Search: “framework”

Adaptive multi-fidelity optimization with fast learning rates

arXiv:2604.16239v1 Announce Type: cross Abstract: In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimiz

safetyarxiv-cs-lg

Human

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AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows

DGX agent

arXiv:2602.00052v2 Announce Type: replace-cross Abstract: Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial team

applicationsarxiv-cs-ai

20 Apr 2026

AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection

arXiv:2604.16207v1 Announce Type: cross Abstract: As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods t

Applied Explainability for Large Language Models: A Comparative Study

arXiv:2604.15371v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across many natural language processing tasks, yet their decision processes remain difficult t

AutoDrive-R^2: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving

arXiv:2509.01944v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimoda

safetyarxiv-cs-cv

Hardware

AutoFlows++: Hierarchical Message Flow Mining for System on Chip Designs

arXiv:2604.15359v1 Announce Type: cross Abstract: Understanding communication behavior in modern system-on-chip (SoC) designs is critical for functional verification, performance analysis, and post-si

hardwarearxiv-cs-lg

Automatic Combination of Sample Selection Strategies for Few-Shot Learning

arXiv:2402.03038v2 Announce Type: replace-cross Abstract: In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection stra

Beyond Passive Viewing: A Pilot Study of a Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

arXiv:2604.15334v1 Announce Type: cross Abstract: The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critica

Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials

model-releasesarxiv-cs-lg

arXiv:2604.15821v1 Announce Type: cross Abstract: Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic mol

Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations

arXiv:2602.05523v2 Announce Type: replace-cross Abstract: Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing p

Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs

arXiv:2604.16060v1 Announce Type: cross Abstract: Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. Howe

Closing the Theory-Practice Gap in Spiking Transformers via Effective Dimension

arXiv:2604.15769v1 Announce Type: cross Abstract: Spiking transformers achieve competitive accuracy with conventional transformers while offering 38-57imes energy efficiency on neuromorphic hardware,

ConFu: Contemplate the Future for Better Speculative Sampling

arXiv:2603.08899v2 Announce Type: replace Abstract: Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to

Local Ai

Continual Hand-Eye Calibration for Open-world Robotic Manipulation

arXiv:2604.15814v1 Announce Type: new Abstract: Hand-eye calibration through visual localization is a critical capability for robotic manipulation in open-world environments. However, most deep learni

local-aiarxiv-cs-cv

DALM: A Domain-Algebraic Language Model via Three-Phase Structured Generation

arXiv:2604.15593v1 Announce Type: cross Abstract: Large language models compress heterogeneous knowledge into a single parameter space, allowing facts from different domains to interfere during genera

Differential privacy representation geometry for medical image analysis

arXiv:2603.01098v2 Announce Type: replace-cross Abstract: Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of pri

Tutorials

Discovering quantum phenomena with Interpretable Machine Learning

arXiv:2604.16015v1 Announce Type: cross Abstract: Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent

tutorialsarxiv-cs-lg

Disentangling Mathematical Reasoning in LLMs: A Methodological Investigation of Internal Mechanisms

arXiv:2604.15842v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain unde

researcharxiv-cs-cl

Explainable Iterative Data Visualisation Refinement via an LLM Agent

arXiv:2604.15319v1 Announce Type: cross Abstract: Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space (typically 2D or 3D), based on which visualiza

Exploring LLM-based Verilog Code Generation with Data-Efficient Fine-Tuning and Testbench Automation

arXiv:2604.15388v1 Announce Type: cross Abstract: Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, tr

From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation

arXiv:2604.15805v1 Announce Type: cross Abstract: Learning robust robot policies in real-world environments requires diverse data augmentation, yet scaling real-world data collection is costly due to

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents

arXiv:2602.01566v2 Announce Type: replace Abstract: Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research o

Getting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that sol…

Getting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that solves this by having the model generate and manipulate a rando

researchdavid-ha--x

GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows

arXiv:2604.15715v1 Announce Type: cross Abstract: The development of general-purpose agents requires a shift from executing simple instructions to completing complex, real-world productivity workflows

Heterogeneous Sheaf Neural Networks

model-releasesarxiv-cs-lg

arXiv:2409.08036v2 Announce Type: replace Abstract: Heterogeneous graphs, whose nodes and edges may belong to different types and feature spaces, arise in a wide variety of real-world domains such as

