Search: “models”

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606.04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities. However, purely data-driven machine learning models

4 Jun 2026

Local Ai

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data

DGX agent

arXiv:2606.05122v1 Announce Type: new Abstract: Large language models are increasingly evaluated by other models, raising a natural question: can a model predict how a judge will score its own output?

local-aiarxiv-cs-cl

4 Jun 2026

Toward a Generalized Defense Across Sparse, Continuous, and Structured Parameter Attacks

arXiv:2606.04317v1 Announce Type: cross Abstract: Deep neural networks are increasingly deployed across heterogeneous and partially untrusted environments, where models are distributed through cloud s

4 Jun 2026

WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation

arXiv:2606.04907v1 Announce Type: new Abstract: Visual navigation requires generating smooth and collision-free trajectories under complex geometric and physical constraints. Existing reactive policie

safetyarxiv-cs-ro

4 Jun 2026

A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents

arXiv:2601.09869v2 Announce Type: replace Abstract: Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise

researcharxiv-cs-ai

APIC: Amortized Physics-Informed Calibration using Neural Processes

arXiv:2606.03355v1 Announce Type: new Abstract: Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and r

Balancing Symmetry and Efficiency in Graph Flow Matching

arXiv:2602.18084v2 Announce Type: replace Abstract: Equivariance is central to graph generative models, as it ensures the model respects the permutation symmetry of graphs. However, strict equivarianc

COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions

arXiv:2606.02603v1 Announce Type: new Abstract: Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images. This is not realistic becaus

Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition

arXiv:2606.03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data. This requires more than recalling a functi

Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy

arXiv:2606.03142v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) show strong visualization interpretation, yet it is unclear whether their responses reflect genuine reasoning over

Applications

E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environments

arXiv:2606.03770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themse

applicationsarxiv-cs-ai

Enginuity: A Dataset and Benchmark for Vision-Language Understanding of Engineering Diagrams

arXiv:2606.03410v1 Announce Type: new Abstract: Engineering diagrams pose a distinct challenge for vision-language models: unlike natural images or general documents, they encode information through d

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606.03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps. Real-world benchmarks off

Fixed-Time Dynamic Landing of Quadrotors using Adaptive Unscented Kalman Filtering and Nonlinear Model Predictive Control

arXiv:2606.02658v1 Announce Type: new Abstract: This paper introduces an estimation and control framework for dynamic landing of multi-rotor uncrewed aerial vehicles on moving platforms. The proposed

researcharxiv-cs-ro

Follow-Your-Preference++: Rethinking Preference Alignment for Image Inpainting

arXiv:2606.03216v1 Announce Type: new Abstract: We study preference alignment for image inpainting. Rather than proposing yet another method, we revisit the problem from first principles and reassess

safetyarxiv-cs-cv

Gender-Dependent Diagnostic Substitution in LLM Medical Triage: Same Symptoms, Unequal Urgency

arXiv:2606.03641v1 Announce Type: new Abstract: We investigate whether large language models produce different medical triage recommendations for identical neurological symptoms when only the patient'

GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving

arXiv:2606.02774v1 Announce Type: new Abstract: Vision-language models (VLMs) for autonomous driving have shown promising performance, but their ability to handle region-specific traffic rules remains

Laplacian Representations for Decision-Time Planning

arXiv:2602.05031v2 Announce Type: replace Abstract: Planning with a learned model remains a key challenge in model-based reinforcement learning (RL). In decision-time planning, state representations a

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606.03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a sys

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

arXiv:2606.03980v1 Announce Type: cross Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) p

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

arXiv:2606.00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606.01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual

BraveGuard: From Open-World Threats to Safer Computer-Use Agents

arXiv:2606.01166v1 Announce Type: cross Abstract: Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shi

ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression

arXiv:2606.00111v1 Announce Type: cross Abstract: State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures. To further improve rate-disto

researcharxiv-cs-cv

Correcting Gradient-Based Circuit Localization via Interaction-Aware Backpropagation

arXiv:2505.17630v4 Announce Type: replace Abstract: Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling de

CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

arXiv:2606.00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack special

DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts

arXiv:2606.01062v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have become a leading approach for decoupling parameter count from computational cost in large language models, yet effe

Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark

arXiv:2606.02214v1 Announce Type: new Abstract: Large language models are increasingly used in value-sensitive decision settings, where irrelevant demographic cues should not alter judgments. We const

Efficient Exploration for Iterative Nash Preference Optimization

arXiv:2606.01382v1 Announce Type: cross Abstract: Preference alignment is central to improving large language models, but standard reward-based formulations can be restrictive when human preferences a

Enhancing LLM Metacognition via Cognitive Pairwise Training

arXiv:2606.00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to

Applications

Evolving to the Aesthetics of a Vision-Language Model

arXiv:2606.00112v1 Announce Type: cross Abstract: Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. H

applicationsarxiv-cs-cv

Experimenting with TPUs, GKE Managed DRANET, and Multi-cluster Inference Gateway

model-releasesgoogle-cloud-ai

What happens when your workload fails in one region but you need access to service? This is a common case for availability and uptime. With recent enhancement to the Kubernetes ecosystem and capabilit

Local Ai

FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment

arXiv:2606.01607v1 Announce Type: cross Abstract: Federated learning (FL) is a decentralized approach that enables collaborative model training without exposing raw data. Instead of transferring sensi

local-aiarxiv-cs-ai

FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning

arXiv:2604.03893v2 Announce Type: replace Abstract: Current multimodal benchmarks for scientific reasoning primarily evaluate local information extraction -- models recognize symbols and values and th

FlatVPR: Plug-and-play Geo-linear Residual Adapter for Geometric Rectification of Foundation Model Feature Manifolds

arXiv:2606.01734v1 Announce Type: new Abstract: This paper proposes ``FlatVPR,'' a novel geometric rectification paradigm that effectively bridges the trade-off between map lightweightness and localiz

researcharxiv-cs-cv

HypothesisMed: Inference-Time Answer Fusion and Structured Hypothesis-Space Reporting for Biomedical Question Answering

arXiv:2606.00971v1 Announce Type: new Abstract: Biomedical question answering with large language models is commonly evaluated using answer accuracy, but answer accuracy alone does not indicate whethe

LAP: Fast LAtent Diffusion Planner for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2512.00470v4 Announce Type: replace Abstract: Diffusion models have demonstrated strong capabilities for modeling human-like driving behaviors in autonomous driving, but their iterative sampling

Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning

arXiv:2603.24324v4 Announce Type: replace-cross Abstract: Designing effective auxiliary rewards for cooperative multi-agent systems remains challenging, as misaligned incentives can induce suboptimal

Industry

Microsoft’s first advanced reasoning AI is here

Microsoft announced a bunch of new in-house AI models at Build 2026, including a new 'flagship' model: MAI-Thinking-1. It's an ambitious step into model development for Microsoft, which introduced its

industrythe-verge-ai

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

arXiv:2606.00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectr

Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events

arXiv:2606.02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve br

MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition

arXiv:2606.00708v1 Announce Type: new Abstract: Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, trai

MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching

arXiv:2606.01985v1 Announce Type: new Abstract: Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing de

Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning

arXiv:2411.11793v2 Announce Type: replace Abstract: In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients m

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinica

Paving the Way for Point Cloud Video Representation Learning Using A PDE Model

arXiv:2606.01604v1 Announce Type: new Abstract: Investigating spatial-temporal correlations, specifically how spatial points vary over time, is crucial for understanding point cloud videos. Traditiona

researcharxiv-cs-cv

Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces

arXiv:2603.14798v2 Announce Type: replace-cross Abstract: We propose a machine-learning algorithm for Bayesian inverse problems in the function-space regime. Based on one-step generative transport, th

Residual Decoder Adapter: ID-Preserving Tokenizer Adaption for Autoregressive Text Rendering

arXiv:2606.01911v1 Announce Type: new Abstract: Visual Autoregressive (AR) models generate images by predicting discrete tokens that are decoded by a visual tokenizer. Despite demonstrating strong ove