Search: “models”

Optimality of Sub-network Laplace Approximations: New Results and Methods

arXiv:2605.09075v1 Announce Type: cross Abstract: Although the Laplace approximation offers a simple route to uncertainty quantification in deep neural networks, its reliance on inverting large Hessia

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Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning

DGX agent

arXiv:2605.08660v1 Announce Type: new Abstract: In the recent literature, Support Vector Regression (SVR) has been cited as one of the weakest performers on the California Housing benchmark dataset, w

model-releasesarxiv-cs-lg

12 May 2026

OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting

arXiv:2502.06830v5 Announce Type: replace-cross Abstract: Probabilistic intraday electricity price forecasting is becoming increasingly important for short-term power-system operation. With increasing

PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation

arXiv:2605.09036v1 Announce Type: new Abstract: Accurate and efficient storm-surge emulation is essential for coastal hazard assessment, yet high-fidelity hydrodynamic models remain too expensive for

researcharxiv-cs-lg

Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding

arXiv:2602.06733v2 Announce Type: replace-cross Abstract: Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their

Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses

arXiv:2605.10219v1 Announce Type: cross Abstract: We study the parameterized complexity of testing approximate first-order stationarity at a prescribed point for continuous piecewise-affine (PA) funct

PDEAgent-Bench: A Multi-Metric, Multi-Library Benchmark for PDE Solver Generation

arXiv:2605.09636v1 Announce Type: new Abstract: PDE-to-solver code generation aims to automatically synthesize executable numerical solvers from partial differential equation (PDE) specifications. Thi

Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

arXiv:2605.09339v1 Announce Type: cross Abstract: Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured

Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning

arXiv:2605.09638v1 Announce Type: new Abstract: Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world

safetyarxiv-cs-lg

PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.10275v1 Announce Type: new Abstract: Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP)

Local Ai

Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention

arXiv:2605.10581v1 Announce Type: new Abstract: Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consiste

local-aiarxiv-cs-cv

Applications

Position: Avoid Overstretching LLMs for every Enterprise Task

arXiv:2605.09365v1 Announce Type: new Abstract: Enterprise workloads are dominated by deterministic, structured, and knowledge-dependent tasks operating under strict cost, latency, and reliability con

applicationsarxiv-cs-ai

PrepBench: How Far Are We from Natural-Language-Driven Data Preparation?

arXiv:2605.08687v1 Announce Type: cross Abstract: Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user inter

Primal-Dual Guided Decoding for Constrained Discrete Diffusion

arXiv:2605.09749v1 Announce Type: new Abstract: Discrete diffusion models generate structured sequences by progressively unmasking tokens, but enforcing global property constraints during generation r

safetyarxiv-cs-ai

PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines

arXiv:2605.10614v1 Announce Type: new Abstract: Multi-agent LLM systems introduce a security risk in which sensitive information accessed by one agent can propagate through shared context and reappear

Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers

model-releasesarxiv-cs-cv

arXiv:2510.25372v2 Announce Type: replace Abstract: Visual Prompt Tuning (VPT) of pre-trained Vision Transformers (ViTs) has proven highly effective as a parameter-efficient fine-tuning technique for

PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram

arXiv:2605.09431v1 Announce Type: new Abstract: Cryptocurrency pump-and-dump schemes coordinated via Telegram threaten market integrity. However, existing research addressing this specific threat has

Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants

arXiv:2605.09808v1 Announce Type: new Abstract: User simulators are increasingly leveraged to build interactive AI assistants, yet how to measure the quality of these simulators remains an open questi

Quantile-Coupled Flow Matching for Distributional Reinforcement Learning

arXiv:2605.08515v1 Announce Type: new Abstract: Unlike standard expected-return Reinforcement Learning (RL), Distributional RL (DRL) models the full return distribution, making it better-suited for un

safetyarxiv-cs-lg

Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers Equation

arXiv:2605.08170v1 Announce Type: new Abstract: Neural operators have emerged as a powerful tool for learning mappings between infinite-dimensional function spaces. However, their approximation proper

Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms

arXiv:2605.08423v1 Announce Type: cross Abstract: We present a data-adaptive method for parameter-efficient fine-tuning of large neural networks. Standard low-rank adaptation methods improve efficienc

R4Det: 4D Radar-Camera Fusion for High-Performance 3D Object Detection

model-releasesarxiv-cs-cv

arXiv:2603.11566v2 Announce Type: replace Abstract: 4D radar-camera sensing configuration has gained increasing importance in autonomous driving. However, existing 3D object detection methods that fus

RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction

arXiv:2605.08857v1 Announce Type: new Abstract: Recent advances in uncertainty quantification for time series forecasting show that conformal prediction can provide reliable prediction intervals, yet

Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency

arXiv:2605.08454v1 Announce Type: cross Abstract: Recovering continuous-time dynamics from discrete observations is difficult because local supervision (e.g., pointwise regression targets, derivative

Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions

arXiv:2605.10317v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) models typically represent each relation as an operator on entity embeddings. In this work, we identify three structur

safetyarxiv-cs-ai

ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing

arXiv:2605.08840v1 Announce Type: new Abstract: Large language models (LLMs) face growing challenges in efficient generative inference due to the increasing memory demands of Key-Value (KV) caches, es

researcharxiv-cs-cl

Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging

arXiv:2605.09905v1 Announce Type: cross Abstract: Automatic sleep staging commonly adopts Transformers under the assumption that they learn complex long-range dependencies. We challenge this view by r

Robust Spectral Watermark for Synthetic Tabular Data

arXiv:2511.21600v2 Announce Type: replace-cross Abstract: The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and pu

RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild

arXiv:2605.10357v1 Announce Type: cross Abstract: Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce ex

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning

arXiv:2605.08391v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning agents that act on partial local observations face a fundamental information bottleneck: the knowledge ne

SAP SAPPHIRE 2026: Google Cloud unveils unified agentic vision and massive compute scaling

model-releasesgoogle-cloud-ai

In today's hyper-connected market, an enterprise's most valuable asset — mission-critical data — often remains trapped in legacy silos. For years, leadership teams have navigated a data pipeline dilem

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605.09038v1 Announce Type: new Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especia

Selective LoRA for Visual Tokens and Attention Heads

arXiv:2512.19219v2 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) is widely used for parameter-efficient fine-tuning, but its standard all-token, all-head design ignores the heterog

Semantic Alignment in Hyperbolic Space for Open-Vocabulary Semantic Segmentation

arXiv:2605.08874v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires adapting image-level vision-language models such as CLIP to dense pixel-level prediction, which is challe

safetyarxiv-cs-cv

Applications

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding

arXiv:2605.09271v1 Announce Type: new Abstract: Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for comp

applicationsarxiv-cs-ai

Single-Configuration Attack Success Rate Is Not Enough: Jailbreak Evaluations Should Report Distributional Attack Success

arXiv:2605.09070v1 Announce Type: cross Abstract: Many jailbreak attack research papers report attack success rates for a limited number of parameter settings, even though there are many combinations

Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching

arXiv:2605.08658v1 Announce Type: cross Abstract: SKETCHVERIFY is a within-tier cost-performance policy, not a universal accuracy improvement. The operational question: a practitioner stuck with a sma

Sources: Anthropic is in talks to raise between 30B and 50B in a funding round that would value it at up to $950B (Mike Isaac/New York Times)

Mike Isaac / New York Times: Sources: Anthropic is in talks to raise between 30B and 50B in a funding round that would value it at up to 950B — The start-up, which recently released a powerful A.I. mo

model-releasestechmeme

Sparsity Moves Computation: How FFN Architecture Reshapes Attention in Small Transformers

arXiv:2605.09403v1 Announce Type: cross Abstract: Architectural choices inside the Transformer feedforward network (FFN) block do not merely affect the block itself; they reshape the computations lear

Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction

arXiv:2605.08220v1 Announce Type: new Abstract: The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (

Spectral Condition for muP under Width-Depth Scaling

arXiv:2603.00541v2 Announce Type: replace Abstract: Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable

researcharxiv-cs-lg

Step Rejection Fine-Tuning: A Practical Distillation Recipe

arXiv:2605.10674v1 Announce Type: cross Abstract: Rejection Fine-Tuning (RFT) is a standard method for training LLM agents, where unsuccessful trajectories are discarded from the training set. In the

Strategic commitments shape collective cybersecurity under AI inequality

arXiv:2605.09415v1 Announce Type: new Abstract: The growing integration of AI into cybersecurity is reshaping the balance between attackers and defenders. When access to advanced AI-enabled defence to

TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network

arXiv:2506.01352v2 Announce Type: replace Abstract: Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participant

researcharxiv-cs-lg

Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation

arXiv:2505.11604v5 Announce Type: replace Abstract: Editing presentation slides is a frequent yet tedious task, ranging from creative layout design to repetitive text maintenance. While recent GUI-bas

Task complexity shapes internal representations and robustness in neural networks

arXiv:2508.05463v2 Announce Type: replace-cross Abstract: Neural networks excel across a wide range of tasks, yet remain black boxes. In particular, how their internal representations are shaped by th

The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT nicode{x2013} Multitracer Multicenter Generalization

arXiv:2605.05775v2 Announce Type: replace-cross Abstract: We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body P

The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions

arXiv:2605.10698v1 Announce Type: cross Abstract: Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that

safetyarxiv-cs-ai