Search: “framework”

arXiv:2609.12108v1 Announce Type: new Abstract: Functional integration is a growing trend in vehicle control, often involving the coordination of multiple controllers to achieve various objectives sim

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Model Releases

One Skill Does Not Fit All: Automatic Discovery and Taxonomy-Guided Routing of Frame-Selection Skills for Long-Video Question Answering

DGX agent

arXiv:2609.12517v1 Announce Type: new Abstract: Long-Video Question Answering (LVQA) requires locating decisive evidence in hour-scale videos under a limited frame budget. Most training-free methods a

model-releasesarxiv-cs-cv

14 Sept 2026

OphBiWSSD: Scaling Temporal Action Localization in Ophthalmic Surgeries with Bidirectional Weight-tied State Space Duality

arXiv:2609.12409v1 Announce Type: new Abstract: High-frequency surgical maneuvers in ophthalmology necessitate high-fidelity temporal modeling, yet characterizing long-range procedural dependencies re

PACEvolve: Enabling Progress-Aware Consistent Evolution

arXiv:2601.10657v3 Announce Type: replace-cross Abstract: Self-evolving agents powered by Large Language Models (LLMs) have emerged as a promising direction across diverse domains, including code opti

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Partition-Invariant Tuning for 3D Scene Understanding

arXiv:2609.12473v1 Announce Type: new Abstract: Scene-level point cloud understanding remains challenging due to diverse geometries and spatial layouts. While pre-trained 3D point cloud foundation mod

PATH: Continuous Target Sensing among Autonomous Cooperative Drones

arXiv:2609.12456v1 Announce Type: new Abstract: Continuous target sensing by uncrewed aerial vehicles (UAVs) is constrained by limited flight endurance, motivating the transfer of tracking responsibil

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PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning

arXiv:2403.05864v3 Announce Type: replace Abstract: In human-centric Cyber-Physical Systems (CPS), personalized Deep Reinforcement Learning (DRL) agents must share fine-grained control actions with cl

Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators

arXiv:2609.11935v1 Announce Type: new Abstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDE

PhysioAI: Clinical Knowledge-Guided Semantic Supervision for Skeleton-Based Physiotherapy Action Recognition

arXiv:2609.12491v1 Announce Type: new Abstract: Skeleton-based action recognition can support automated tracking of physiotherapy exercises, particularly in remote rehabilitation settings where contin

PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability

arXiv:2609.12225v1 Announce Type: cross Abstract: Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing O

QTrans: A Quantum Transformer for Sentiment Classification

arXiv:2609.12011v1 Announce Type: new Abstract: In small-scale binary sentiment classification scenarios, factors such as negation, contrastive shifts, and cross-word dependencies lead to the non-line

Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent

arXiv:2609.13040v1 Announce Type: new Abstract: We study loss-based filtering for finite-sum optimization with a subset of corrupted component functions whose gradients may be highly unreliable. Motiv

R^2M: Real-Aware Residual Model Merging for Robust and Generalizable Deepfake Detection

arXiv:2509.24367v2 Announce Type: replace Abstract: Deepfake generators evolve rapidly, making exhaustive data collection and repeated retraining impractical. Unlike generic multi-task settings, deepf

READ: Learning Risk-Informed Fields for End-to-End Autonomous Driving

arXiv:2609.12371v1 Announce Type: new Abstract: Autonomous driving requires more than recognizing what is present in a scene: a planner must determine how road structure, surrounding agents, and their

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Local Ai

RefMods - A little easier to create, edit, and use with Fantastic Minimax H3 Promptbuilder

local-air-stablediffusion

Repo here- https://github.com/Adudeguyman/ComfyUI-Fantastic-MiniMaxH3-PromptBuilder Alrighty, back again with an update to my (manual, no LLM connected) Fantastic MiniMax H3 Prompt Builder. Yeah it's

Reinforcement Learning for Syndrome Extraction

arXiv:2609.12020v1 Announce Type: new Abstract: A key subtask of quantum error correction is to extract a syndrome that, if nontrivial, signals an error. The number of possible ways to extract a syndr

Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models

arXiv:2609.12277v1 Announce Type: new Abstract: Electronic health record (EHR) foundation models trained on longitudinal patient trajectories have demonstrated strong performance across diverse clinic

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Tutorials

Representation-based Masked Diffusion Model

arXiv:2609.12382v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) have emerged as a compelling paradigm for language modeling, offering the capability for efficient parallel text generati

tutorialsarxiv-cs-cl

Tutorials

SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling

arXiv:2609.12455v1 Announce Type: new Abstract: Automated machine learning (AutoML) is reshaping data-driven science and industrial practice, and as large language models are introduced into AutoML, p

tutorialsarxiv-cs-lg

Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models

arXiv:2609.12317v1 Announce Type: new Abstract: A central question in LLM reasoning is whether reinforcement learning (RL) instills genuinely new capabilities or merely reshapes how existing knowledge

Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning

model-releasesarxiv-cs-lg

arXiv:2502.07250v3 Announce Type: replace Abstract: Modern machine learning models excel at detecting individual actions, sounds, or scene attributes from short, localized observations. However, many

SCORE: SubDistribution-aware Collaborative Knowledge Reinforcing for Cloth-Hybrid Lifelong Person Re-Identification

arXiv:2609.12577v1 Announce Type: new Abstract: Lifelong Person Re-Identification (LReID) aims to train a unified person retrieval model from a non-stationary data stream. Existing LReID methods mainl

Semantically Aligned Gradient-Driven Context-Preserving Image Editing

arXiv:2609.12691v1 Announce Type: new Abstract: Instruction-guided image editing has a training-time blind spot. Generative editors are never required to semantically verify whether their outputs actu

Simulating Disengaged Students to Evaluate LLM-based Tutors

model-releasesarxiv-cs-lg

arXiv:2609.12331v1 Announce Type: new Abstract: Simulated students generated by computational models provide a practical way to evaluate tutoring strategies and pedagogical approaches used by human an

Single-Query Person-Centric Bimanual Hand-Object Interaction Detection

arXiv:2609.12155v1 Announce Type: new Abstract: Understanding person-level bi-manual interactions requires not only detecting hands, but also identifying which two hands belong to the same person and

Applications

SNAP3D: Physically Grounded 3D Parts for Assembly from a Single Image

arXiv:2609.13146v1 Announce Type: cross Abstract: Part-aware 3D asset generation enables applications such as editing, articulation, simulation, and fabrication, yet existing methods can generate visu

applicationsarxiv-cs-cv

Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes

arXiv:2609.12417v1 Announce Type: new Abstract: Multiview point cloud registration is particularly challenging in low-overlap scenes, where reliable correspondences are limited and incorrect pairwise

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks

model-releasesarxiv-cs-lg

arXiv:2501.04234v2 Announce Type: replace-cross Abstract: Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus

TestDG: Test-time Domain Generalization for Continual Test-time Adaptation

arXiv:2504.04981v3 Announce Type: replace Abstract: This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preser

TokenMapper: A Step Toward Interoperable Speech Token Translation

arXiv:2609.12563v1 Announce Type: new Abstract: Neural audio codecs discretize speech into token sequences, but the resulting token spaces differ in vocabulary and codebook structure, preventing direc

Towards Sustainable Hydrogen Systems: Supply Chain Optimization with Model Predictive Control and Reinforcement Learning

arXiv:2609.11933v1 Announce Type: cross Abstract: Hydrogen supply chains are expected to play a central role in future low-carbon energy systems by enabling renewable energy integration, long-duration

Understanding Whole-Body Robot Teleoperation Strategies Under Diverse Task Objectives and Constraints

arXiv:2609.12384v1 Announce Type: new Abstract: This work investigates the control strategies of complex whole-body robot teleoperation that coordinate active perception, bimanual manipulation, and na

researcharxiv-cs-ro

Applications

UniLayDiff: A Unified Diffusion Transformer for Content-Aware Layout Generation

arXiv:2512.08897v2 Announce Type: replace Abstract: Content-aware layout generation is a critical task in graphic design automation, focused on creating visually appealing arrangements of elements tha

applicationsarxiv-cs-cv

UniMo: Unifying Human and Animal Motion Generation

arXiv:2609.12342v1 Announce Type: new Abstract: The conditional generation of 3D motion has emerged as a key research topic due to its wide applicability across robotics, AR/VR, gaming, and content cr

USPLIT-VQA: U-Shaped Split Learning for Visual Question Answering with Contribution-Aware Weighted Aggregation

arXiv:2609.12168v1 Announce Type: new Abstract: Visual Question Answering (VQA) systems, jointly interpreting images and natural language queries, hold significant promise across many domains, yet the

VertiFuseX: Generalizable Financial Forecasting via Multi-Stream Temporal Fusion

arXiv:2609.12793v1 Announce Type: new Abstract: Stock price prediction remains challenging due to the non-stationary and noisy nature of financial time series. Existing deep learning models often rely

VS-Splat: Voxel-Selective feed-forward Gaussian Splatting for end-to-end 3D object reconstruction from sparse-views

arXiv:2609.12343v1 Announce Type: new Abstract: Feed-forward Gaussian splatting models have demonstrated remarkable effectiveness in reconstructing three-dimensional (3D) objects from a few two-dimens

What Did the MLLM Hear? Token-Level Spectro-Temporal Grounding for Audio MLLM Explainability

arXiv:2609.12663v1 Announce Type: cross Abstract: Audio-based Multimodal Large Language Models (MLLMs) can generate detailed natural-language descriptions of complex acoustic scenes, yet it remains un

What does it mean to pace the frontier? Over the last month, @random_walker and I have analyzed the loss-of-control incidents at AI companie…

What does it mean to pace the frontier? Over the last month, @random_walker and I have analyzed the loss-of-control incidents at AI companies to understand what technical and policy interventions can

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Industry

Dario Amodei says the 'toughest dilemma' about his proposal to 'pace the frontier' is what happens if China does not do the same (Ashley Capoot/CNBC)

Ashley Capoot / CNBC: Dario Amodei says the “toughest dilemma” about his proposal to “pace the frontier” is what happens if China does not do the same — Anthropic CEO Dario Amodei said Sunday that the

industrytechmeme

Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead. You guys are th…

Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead. You guys are the frontier. By any reasonable metric — market share, revenue

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Local Ai

internlm/Intern-S2 · Hugging Face

from internlm: We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre

local-air-localllama

Industry

Sources: Anthropic, OpenAI, and Google have held working group meetings since July to discuss creating an industry-led standards body for AI (Leo Schwartz/The Information)

Leo Schwartz / The Information: Sources: Anthropic, OpenAI, and Google have held working group meetings since July to discuss creating an industry-led standards body for AI — Anthropic, OpenAI and Goo

industrytechmeme

Agents

Agent-Integrated Software: Interaction Contracts and Continuous Assurance

arXiv:2609.11381v1 Announce Type: cross Abstract: Embedding an intelligent agent in an existing application creates a persistent coordination problem: users can revise goals and manipulate shared obje

agentsarxiv-cs-ai

Local Ai

Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning

arXiv:2609.11393v1 Announce Type: new Abstract: Test-time adaptation has emerged as a lightweight alternative to costly post-training for improving the reasoning capabilities of Large Language Models

local-aiarxiv-cs-ai

Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models

arXiv:2609.09957v1 Announce Type: cross Abstract: Modern large language models (LLMs) can translate natural-language descriptions into operations research (OR) formulations. Post-training techniques i

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Calibration-Aware Uncertainty Cascades for Efficient Heterogeneous Model Collaboration

arXiv:2609.11446v1 Announce Type: new Abstract: Heterogeneous model collaboration seeks to exploit the complementary strengths of different models to balance predictive performance and inference cost.

researcharxiv-cs-ai

Can LLMs Follow Medical Expert Logic? A Benchmark for Hierarchical Logical Consistency in Risk-of-Bias Assessment

model-releasesarxiv-cs-ai

arXiv:2609.11185v1 Announce Type: new Abstract: Evidence-based medicine demands strict logical consistency, yet current evaluations of large language models (LLMs) prioritize superficial label matchin