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Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis

arXiv:2608.00956v1 Announce Type: new Abstract: This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-drive

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Model Releases

Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods

DGX agent

arXiv:2608.01180v1 Announce Type: new Abstract: Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and

model-releasesarxiv-cs-lg

4 Aug 2026

ISRS-DETR: Detection-Guided Click Propagation for Remote Sensing Interactive Segmentation

arXiv:2608.02468v1 Announce Type: new Abstract: Interactive segmentation reduces the prohibitive cost of pixel-level annotation by allowing users to delineate objects with a few clicks. However, apply

It's the Decoding Format, Not the Perturbation: Auditing Consistency-Based Selection for Vision-Language Test-Time Scaling

arXiv:2608.01207v1 Announce Type: new Abstract: Test-time scaling lifts large language model reasoning by sampling many candidate solutions and selecting among them, yet the same recipe transfers poor

JADE-GS: Joint Allocation of Deblurring Evidence for Event-Assisted 3D Gaussian Splatting

arXiv:2607.14990v2 Announce Type: replace Abstract: Neural radiance fields and 3D Gaussian Splatting assume that each training image is a sharp and geometrically consistent observation of the scene. M

Kilobyte Models: Neural Networks as a Seed and a Quantized Latent

arXiv:2608.00860v1 Announce Type: new Abstract: The cost of storing and transmitting a trained neural network scales with its parameter count, a bottleneck for over-the-air updates, on-device librarie

LakeMLB: Data Lake Machine Learning Benchmark

arXiv:2602.10441v2 Announce Type: replace Abstract: Data lakes have become a fundamental platform for large-scale machine learning by enabling flexible management of heterogeneous data. Despite their

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

arXiv:2604.03532v2 Announce Type: replace Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Repres

Large Causal Models for Temporal Causal Discovery

arXiv:2602.18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for

Latency-Tolerant Cloud-Edge Collaborative Vision-Language-Action Models via Emergent Representational Specialization

arXiv:2608.00569v1 Announce Type: new Abstract: Deploying billion-parameter Vision-Language-Action (VLA) policies on mobile robots creates a systems conflict: semantic reasoning benefits from cloud GP

Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving

arXiv:2608.00237v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently emerged as a promising paradigm for end-to-end autonomous driving, enabling agents to map multimodal inputs

Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark

arXiv:2608.00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it. A controller may answer dir

Learning What to Remember: Test-Time Training via Context Distillation

arXiv:2608.01672v1 Announce Type: new Abstract: Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later. Test

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging

arXiv:2608.01094v1 Announce Type: new Abstract: Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', t

Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention

arXiv:2602.04789v4 Announce Type: replace Abstract: Advanced autoregressive (AR) video generation models have improved visual fidelity and interactivity, but the quadratic complexity of attention rema

Linear Multi-Timescale Retention as a Memory-Efficient Vision-Language Bridge

arXiv:2608.01614v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a critical computational bottleneck when processing high-resolution imagery due to the O(N^2) memory complexity of So

LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference

arXiv:2608.02515v1 Announce Type: new Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retri

Loggia dei Lanzi: AI Thermography Enhancement Comparisons through 3D Photogrammetry

arXiv:2608.02404v1 Announce Type: new Abstract: The Loggia dei Lanzi in the Piazza della Signoria is one of Florence's most prominent structures visited by millions every year. Its construction histor

Long-Horizon Embodied Decision-Making via Multimodal Memory Compression

arXiv:2608.01456v1 Announce Type: cross Abstract: Agents are increasingly expected to act not only as task executors, but also as decision-makers on behalf of human users. This shift requires agents t

LongCat Sparse Attention: Taming the Lightning via Streaming-aware Hierarchical Cross-Layer Indexing

arXiv:2608.01662v1 Announce Type: cross Abstract: DeepSeek Sparse Attention (DSA) enables efficient long-context modeling through its Lightning Indexer. However, practical deployment remains constrain

LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning

arXiv:2608.01328v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities, enabling multi-chart un

LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks

arXiv:2608.01964v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interde

Look Up and Look Back: Hidden Attention and Latent Orientation in a Frozen Foundation Model for Panoramic SLAM

arXiv:2608.00925v1 Announce Type: new Abstract: Monocular panoramic SLAM benefits from substantial visual overlap under large camera rotations, yet remains prone to errors caused by camera tilt, scale

Look Where It Matters: Adaptive Visual Refinement for Vision-Language-Action Models

arXiv:2608.02197v1 Announce Type: new Abstract: Visual representations of VLA models remain unreliable for spatially precise robotic manipulation. We uncover that vision encoders in VLAs also exhibit

Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration

arXiv:2608.02346v1 Announce Type: new Abstract: Old photographs often suffer from multiple coupled degradations, including scratches, cracks, fading, blur, noise, and missing regions, severely degradi

LooperMuscle: Fast and Stable Learning of Humanoid Whole-Body Tracking via Structured Mixture-of-Experts

arXiv:2608.00820v1 Announce Type: new Abstract: FastSAC-style methods significantly reduce humanoid motion training time but often suffer from notable performance degradation compared with PPO in whol

LoopsBench: From Harness Engineering to Loop Engineering in Benchmarking Coding Agent

arXiv:2608.00267v1 Announce Type: cross Abstract: Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon soft

Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data

arXiv:2507.09652v2 Announce Type: replace-cross Abstract: Low-dimensional chaotic systems such as the Lorenz-63 model are commonly used to benchmark system-agnostic methods for learning dynamics from

Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models

arXiv:2409.07163v3 Announce Type: replace-cross Abstract: Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing

Manifold-GS: Certified Hybrid Assets via Varifold-Conservative Gaussian Splatting

arXiv:2608.00214v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) gives high-quality novel-view synthesis, but its adaptive radiance primitives are not directly usable as structured assets:

MAPLE: Metadata Augmented Private Language Evolution

arXiv:2603.19258v2 Announce Type: replace Abstract: Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the

MDTD-ArtIR: Benchmarking Image Editing and Restoration Models for Art Image Restoration under Texture-Overlay Degradations

arXiv:2608.00736v1 Announce Type: new Abstract: Restoring severely degraded visual media still remains a formidable challenge, as existing methods often hallucinate unnatural textures and contents, st

MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments

arXiv:2608.00257v1 Announce Type: new Abstract: Automated visual monitoring of urban environments is a growing Computer Vision research area, but municipal solid waste detection remains under-represen

Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal standard

arXiv:2608.01575v1 Announce Type: new Abstract: Whether large language models perform genuine algorithmic reasoning or mere pattern completion is hard to test, because most benchmarks lack a ground tr

MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs

arXiv:2608.02520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for health-related advice. Existing research measures their safety with static questions rather than

MetaRoute-Bench: Evaluating Meta-Decision Policies for Agentic Workflow Routing

arXiv:2608.00107v1 Announce Type: new Abstract: Agentic systems must repeatedly decide whether to answer directly, decompose a task, invoke a tool, execute code, delegate to a specialist, verify an in

MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing

arXiv:2608.02059v1 Announce Type: new Abstract: Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-

Minute-Scale Training for Microrobot Navigation

arXiv:2608.00854v1 Announce Type: new Abstract: Microrobots hold significant potential for various applications, where targeted navigation is a basic requirement. Deep reinforcement learning (DRL) has

Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling

arXiv:2608.00732v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject ma

MixedComplementarityProblems.jl: A Fast, Batched, Open-Source Interior Point Solver for Mixed Complementarity Problems

arXiv:2608.00959v1 Announce Type: cross Abstract: Mixed complementarity problems (MCPs) arise as the first-order optimality conditions of nonlinear programs and noncooperative games, and provide a nat

MoCRA: Mixture of Compositional Rank-1 Atoms for 4K All-in-One Video Restoration

arXiv:2608.01829v1 Announce Type: new Abstract: Real-world video arrives hazy, rainy, dark, or noisy, and a deployable restorer faces three demands at once: no degradation label, native 4K output, and

Models as Tools: An Agentic Coordination Framework for Unified Multimodal Visual Tracking

arXiv:2608.00847v1 Announce Type: new Abstract: Most current visual trackers adopt a matching-based architecture trained exclusively on tracking datasets, whose performance gains depend heavily on the

MoRAL: Sensor-Grounded BEV Reasoning for Compact VLMs toward Edge-Oriented Autonomous Driving

arXiv:2608.02449v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) for safety-critical spatial reasoning on resource-constrained autonomous driving platforms requires both compact

Morphology Aware Reversible Semantic Tokenization and Hierarchical Word Composition for Tamil Language Models

arXiv:2608.01153v1 Announce Type: new Abstract: Statistical subword tokenizers can process arbitrary text, but their units need not align with lexical or grammatical structure. This is especially impo

Move What Matters: Parameter-Efficient Domain Adaptation via Optimal Transport Flow for Collaborative Perception

arXiv:2602.11565v5 Announce Type: replace Abstract: Efficient domain adaptation remains a fundamental challenge for deploying multi-agent systems across diverse environments in Vehicle-to-Everything (

New York Smells: A Large Multimodal Dataset for Olfaction

arXiv:2511.20544v2 Announce Type: replace Abstract: While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key

Not the Dimension, the Norm: What Matters in Gradient-Free Weight Perturbation of Language Models

arXiv:2608.01624v1 Announce Type: new Abstract: Adapting a language model to a task no longer requires training all of its weights, and a line of parameter-efficient methods has driven the trainable c

Nova: An End-to-End MLIR Compiler for Deep Learning

arXiv:2608.00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physica