Search: “concepts”

Training and Benchmarking Code Generation for Physics-Inspired Animations

arXiv:2602.10840v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving. Howev

14 Aug 2026

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A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems

DGX agent

arXiv:2608.11221v1 Announce Type: new Abstract: Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The

applicationsarxiv-cs-ai

13 Aug 2026

Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification

arXiv:2608.12117v1 Announce Type: new Abstract: Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system. Thus, vascular age is a

Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations

arXiv:2608.12299v1 Announce Type: cross Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence. Its purpose is intui

local-aiarxiv-cs-ai

Credo: Declarative Control of LLM Pipelines via Beliefs and Policies

arXiv:2604.14401v2 Announce Type: replace Abstract: Agentic AI systems are becoming commonplace in domains that require long-lived, stateful decision-making in continuously evolving conditions. As suc

Designing Agentic AI-Based Screening for Portfolio Investment

arXiv:2603.23300v2 Announce Type: replace-cross Abstract: We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, t

Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving

arXiv:2608.12051v1 Announce Type: new Abstract: Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Covera

safetyarxiv-cs-cv

Dynamics Models for Offline Hyperparameter Selection in Real-World RL

arXiv:2608.11349v1 Announce Type: cross Abstract: A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and

applicationsarxiv-cs-ai

Tutorials

Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads

arXiv:2608.11661v1 Announce Type: cross Abstract: A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings. This archit

tutorialsarxiv-cs-ai

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

arXiv:2608.11408v1 Announce Type: new Abstract: Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no lon

researcharxiv-cs-cl

Multi-Agent Target-Existence Verification and Learned Mask Geometry Refinement: Winning Report of the MeViS-Text Track at the 8th LSVOS Challenge 2026

arXiv:2608.11458v1 Announce Type: new Abstract: We present the first-place solution to the MeViS-Text track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge 2026: referring video obj

agentsarxiv-cs-cv

RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers

model-releasesarxiv-cs-lg

arXiv:2608.11572v1 Announce Type: new Abstract: Coarse-grid numerical solvers can substantially reduce the computational cost of time-dependent PDE simulation, but under-resolution often degrades both

Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training

arXiv:2604.07037v2 Announce Type: replace-cross Abstract: Accelerator-based neutrino physics is entering an energy-frontier regime in which interactions reach the TeV scale and produce exceptionally d

researcharxiv-cs-cv

Beyond a Bag of Features: Set-Level Instability in Sparse Autoencoders

arXiv:2608.11197v1 Announce Type: cross Abstract: Shani et al. (2026) show that LLM representations broadly recover human category boundaries, while failing to reflect fine-grained typicality structur

researcharxiv-cs-cl

Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding

arXiv:2603.25251v2 Announce Type: replace-cross Abstract: Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, sometimes termed faithfulness or fidelity, which est

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

Hierarchical Compositionality for An Assistive AI Agent

arXiv:2608.10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are

HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models

arXiv:2506.03922v4 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchma

Inferential Capability Does Not Determine Legal Scope

arXiv:2608.10601v1 Announce Type: cross Abstract: Two instruments of EU digital law place inference at their centre and mean different things by it. Article 3(1) of the AI Act uses the capability to i

Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework

arXiv:2510.07749v2 Announce Type: replace Abstract: Perception failures in autonomous vehicles (AV) remain a major safety concern because they are the basis for many accidents. To study how these fail

safetyarxiv-cs-ro

Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

arXiv:2608.09992v1 Announce Type: cross Abstract: Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of s

On Solomonoff Induction in Large Language Models and the Limits of Self-Improving: The Singularity Is Not Near Without Symbolic Model Synthesis

arXiv:2601.05280v3 Announce Type: replace-cross Abstract: On the one hand, the question of whether large language models (LLMs) are Solomonoff induction estimators has become an explicit question at t

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information

arXiv:2608.10766v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision. We propose ''Rul

The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM

arXiv:2505.11635v2 Announce Type: cross Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous laten

The Illusion of Cross-Lingual Safety in Low-Resource Languages

arXiv:2608.11146v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings. How

local-aiarxiv-cs-cl

TRACE: Trustworthy Retrieval-Augmented Conversational Engine

arXiv:2608.10176v1 Announce Type: new Abstract: Public service chatbots are expected to deliver recommendations from an underlying public service directory, while also making sure that the recommendat

