-releasesarxiv-cs-ro

arXiv:2608.19661v1 Announce Type: new Abstract: Large language models can turn a natural-language mission into a sequence of robot actions, but they do not have a sense of physics: they cannot judge h

Local Ai

Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures

arXiv:2608.19889v1 Announce Type: new Abstract: The entire ecosystem of open-source language models effectively relies on a single platform. What if this platform was forced to shut down tomorrow? Imp

local-aiarxiv-cs-ai

YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs

arXiv:2512.18046v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone d

A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation

arXiv:2608.18709v1 Announce Type: cross Abstract: Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and cross-domain generalizati

A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering

arXiv:2603.15722v3 Announce Type: replace-cross Abstract: The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Syste

Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework

arXiv:2608.18451v1 Announce Type: new Abstract: extbf{Background and Objective}: Reliable atrial fibrillation (AF) detection from electrocardiogram (ECG) signals remains challenging in real-world clin

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

arXiv:2608.18723v1 Announce Type: cross Abstract: Telecom operators traditionally offer predefined tariff grids, forcing users to choose from a limited set of plans. This paper proposes BFTR (Budget-F

CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation

arXiv:2608.18710v1 Announce Type: new Abstract: Recent advances in generative video models have enabled camera-controlled world video generation, allowing models to synthesize videos under user-define