All entries

IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation

arXiv:2605.30939v1 Announce Type: new Abstract: Semantic road segmentation is important for autonomous driving, but existing methods suffer severe performance degradation under low-light conditions. M

agentsarxiv-cs-cv

Safety

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

DGX agent

arXiv:2602.19049v2 Announce Type: replace Abstract: Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We

safetyarxiv-cs-cl

1 Jun 2026

Identifiable Equivariant Networks are Layerwise Equivariant

model-releasesarxiv-cs-lg

arXiv:2601.21645v2 Announce Type: replace Abstract: We investigate the relation between end-to-end equivariance and layerwise equivariance in deep neural networks. We prove the following: For a networ

Identifying Connectivity Distributions from Neural Dynamics Using Flows

arXiv:2603.26506v2 Announce Type: replace-cross Abstract: Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity

IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving

arXiv:2605.31476v1 Announce Type: new Abstract: End-to-end autonomous driving has emerged as a compelling paradigm for learning planning directly from sensor observations, while recent world-model-bas

agentsarxiv-cs-ro

idSCD: Identifying Training Datasets through Semantic Correlation Descriptors

arXiv:2605.30462v1 Announce Type: cross Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model

If LLMs Have Human-Like Attributes, Then So Does Age of Empires II

arXiv:2605.31514v1 Announce Type: cross Abstract: Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emerg

agentsarxiv-cs-ai

Safety

ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment

arXiv:2605.30965v1 Announce Type: cross Abstract: Recent advancements in text-guided audio generation have yielded promising results in diverse domains, including sound effects, speech, and music. How

safetyarxiv-cs-ai

ImmigrationQA: A Source-Grounded Dataset and Small-Model Adaptation for U.S. Immigration Law

model-releasesarxiv-cs-ai

arXiv:2605.30589v1 Announce Type: cross Abstract: U.S. immigration law spans thousands of pages of official policy, federal regulations, and procedural guidance that change frequently and carry high s

Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness

arXiv:2605.30745v1 Announce Type: new Abstract: Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. Howe

Improved Distribution Estimation in ell_infty

arXiv:2605.30509v1 Announce Type: cross Abstract: We present improved bounds for estimating discrete probability distributions under the ell_infty norm. These include minimax bounds in expectation and

Improved Guarantees for Langevin Monte Carlo with Average Smoothness

arXiv:2605.31413v1 Announce Type: cross Abstract: We establish improved nonasymptotic bounds for Langevin Monte Carlo in the strongly log-concave setting, when the error is measured by the Wasserstein

Improving Relative Representations with Learned Anchors and Whitened Inner Products

arXiv:2605.30596v1 Announce Type: new Abstract: Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI syste

tutorialsarxiv-cs-lg

Improving Selective Classification with Pairwise Queries for Binary Classification

arXiv:2605.30615v1 Announce Type: new Abstract: In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback

model-releasesarxiv-cs-cl

arXiv:2605.30478v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models using programmatically checkable signals such as unit-test outcomes, enab

Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data

model-releasesarxiv-cs-ai

arXiv:2605.31324v1 Announce Type: cross Abstract: Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theor

Incremental BPE Tokenization

arXiv:2605.30813v1 Announce Type: new Abstract: We propose a novel algorithm for incremental Byte Pair Encoding (BPE) tokenization. The algorithm processes each input byte in worst-case O(log^2 t) tim

Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation

arXiv:2605.31278v1 Announce Type: new Abstract: Reliable evaluation of agentic systems requires unbiased estimates with valid uncertainty, but standard practice navigates between costly human annotati

agentsarxiv-cs-ai

Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search

model-releasesarxiv-cs-cv

arXiv:2605.30917v1 Announce Type: cross Abstract: As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing atte

Inferring Events from Time Series using Language Models

arXiv:2503.14190v3 Announce Type: replace Abstract: A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can

IntAttention: A Fully Integer Attention Pipeline for Efficient Edge Inference

arXiv:2511.21513v2 Announce Type: replace Abstract: Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary m

'Intelegi Romaneste?'' A Recipe for Romanian Vision-Language Models

arXiv:2605.31401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) largely follow the text-only LLM trajectory, excelling on English benchmarks but sharply degrading on low-resource languag

Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization

arXiv:2605.31508v1 Announce Type: new Abstract: Video Object-Centric Learning (OCL) aims to represent objects as extit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive

tutorialsarxiv-cs-cv

Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance

arXiv:2605.31304v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely used, but interpreting what they actually learn remains difficult. A major obstacle is that individual neurons

tutorialsarxiv-cs-cv

Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach

arXiv:2605.30903v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) typically assumes demonstrations from a single optimal demonstrator, but in many applications data come from mult

