Search: “framework”

Improved Mean Flows: On the Challenges of Fastforward Generative Models

arXiv:2512.02012v2 Announce Type: replace Abstract: MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key chal

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Integrated Hierarchical Decision-Making in Inverse Kinematic Planning and Control

DGX agent

arXiv:2412.01324v4 Announce Type: replace Abstract: This work presents a novel and efficient nonlinear programming framework that tightly integrates hierarchical decision-making with whole-body invers

researcharxiv-cs-ro

12 May 2026

Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions

arXiv:2605.09870v1 Announce Type: cross Abstract: We propose SVAR-FM (Structural VAR with Flow Matching), a framework for time series causal discovery that treats a physics-based simulator as a mechan

Kernel-Gradient Drifting Models

arXiv:2605.10727v1 Announce Type: new Abstract: We propose kernel-gradient drifting, a one-step generative modeling framework that replaces the fixed Euclidean displacement direction in drifting model

MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving

arXiv:2605.10026v1 Announce Type: new Abstract: With the advancement of autonomous driving, numerous annotated multi-modality datasets have become available. This presents an opportunity to develop do

agentsarxiv-cs-cv

Narrative Landscape: Mapping Narrative Dispositions Across LLMs

arXiv:2605.08742v1 Announce Type: cross Abstract: This study proposes a quantitative framework for profiling LLM dispositions as stable, model-specific regularities in output under repeated, controlle

New AI-Driven Tools for Enhancing Campus Well-being: A Prevention and Intervention Approach

arXiv:2605.10804v1 Announce Type: new Abstract: Campus well-being underpins academic success, yet many universities lack effective methods for monitoring satisfaction and detecting mental health risks

local-aiarxiv-cs-ai

Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials

arXiv:2605.09058v1 Announce Type: cross Abstract: We introduce Nonlinear GENERIC Informed Neural Networks (N-GINNs), a deep learning framework for discovering evolution equations of systems governed b

Online Sharp-Calibrated Bayesian Optimization

arXiv:2605.10572v1 Announce Type: new Abstract: Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions, commonly based on Gaussian process (GP) surrogate mo

Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection

arXiv:2605.10040v1 Announce Type: new Abstract: The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalating the risks of identity fraud and the d

Position: AI Security Policy Should Target Systems, Not Models

arXiv:2605.09504v1 Announce Type: cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, paral

Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead

arXiv:2507.23009v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable results on a range of standardized tests originally designed to assess human cognitive a

PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis

arXiv:2605.08585v1 Announce Type: cross Abstract: Deep learning models in medical imaging typically operate as parametric memory, diagnosing patients by recalling fixed knowledge learned during traini

Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits

arXiv:2510.10730v2 Announce Type: replace-cross Abstract: We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for t

Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR

arXiv:2605.10781v1 Announce Type: cross Abstract: Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student withou

researcharxiv-cs-cl

Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning

arXiv:2605.09212v1 Announce Type: new Abstract: Centralized training with decentralized execution (CTDE) is a standard framework for cooperative multi-agent policy-gradient reinforcement learning, all

safetyarxiv-cs-lg

Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding

arXiv:2508.07722v2 Announce Type: replace Abstract: Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneou

agentsarxiv-cs-lg

SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

arXiv:2605.08334v1 Announce Type: new Abstract: We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-drive

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Scalable Gaussian process inference via neural feature maps

model-releasesarxiv-cs-lg

arXiv:2605.10285v1 Announce Type: cross Abstract: We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the le

SeasonScapes: Learning Large-scale Re-lightable 3D Landscapes with Seasonal Variation from Sparse Webcams

arXiv:2605.09039v1 Announce Type: new Abstract: We introduce SeasonScapes framework and a the SeasonScapes dataset: Swiss Sparse-view Mountain Scenes with Seasonal Changes that covers over 50 km x 60

Sliced Inner Product Gromov-Wasserstein Distances

arXiv:2605.08546v1 Announce Type: cross Abstract: The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical

Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents

arXiv:2605.08580v1 Announce Type: cross Abstract: To cope with the large contexts that long-horizon LLM agents produce, modern frameworks increasingly rely on compaction -- invoking an LLM to rewrite

agentsarxiv-cs-ai

STRIVE: Structured Spatiotemporal Exploration for Reinforcement Learning in Video Question Answering

arXiv:2604.01824v2 Announce Type: replace Abstract: We introduce STRIVE (SpatioTemporal Reinforcement with Importance-aware Variant Exploration), a structured reinforcement learning framework for vide

safetyarxiv-cs-cv

Switching-Geometry Analysis of Deflated Q-Value Iteration

arXiv:2605.10811v1 Announce Type: cross Abstract: This paper develops a joint spectral radius (JSR) framework for analyzing rank-one deflated Q-value iteration (Q-VI) in discounted Markov decision pro

safetyarxiv-cs-ai

Thanks to everyone who showcased their projects in today's Hermes Agent Jam!

