Search: “framework”

Memory by Design: Probabilistic Sequence Layers

arXiv:2605.31163v1 Announce Type: cross Abstract: We introduce the design-model framework: a way to derive efficient recurrent sequence maps from explicit assumptions about memory. A design model writ

1 Jun 2026

Rationalize: Shared Semantic Reasoning for Human-AI Alignment

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DGX agent

arXiv:2605.30632v1 Announce Type: cross Abstract: We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ide

safetyarxiv-cs-ai

1 Jun 2026

Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks

arXiv:2508.11911v2 Announce Type: replace-cross Abstract: We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies laten

1 Jun 2026

Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates

arXiv:2605.30722v1 Announce Type: new Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalisin

1 Jun 2026

Active Learning for Machine Learning Driven Molecular Dynamics

arXiv:2509.17208v3 Announce Type: replace Abstract: Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and

Certified Causal Defense with Generalizable Robustness

arXiv:2408.15451v3 Announce Type: replace Abstract: While machine learning models have proven effective across various scenarios, it is widely acknowledged that many models are vulnerable to adversari

Conf-Gen: Conformal Uncertainty Quantification for Generative Models

arXiv:2605.28920v1 Announce Type: cross Abstract: Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machin

Eulerian Gaussian Splatting using Hashed Probability Pyramids

arXiv:2605.29136v1 Announce Type: new Abstract: We introduce a probabilistic splat-based radiance field framework that retains the fast rasterization and test-time efficiency of 3D Gaussian Splatting

researcharxiv-cs-cv

FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views

arXiv:2605.29997v1 Announce Type: new Abstract: We present FRUC, a feed-forward 3D Gaussian splatting framework for dynamic scene reconstruction from uncalibrated collaborative driving views. Existing

agentsarxiv-cs-cv

Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection

arXiv:2605.30042v1 Announce Type: new Abstract: Automating scientific computing workflows requires more than generating executable code: autonomous systems must also select appropriate computational s

Recurrent Structural Policy Gradient for Partially Observable Mean Field Games

arXiv:2602.20141v2 Announce Type: replace Abstract: Mean Field Games (MFGs) provide a principled framework for modelling interactions in large population systems. However, algorithmic progress has bee

Spectral Guidance for Flexible and Efficient Control of Diffusion Models

arXiv:2605.28900v1 Announce Type: new Abstract: We introduce Spectral Guidance, a framework for controlling diffusion models by leveraging the intrinsic geometry of the generative process. As data is

Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field

arXiv:2605.30342v1 Announce Type: new Abstract: We present Gaussian Splatting Anisotropic Visibility Field (GAVIS), a novel framework for uncertainty quantification and active mapping in 3DGS. Our key

researcharxiv-cs-cv

Tutorials

Zero-shot CT Super-Resolution using Diffusion-based 2D Projection Priors and Signed 3D Gaussians

arXiv:2508.15151v3 Announce Type: replace-cross Abstract: Computed tomography (CT) is important in clinical diagnosis, but acquiring high-resolution (HR) CT is constrained by radiation exposure risks.

tutorialsarxiv-cs-cv

A Policy-Driven Runtime Layer for Agentic LLM Serving

arXiv:2605.27744v1 Announce Type: new Abstract: Multi-agent LLM systems have become the dominant production workload, but the serving stack was not built for them. The agent framework above knows agen

Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting

arXiv:2605.28145v1 Announce Type: new Abstract: We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark, which evaluates machine learning models across twelve disti

Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction

arXiv:2605.27706v1 Announce Type: new Abstract: We introduce CAROL (Chain-based Adaptive Reconfiguration Over Lattices), a probabilistic framework for test-time hallucination reduction in large langua

agentsarxiv-cs-cl

CodeGENCAT: Generative Computerized Adaptive Testing for Open-ended Coding Problems

arXiv:2602.20020v2 Announce Type: replace Abstract: Existing Computerized Adaptive Testing (CAT) frameworks typically select questions based on the predicted likelihood that the student will answer co

safetyarxiv-cs-cl

Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model

arXiv:2605.27651v1 Announce Type: new Abstract: Autonomous space systems operating in extreme thermal environments require accurate and efficient thermal modeling to support both pre-mission system de

agentsarxiv-cs-lg

HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation

arXiv:2603.14773v2 Announce Type: replace-cross Abstract: Fine-tuning large models on edge devices is severely hindered by the memory-intensive backpropagation (BP) in standard frameworks like federat

Opus 4.8 is now supported in Hermes Agent ^_^

Nous Research has announced support for Opus 4.8 in the Hermes Agent framework. This update enables the Hermes Agent to utilize Anthropic's Opus 4.8 model, expanding the available model options for us

agentsnous-research--x

Parameter-Efficient Generative Modeling with Controlled Vector Fields

arXiv:2605.28267v1 Announce Type: new Abstract: We introduce a continuous-time generative modeling framework, motivated by the Chow-Rashevskii theorem, that builds expressive flows from a small set of

SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints

arXiv:2605.28549v1 Announce Type: cross Abstract: The pursuit of humanoid athletic sprints is hindered by a scarcity of humanoid-viable kinematic reference data and the inability of existing framework

safetyarxiv-cs-lg

Verified Misguidance: Measuring Structural Citation Failures in Search-Augmented LLMs

arXiv:2605.28565v1 Announce Type: cross Abstract: Users of search-augmented LLMs rely on citations as evidence that responses are grounded in real sources, and rarely verify the cited pages themselves

Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference

arXiv:2605.26552v1 Announce Type: cross Abstract: Aligning a few-step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likeli

CompassDPO: Dynamics-Controlled Direct Preference Optimization for Robust Safety Alignment

arXiv:2603.07211v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) has become a standard framework for safety alignment, but its reliance on pairwise preference updates makes tra

safetyarxiv-cs-lg

Tutorials

Corrected Samplers for Discrete Flow Models

arXiv:2601.22519v2 Announce Type: replace-cross Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alter

tutorialsarxiv-cs-lg

DEI: Diversity in Evolutionary Inference for Quality-Diversity Search

arXiv:2605.27130v1 Announce Type: cross Abstract: We present DEI: Diversity in Evolutionary Inference, a distributed Quality-Diversity (QD) search framework that assigns heterogeneous large language m

Error Analysis of Discrete Flow with Generator Matching

arXiv:2509.21906v3 Announce Type: replace-cross Abstract: Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performan

Local Ai

Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography

arXiv:2511.06625v5 Announce Type: replace-cross Abstract: Low-dose chest computed tomography (LDCT) captures pulmonary and cardiac structures in a single scan, enabling joint assessment of lung and ca

local-aiarxiv-cs-ai

Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions

arXiv:2408.15787v2 Announce Type: replace Abstract: Creating effective dialogue systems for mental health support requires high-quality multi-turn counseling dialogue data, yet collecting real counsel

agentsarxiv-cs-cl

Normalizing Flows on Quotient Manifolds via Boundary Quotients

arXiv:2511.22882v3 Announce Type: replace Abstract: We introduce boundary quotients and present a framework for learning densities on manifolds that arise as boundary quotients of simpler domains. We

Plans for Evaluating Structured Generative Search Summaries

arXiv:2605.26400v1 Announce Type: cross Abstract: We propose a framework for evaluating structured generative search summaries that are placed atop organic web search results. A structured summary, ge

Qiskit QuantumKatas: Adapting Microsoft's Quantum Computing exercises for LLM evaluation

arXiv:2605.27210v1 Announce Type: cross Abstract: We adapt Microsoft's QuantumKatas -- a well-established quantum computing curriculum -- from Q# to Qiskit, the most widely-adopted quantum computing f

The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance

arXiv:2601.07085v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding mi

Architecting Agentic Communities using Design Patterns

arXiv:2601.03624v3 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLM) and subsequent Agentic AI technologies requires systematic architectural guidance for building so

agentsarxiv-cs-ai

Clustering based on Stochastic Dominance with application for risk averters and risk seekers

arXiv:2605.24422v1 Announce Type: cross Abstract: Stochastic Dominance (SD) theory provides a rigorous framework for selecting superior assets tailored to the asset allocation needs of investors with

Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models

arXiv:2605.25991v1 Announce Type: new Abstract: We introduce Fuzzy PyTorch, a framework for rapid evaluation of numerical variability in deep learning (DL) models. As DL is increasingly applied to div

Local Ai

G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation

arXiv:2605.25646v1 Announce Type: new Abstract: Autonomous ground robots operating in large-scale outdoor environments require both robust long-range navigation and fine-grained ''last-mile'' explorat

local-aiarxiv-cs-ro

Geometric Workspace Analysis and Transmission-Aware Dynamics of a Serial Spherical Tool for Microsurgery

arXiv:2605.24760v1 Announce Type: new Abstract: We present a kinematic and transmission-aware design framework for a serial spherical mechanism with an additional translational degree of freedom for m

researcharxiv-cs-ro

High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications

arXiv:2605.24763v1 Announce Type: new Abstract: This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a f

Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization

arXiv:2605.24502v1 Announce Type: cross Abstract: We introduce a physics-inspired continuous relaxation framework that yields substantially improved solutions for NP-hard combinatorial optimization pr

Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol

arXiv:2605.24538v1 Announce Type: cross Abstract: Every major framework for governing artificial intelligence presupposes an identifiable entity -- a developer, deployer, or operator -- who can be hel

Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning

arXiv:2605.25740v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) provides a practical framework for obtaining goal-reaching policies from fixed datasets. However,

safetyarxiv-cs-lg

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology

arXiv:2605.24608v1 Announce Type: new Abstract: We develop a rigorous algebraic framework for deep convolutional architectures, CNNs, ResNets, and encoder--decoder networks such as UNet, grounded in l

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

arXiv:2508.15760v2 Announce Type: replace-cross Abstract: Tool calling has emerged as a critical capability for AI agents. In contrast to conventional tool calling frameworks that rely on static, prov

Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems

arXiv:2605.25233v1 Announce Type: new Abstract: AI agents are increasingly used to solve complex, multi-step tasks, but existing multi-agent frameworks remain brittle as workflows grow in scale and de

agentsarxiv-cs-ai

NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning

arXiv:2603.06798v2 Announce Type: replace Abstract: The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prio