Search: “models”

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2

arXiv:2512.22671v2 Announce Type: replace Abstract: Structured width pruning of GLU-MLP layers, guided by the Maximum Absolute Weight (MAW) criterion, reveals a systematic dichotomy in how reducing th

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Generalization Bounds of Spiking Neural Networks via Rademacher Complexity

DGX agent

arXiv:2605.02927v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have garnered increasing attention as one of bio-inspired models due to their great potential in neuromorphic computing

model-releasesarxiv-cs-ai

7 May 2026

Generative Quantum-inspired Kolmogorov-Arnold Eigensolver

arXiv:2605.04604v1 Announce Type: cross Abstract: High-performance computing (HPC) is increasingly important for scalable quantum chemistry workflows that couple classical generative models, quantum c

Intermediate Representations are Strong AI-Generated Image Detectors

arXiv:2605.04358v1 Announce Type: new Abstract: The rapid advancement in generative AI models has enabled the creation of photorealistic images. At the same time, there are growing concerns about the

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning

arXiv:2605.04058v1 Announce Type: new Abstract: Parameter-efficient transfer learning (PETL) has emerged as a pivotal paradigm for adapting pre-trained foundation models to downstream tasks, significa

OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation

arXiv:2601.22725v3 Announce Type: replace Abstract: Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remain

Safety

OracleProto: A Reproducible Framework for Benchmarking LLM Native Forecasting via Knowledge Cutoff and Temporal Masking

arXiv:2605.03762v1 Announce Type: new Abstract: Large language models are moving from static text generators toward real-world decision-support systems, where forecasting is a composite capability tha

safetyarxiv-cs-ai

Transformation Categorization Based on Group Decomposition Theory Using Parameter Division

arXiv:2605.04056v1 Announce Type: new Abstract: Representation learning seeks meaningful sensory representations without supervision and can model aspects of human development. Although many neural ne

UFAL-CUNI at SemEval-2026 Task 11: An Efficient Modular Neuro-symbolic Method for Syllogistic Reasoning

arXiv:2605.04941v1 Announce Type: new Abstract: This paper describes our system submitted to SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We present an ef

AI and Open-data Driven Scalable Solar Power Profiling

arXiv:2605.02738v1 Announce Type: new Abstract: Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV rem

Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification

arXiv:2605.03710v1 Announce Type: cross Abstract: Bayesian predictive inference propagates parameter uncertainty to quantities of interest through the posterior-predictive distribution. In practice, t

Artificial Jagged Intelligence as Uneven Optimization Energy Allocation Capability Concentration, Redistribution, and Optimization Governance

arXiv:2605.01420v1 Announce Type: new Abstract: Artificial Jagged Intelligence (AJI) denotes a recurring pattern in which large learning systems exhibit strong local capabilities while remaining weak

Benchmarking Document Parsers on Mathematical Formula Extraction from PDFs

arXiv:2512.09874v2 Announce Type: replace Abstract: Correctly parsing mathematical formulas from PDFs is critical for training large language models and building scientific knowledge bases from academ

Calibration of the underlying surface parameters for urban flood using latent variables and adjoint equation

arXiv:2605.02959v1 Announce Type: new Abstract: Calibrating the urban underlying surface parameters is crucial for urban flood simulation. We formulate the parameter calibration problem into an optimi

ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files

arXiv:2603.00822v2 Announce Type: replace-cross Abstract: As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language

DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset

arXiv:2605.03544v1 Announce Type: new Abstract: Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent ben

DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams

arXiv:2605.01338v1 Announce Type: new Abstract: System-level diagrams encode the architectural blueprint of chip design, specifying module functions, dataflows, and interface protocols. However, non-s

DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion

arXiv:2605.02163v1 Announce Type: cross Abstract: Software documentation frequently drifts from executable logic as codebases evolve, creating technical debt that degrades maintainability and causes d

Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls

arXiv:2605.03147v1 Announce Type: new Abstract: Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult. Unlike t

Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for controlled, single-s

FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction

arXiv:2605.01726v1 Announce Type: cross Abstract: Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-dom

Research

GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction

arXiv:2605.03764v1 Announce Type: new Abstract: Diffusion-based voxel prior modelling is challenging for the reconstruction of large-scale 3D porous microstructures. Due to the demanding requirements

researcharxiv-cs-cv

'I Don't Know' -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation

arXiv:2605.00957v1 Announce Type: cross Abstract: Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LL