Impact of Nonlinear Power Amplifier on Massive MIMO: Machine Learning Prediction Under Realistic Radio Channel

arXiv:2604.15977v1 Announce Type: new Abstract: M-MIMO is one of the crucial technologies for increasing spectral and energy efficiency of wireless networks. Most of the current works assume that M-MI

researcharxiv-cs-lg

Just Type It in Isabelle! AI Agents Drafting, Mechanizing, and Generalizing from Human Hints

arXiv:2604.15713v1 Announce Type: cross Abstract: Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of c

Large Language Models for Market Research: A Data-augmentation Approach

arXiv:2412.19363v3 Announce Type: replace Abstract: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to ge

Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners

arXiv:2601.02996v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit

researcharxiv-cs-cl

Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts

arXiv:2604.15581v1 Announce Type: cross Abstract: Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key compone

researcharxiv-cs-lg

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks

arXiv:2604.15392v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differe

Long-Term Memory for VLA-based Agents in Open-World Task Execution

arXiv:2604.15671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential for embodied decision-making; however, their application in complex chemical

safetyarxiv-cs-ro

Majority Voting for Code Generation

arXiv:2604.15618v1 Announce Type: new Abstract: We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies

researcharxiv-cs-lg

Tutorials

Make your next Replit purchase more rewarding with Amex Offers®. Eligible American Express® Card Members can enroll and earn a $50 statement…

Make your next Replit purchase more rewarding with Amex Offers®. Eligible American Express® Card Members can enroll and earn a 50 statement credit after they enroll and spend a minimum of 100 in one o

tutorialsreplit--x

Meet Kimi K2.6 agent - Video hero section, WebGL shaders, real backends. From one prompt. 🔹 Video hero sections - cinematic aesthetic, auto…

Meet Kimi K2.6 agent - Video hero section, WebGL shaders, real backends. From one prompt. 🔹 Video hero sections - cinematic aesthetic, auto-composited 🔹 WebGL shader animations - native GLSL / WGSL, l

agentskimi-moonshot--x

MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents

arXiv:2604.15774v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks. Speci

Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation

arXiv:2512.03053v2 Announce Type: replace-cross Abstract: We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain

No Universal Courtesy: A Cross-Linguistic, Multi-Model Study of Politeness Effects on LLMs Using the PLUM Corpus

arXiv:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T

One-Shot Cross-Geometry Skill Transfer through Part Decomposition

arXiv:2604.15455v1 Announce Type: new Abstract: Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to

safetyarxiv-cs-ro

Phase Transitions as the Breakdown of Statistical Indistinguishability

arXiv:2604.15773v1 Announce Type: cross Abstract: We introduce a novel characterization of phase transitions based on hypothesis testing. In our formulation, a phase transition is defined as the break

{pi}_{0.7}: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

arXiv:2604.15483v1 Announce Type: new Abstract: We present a new robotic foundation model, called {pi}_{0.7}, that can enable strong out-of-the-box performance in a wide range of scenarios. {pi}_{0.7}

agentsarxiv-cs-lg

ProtoTTA: Prototype-Guided Test-Time Adaptation

arXiv:2604.15494v1 Announce Type: cross Abstract: Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balan

applicationsarxiv-cs-cv

Puppets or partners? Governing cyborg propaganda in the digital public square

arXiv:2602.13088v2 Announce Type: replace-cross Abstract: The distinction between genuine grassroots activism and automated influence operations is collapsing. While contemporary policy debates priori

Tutorials

Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift

arXiv:2604.15838v1 Announce Type: new Abstract: Distribution shift severely degrades the performance of deep forecasting models. While this issue is well-studied for individual time series, it remains

tutorialsarxiv-cs-lg

Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection

arXiv:2604.15856v1 Announce Type: cross Abstract: Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unav

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo

safetyarxiv-cs-lg

SecureRouter: Encrypted Routing for Efficient Secure Inference

arXiv:2604.15499v1 Announce Type: cross Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabli

Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

arXiv:2602.11327v2 Announce Type: replace-cross Abstract: The rapid development of the AI agent communication protocols, including the Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent