Search: “papers”

arXiv:2604.15390v1 Announce Type: cross Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires

tutorialsarxiv-cs-ai

Applied Explainability for Large Language Models: A Comparative Study

DGX agent

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

applicationsarxiv-cs-ai

20 Apr 2026

ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence

arXiv:2603.24621v2 Announce Type: replace Abstract: We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn-based environments in which agents

ASMR-Bench: Auditing for Sabotage in ML Research

arXiv:2604.16286v1 Announce Type: new Abstract: As AI systems are increasingly used to conduct research autonomously, misaligned systems could introduce subtle flaws that produce misleading results wh

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

arXiv:2512.24625v2 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle flee

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

C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment

arXiv:2604.15675v1 Announce Type: new Abstract: Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initi

safetyarxiv-cs-cl

Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Models

arXiv:2604.16258v1 Announce Type: new Abstract: Competency Questions (CQs) are a cornerstone of requirement elicitation in ontology engineering. CQs represent requirements as a set of natural language

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,

Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction

arXiv:2603.21735v2 Announce Type: replace-cross Abstract: The proliferation of Generative Artificial Intelligence has transformed benign cognitive offloading into a systemic risk of cognitive agency s

safetyarxiv-cs-ai

Collaborative Filtering Through Weighted Similarities of User and Item Embeddings

arXiv:2604.15573v1 Announce Type: cross Abstract: In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art perfor

researcharxiv-cs-lg

Cost-Aware Model Orchestration for LLM-based Systems

arXiv:2512.01099v2 Announce Type: replace Abstract: As modern artificial intelligence (AI) systems become more advanced and capable, they can leverage a wide range of tools and models to perform compl

COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments

arXiv:2510.03875v2 Announce Type: replace Abstract: The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Se

researcharxiv-cs-ro

CSLE: A Reinforcement Learning Platform for Autonomous Security Management

arXiv:2604.15590v1 Announce Type: cross Abstract: Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement lea

CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification

model-releasesarxiv-cs-cv

arXiv:2604.15555v1 Announce Type: new Abstract: Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing

DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability

arXiv:2510.20299v3 Announce Type: replace-cross Abstract: Brain tumors are a challenging problem in neuro-oncology, where early and precise diagnosis is important for successful treatment. Deep learni

local-aiarxiv-cs-ai

DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference

arXiv:2604.15750v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive language generation due to their potential for parallel dec

local-aiarxiv-cs-ai

Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness

arXiv:2510.09033v3 Announce Type: replace Abstract: Recent work suggests that LLMs 'know what they don't know', positing that hallucinated and factually correct outputs arise from distinct internal pr

Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

arXiv:2604.16047v1 Announce Type: cross Abstract: The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accel

researcharxiv-cs-lg

DTEA: A Dual-Topology Elastic Actuator Enabling Real-Time Switching Between Series and Parallel Compliance

arXiv:2604.15865v1 Announce Type: new Abstract: Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition betwe

researcharxiv-cs-ro

DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation

arXiv:2506.03103v2 Announce Type: replace Abstract: Reconstructing dynamic hand-object contacts is essential for realistic manipulation in AI character animation, XR, and robotics, yet it remains chal

safetyarxiv-cs-cv

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

arXiv:2601.05808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich a

ExoNet: Multimodal Deep Learning for TESS Exoplanet Candidate Identification via Phase-Folded Light Curves, Stellar Parameters, and Multi-Head Attention Fusion

arXiv:2604.15560v1 Announce Type: cross Abstract: NASA's Transiting Exoplanet Survey Satellite (TESS) has identified thousands of exoplanet candidates, yet many remain unconfirmed due to the limitatio

local-aiarxiv-cs-lg

Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents

arXiv:2604.15877v1 Announce Type: new Abstract: As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory

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

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language

model-releasesarxiv-cs-cl

arXiv:2604.15841v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative

Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation

arXiv:2505.21072v4 Announce Type: replace Abstract: Large Language Models (LLMs) enhanced with retrieval, an approach known as Retrieval-Augmented Generation (RAG), have achieved strong performance in

Fed3D: Federated 3D Object Detection

arXiv:2604.15795v1 Announce Type: new Abstract: 3D object detection models trained in one server plays an important role in autonomous driving, robotics manipulation, and augmented reality scenarios.

local-aiarxiv-cs-cv

FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound

model-releasesarxiv-cs-cv

arXiv:2512.22278v2 Announce Type: replace Abstract: The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal h

Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations

arXiv:2603.03332v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the ro

Applications

From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts

arXiv:2604.16115v1 Announce Type: new Abstract: Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from

applicationsarxiv-cs-cv

From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text

arXiv:2604.16270v1 Announce Type: cross Abstract: The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solu

Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)

arXiv:2604.15772v1 Announce Type: new Abstract: Reinforcement learning (RL) often struggles in real-world tasks with high-dimensional state spaces and long horizons, where sparse or fixed rewards seve

agentsarxiv-cs-ro

HarmfulSkillBench: How Do Harmful Skills Weaponize Your Agents?

arXiv:2604.15415v1 Announce Type: cross Abstract: Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous

Hierarchical Codec Diffusion for Video-to-Speech Generation

arXiv:2604.15923v1 Announce Type: cross Abstract: Video-to-Speech (VTS) generation aims to synthesize speech from a silent video without auditory signals. However, existing VTS methods disregard the h

safetyarxiv-cs-cv

How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models

arXiv:2604.15873v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly studied as repositories of linguistic knowledge. In this line of work, models are commonly evaluated both

HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning

model-releasesarxiv-cs-cl

arXiv:2604.15648v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain

HYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design

arXiv:2604.15642v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rap

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

Information-Consistent Language Model Recommendations through Group Relative Policy Optimization

arXiv:2512.12858v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in business-critical domains such as finance, education, healthcare, and customer suppo

safetyarxiv-cs-ai

Applications

Information Router for Mitigating Modality Dominance in Vision-Language Models

arXiv:2604.16264v1 Announce Type: new Abstract: Vision Language models (VLMs) have demonstrated strong performance across a wide range of benchmarks, yet they often suffer from modality dominance, whe

applicationsarxiv-cs-cv

InstructTable: Improving Table Structure Recognition Through Instructions

model-releasesarxiv-cs-cv

arXiv:2604.02880v2 Announce Type: replace Abstract: Table structure recognition (TSR) holds widespread practical importance by parsing tabular images into structured representations, yet encounters si

JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models

arXiv:2604.16171v1 Announce Type: cross Abstract: Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a lo

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

safetyarxiv-cs-ai

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

LLMbench: A Comparative Close Reading Workbench for Large Language Models

arXiv:2604.15508v1 Announce Type: cross Abstract: LLMbench is a browser-based workbench for the comparative close reading of large language model (LLM) outputs. Where existing tools for LLM comparison