Label-Efficient School Detection from Aerial Imagery via Weakly Supervised Pretraining and Fine-Tuning

arXiv:2605.03968v1 Announce Type: new Abstract: Accurate school detection is essential for supporting education initiatives, including infrastructure planning and expanding internet connectivity to un

Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective

arXiv:2605.03373v1 Announce Type: new Abstract: Classical optimization theory establishes that zeroth-order (ZO) algorithms suffer from a dimension-dependent slowdown, with convergence rates typically

LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation

arXiv:2605.01394v1 Announce Type: cross Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Alth

MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs

arXiv:2505.20740v3 Announce Type: replace Abstract: The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application

OCRR: A Benchmark for Online Correction Recovery under Distribution Shift

arXiv:2605.03153v1 Announce Type: cross Abstract: Static benchmarks measure a model frozen at training time. Real systems face distribution shift: new categories, paraphrased queries, drift: and must

PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution

arXiv:2605.03399v1 Announce Type: new Abstract: Probabilistic super-resolution of high-dimensional spatial fields using diffusion models is often computationally prohibitive due to the cost of operati

PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition

arXiv:2603.05965v2 Announce Type: replace-cross Abstract: We present PROBE (PRobabilistic Occupancy BEV Encoding), a learning-free LiDAR place recognition descriptor that models each BEV cell's occupa

ReLeaf: Benchmarking Leaf Segmentation across Domains and Species

arXiv:2605.03784v1 Announce Type: new Abstract: Rising global food demand and growing climate pressure increase the need for sustainable, precise agricultural practices. Automated, individualized plan

Local Ai

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs

arXiv:2605.02946v1 Announce Type: new Abstract: Safety alignment is critical for the responsible deployment of large language models (LLMs). As Mixture-of-Experts (MoE) architectures are increasingly

local-aiarxiv-cs-lg

Agents

SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue

arXiv:2602.03548v3 Announce Type: replace Abstract: Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in

agentsarxiv-cs-cl

Simulated Students in Tutoring Dialogues: Substance or Illusion?

arXiv:2601.04025v2 Announce Type: replace Abstract: Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires

Research

Steer Like the LLM: Activation Steering that Mimics Prompting

arXiv:2605.03907v1 Announce Type: new Abstract: Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform

researcharxiv-cs-cl

The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission

arXiv:2605.00844v1 Announce Type: cross Abstract: When large language models (LLMs) are consulted as forecasting tools, the independence of individual errors -- the foundation of collective intelligen

Safety

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

arXiv:2605.02640v1 Announce Type: new Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), is increasingly deployed in high-stakes domains, en

safetyarxiv-cs-ai

A multilingual hallucination benchmark: MultiWikiQHalluA

arXiv:2605.02504v1 Announce Type: new Abstract: Most hallucination evaluations focus on English, leaving it unclear whether findings transfer to lower-resource languages. We investigate faithfulness h

Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach

arXiv:2605.01292v1 Announce Type: new Abstract: The growing spread of misinformation in digital media highlights the need for reliable fake news detection systems, yet progress in under-resourced lang

Safety

AI Alignment via Incentives and Correction

arXiv:2605.01643v1 Announce Type: new Abstract: We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an exter

safetyarxiv-cs-lg

AI-Driven Expansion and Application of the Alexandria Database

arXiv:2512.09169v2 Announce Type: replace-cross Abstract: We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within

Applications

Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events

arXiv:2605.01226v1 Announce Type: new Abstract: Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely

applicationsarxiv-cs-lg

Attention Is Where You Attack

arXiv:2605.00236v1 Announce Type: cross Abstract: Safety-aligned large language models rely on RLHF and instruction tuning to refuse harmful requests, yet the internal mechanisms implementing safety b

Applications

Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs

arXiv:2506.13727v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and c

applicationsarxiv-cs-cl

Boosting Multimodal Remote Sensing Image Classification with Transformer-based Heterogeneously Salient Graph Representation

arXiv:2311.10320v3 Announce Type: replace Abstract: Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-s

BRITE: A Benchmark for Reliable and Interpretable T2V Evaluation on Implausible Scenarios

arXiv:2605.00873v1 Announce Type: cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods. Existing benchmarks

Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks

arXiv:2605.01291v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are widely regarded as an energy-efficient paradigm for modeling and processing temporal and event-driven information. In

Applications

CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation

arXiv:2605.02600v1 Announce Type: new Abstract: While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understand

applicationsarxiv-cs-ro