V-FiLLM: Verified Financial LLM Reasoning Benchmark

arXiv:2608.11047v1 Announce Type: new Abstract: While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains compar

Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy

arXiv:2604.07557v2 Announce Type: replace Abstract: Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slo

Agentic Stage-One Stellarator Optimization: Autonomous Multi-Objective Search for Finite-Beta Equilibria

arXiv:2608.01344v2 Announce Type: replace Abstract: Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configu

local-aiarxiv-cs-ai

Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization

arXiv:2504.08811v3 Announce Type: replace Abstract: Modern learning systems often struggle with joint learning across diverse scenarios and immediate adaptation to new ones, because they rely heavily

applicationsarxiv-cs-lg

Approximation Rates for Metaplectic Neural Networks

arXiv:2608.08872v1 Announce Type: new Abstract: In this paper we develop quantitative approximation results for shallow neural networks constructed using a dictionary based on metaplectic operators. F

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

arXiv:2608.09888v1 Announce Type: cross Abstract: We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuou

Beyond Hazard Resemblance: Contrastive Event Adjudication for Training-Free Video Anomaly Detection

arXiv:2608.09908v1 Announce Type: new Abstract: Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries f

local-aiarxiv-cs-cv

DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing

arXiv:2608.08381v1 Announce Type: new Abstract: Motivated by the IEEE 802.11bf effort to standardize advanced WLAN sensing, interest in Wi-Fi Channel State Information (CSI) for passive, device-free,

applicationsarxiv-cs-cv

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

safetyarxiv-cs-cv

Enhanced Real-Time 6-DOF Extended Reality Catheter Tracking for Evaluating Potential Improvement in Efficiency, Precision, and Depth Perception for Cardiac Interventions

arXiv:2608.07606v1 Announce Type: cross Abstract: Despite advances in 3D ultrasound, most percutaneous cardiac interventions still rely on 2D visualization, limiting depth perception and spatial under

Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings

arXiv:2508.17092v2 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content. Many KT m

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

arXiv:2603.26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space,

Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios

arXiv:2608.08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different dom

applicationsarxiv-cs-ai

Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective

arXiv:2608.08445v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to gr

GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

arXiv:2608.07881v1 Announce Type: new Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discret

I gave DeepSeek V4 Flash basic vision by training a 40M connector on 100K examples

model-releasesr-localllama

I wanted to find out whether a huge text-only MoE could be given basic vision without retraining the language model itself. The short answer is yes. I froze DeepSeek V4 Flash and a 417M-parameter Moon

Is CLIP Cross-Eyed? Revealing and Mitigating Center Bias in the CLIP Family

arXiv:2604.05971v2 Announce Type: replace-cross Abstract: Recent research has shown that contrastive vision-language models such as CLIP often lack fine-grained understanding of visual content. While

safetyarxiv-cs-cl

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

arXiv:2608.07733v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems

Lying mirror using structured surfaces

arXiv:2410.15521v2 Announce Type: replace-cross Abstract: We introduce an all-optical system, termed the 'lying mirror', to hide input information by transforming it into misleading, ordinary-looking

researcharxiv-cs-cv

Machine Learning and Data Analysis Using Posets: A Survey

arXiv:2404.03082v3 Announce Type: replace Abstract: Partially ordered sets (posets) are discrete mathematical structures that formalize the notion of comparison without forcing every pair of objects t

safetyarxiv-cs-lg

MiniMax-H3: ~38 GB less VRAM with Runtime LoRA Bypass — DoRA Dynamic LoRA Loader v1.0.39

local-air-stablediffusion

GitHub: https://github.com/xmarre/ComfyUI-DoRA-Dynamic-LoRA-Loader Release v1.0.39: https://github.com/xmarre/ComfyUI-DoRA-Dynamic-LoRA-Loader/releases/tag/v1.0.39 Also available through ComfyUI Manag

Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interaction

arXiv:2608.08443v1 Announce Type: cross Abstract: Previous studies have shown that people can develop shared symbols, partner-specific expressions, personal idioms, inside jokes, and other parts of a

local-aiarxiv-cs-ai