Nous Research held a 'Hermes Agent Jam' event where developers showcased projects built using or related to their Hermes agent framework. The event appears to have been a community-driven showcase hig

agentsnous-research--x

Tutorials

The finite expression method for turbulent dynamics with high-order moment recovery

arXiv:2605.10687v1 Announce Type: new Abstract: Turbulent dynamical systems are characterized by nonlinear interactions and stochastic effects that generate coupled statistical quantities, such as non

tutorialsarxiv-cs-lg

The Observable Wasserstein Distance

arXiv:2605.09916v1 Announce Type: cross Abstract: We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Po

Toward General and Robust LLM-enhanced Text-attributed Graph Learning

arXiv:2504.02343v2 Announce Type: replace Abstract: Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LL

Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective

arXiv:2602.17283v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are employed worldwide, existing evaluation paradigms for their multilingual capabilities primarily focus on f

Towards Effective Theory of LLMs: A Representation Learning Approach

arXiv:2605.09294v1 Announce Type: cross Abstract: We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather

Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo

model-releasesarxiv-cs-lg

arXiv:2605.09125v1 Announce Type: cross Abstract: Preliminary low-thrust spacecraft mission design is a global search problem characterized by a complex solution landscape, multiple objectives, and nu

What’s new in Microsoft Foundry | April 2026

model-releasesmicrosoft-foundry

April brings Foundry Local GA for local AI development, GPT-5.5 model support with Tier 5 and Tier 6 default quota in Microsoft Foundry, new tracing paths for Microsoft Agent Framework and hosted agen

6D Pose Estimation via Keypoint Heatmap Regression with RGB-D Residual Neural Networks

arXiv:2605.08059v1 Announce Type: new Abstract: In this paper, we propose a modular framework for 6D pose estimation based on keypoint heatmap regression. Our approach combines YOLOv10m for object det

A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method

arXiv:2602.02320v3 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential fo

safetyarxiv-cs-ai

Actor-Critic with Active Importance Sampling

arXiv:2605.07094v1 Announce Type: new Abstract: This paper introduces the Active-Importance-Sampling Actor-Critic (AISAC) algorithm, an extension of the Actor-Critic framework for reducing variance in

safetyarxiv-cs-lg

ADKO: Agentic Decentralized Knowledge Optimization

arXiv:2605.07863v1 Announce Type: new Abstract: We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achie

local-aiarxiv-cs-lg

Alternating Target-Path Planning for Scalable Multi-Agent Coordination

arXiv:2605.07744v1 Announce Type: new Abstract: The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets a

agentsarxiv-cs-ai

Bayesian Conformal Prediction as a Decision Risk Problem

arXiv:2602.03331v2 Announce Type: replace Abstract: We propose Bayesian Conformal Prediction (BCP), a framework that combines Bayesian posterior predictive distributions with PAC-style conformal risk

Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors

arXiv:2605.07993v1 Announce Type: new Abstract: Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis hel

Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings

arXiv:2605.06941v1 Announce Type: new Abstract: We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained t

CGF-Softmax: A Cumulant-Based Softmax Reformulation for Efficient Inference under Homomorphic Encryption

arXiv:2602.01621v3 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. H

Crushed the 4M weekly download mark last week for „@langchain/core“ 💪 let’s go!! 🚀 @LangChain_JS @huntlovell @colifran_

LangChain's JavaScript core package (@langchain/core) reached 4 million weekly downloads, marking a significant milestone for the JavaScript ecosystem of the LangChain framework. This achievement refl

agentsharrison-chase--x

Deeply Dual Supervised learning for melanoma recognition

model-releasesarxiv-cs-cv

arXiv:2508.01994v2 Announce Type: replace Abstract: As the application of deep learning in dermatology continues to grow, the recognition of melanoma has garnered significant attention, demonstrating

Distributional simplicity bias and effective convexity in Energy Based Models

arXiv:2605.07844v1 Announce Type: new Abstract: Energy-based learning is a powerful framework for generative modelling, but its training is inherently non-convex, leading potentially to sensitivity to

local-aiarxiv-cs-lg

Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization

arXiv:2605.07041v1 Announce Type: cross Abstract: This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images.

safetyarxiv-cs-cv

Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency

arXiv:2605.06280v2 Announce Type: replace Abstract: Recent advancements in image animation have utilized diffusion models to breathe life into static images. However, existing controllable frameworks

local-aiarxiv-cs-cv

Evaluating Large Language Models in Scientific Discovery

arXiv:2512.15567v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions

arXiv:2512.20974v3 Announce Type: replace-cross Abstract: Bayesian Reinforcement Learning (BRL), a subclass of Meta-Reinforcement Learning (Meta-RL), provides a principled framework for generalisation

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