Inversion-Free Natural Gradient Descent on Riemannian Manifolds

model-releasesarxiv-cs-lg

arXiv:2604.02969v2 Announce Type: replace-cross Abstract: The natural gradient method is a central tool for statistical optimisation, but its broader application is hindered by the assumption of a Euc

Inverting Data Transformations via Diffusion Sampling

arXiv:2602.08267v2 Announce Type: replace-cross Abstract: We study the problem of transformation inversion on general Lie groups: a datum is transformed by an unknown group element, and the goal is to

Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents

arXiv:2605.30677v1 Announce Type: cross Abstract: Agentic software reverse engineering systems are vulnerable to prompt injection attacks placed into the source code of executable binary files. This r

agentsarxiv-cs-ai

IRIS: time-structured manifold projections

arXiv:2605.30810v1 Announce Type: new Abstract: High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SN

Is Memorization Helpful or Harmful? Prior Information Sets the Threshold

arXiv:2602.09405v2 Announce Type: replace-cross Abstract: We examine the connection between training error and generalization error for arbitrary estimating procedures, working in an overparameterized

Is the Last Layer Sufficient for Uncertainty Quantification?

arXiv:2605.30741v1 Announce Type: cross Abstract: Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Sever

Safety

IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

arXiv:2603.19862v2 Announce Type: replace Abstract: Vision-Language Models like CLIP are extensively used for inter-modal tasks which involve both visual and text modalities. However, when the individ

safetyarxiv-cs-cv

Iterative Framework For Data Augmentation Of Segmented Fingerprints

arXiv:2605.31001v1 Announce Type: new Abstract: Infant biometrics presents unique challenges due to the physiological differences between infants and adults, compounded by the scarcity of available da

Local Ai

iVGR: Internalizing Visually Grounded Reasoning for MLLMs with Reinforcement Learning

arXiv:2605.31096v1 Announce Type: new Abstract: While visually grounded Chain-of-Thought (CoT) has emerged as a promising paradigm to enhance fine-grained perception in multimodal large language model

local-aiarxiv-cs-cv

Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing

arXiv:2605.30622v1 Announce Type: cross Abstract: Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed

tutorialsarxiv-cs-lg

Joint angle based learning to refine kinematic human pose estimation

arXiv:2507.11075v2 Announce Type: replace-cross Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in key

Joint Multi-Camera LiDAR Extrinsic Calibration via Learned Pairwise Initialization and Geometric Refinement

arXiv:2605.31576v1 Announce Type: new Abstract: Most learning-based camera-LiDAR calibration methods treat each camera-LiDAR pair independently, ignoring the rigid geometric coupling in multi-camera p

Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble

arXiv:2605.30720v1 Announce Type: cross Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural

Hardware

Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts

arXiv:2605.30359v1 Announce Type: cross Abstract: Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language

hardwarearxiv-cs-lg

KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware

model-releasesarxiv-cs-lg

arXiv:2603.08721v2 Announce Type: replace-cross Abstract: New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels, a time-consu

KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems

arXiv:2605.31596v1 Announce Type: new Abstract: Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distributio

Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation

model-releasesarxiv-cs-cl

arXiv:2605.31478v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate power-system analysis, but many utilities and energy-research labs require on-premise s

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study

arXiv:2605.30465v1 Announce Type: new Abstract: Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. W

KnowledgeGain: Evaluating and Optimizing Science News Generation for Reader Learning

arXiv:2605.31099v1 Announce Type: cross Abstract: Science news is an important medium to communicate discoveries between the research communities and the public. Yet, most metrics for generated or sum

tutorialsarxiv-cs-ai

Local Ai

L2G-Net: Local to Global Spectral Graph Neural Networks via Cauchy Factorizations

arXiv:2602.18837v2 Announce Type: replace Abstract: Despite their theoretical advantages, spectral methods based on the graph Fourier transform (GFT) are seldom used in graph neural networks (GNNs) du

local-aiarxiv-cs-lg

LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation

model-releasesarxiv-cs-cv

arXiv:2602.02220v2 Announce Type: replace Abstract: Language-conditioned goal navigation (LGN) requires agents to locate user-specified targets without step-by-step guidance. However, existing benchma

Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task

arXiv:2605.31480v1 Announce Type: new Abstract: Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about se

Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions

model-releasesarxiv-cs-ai

arXiv:2605.31586v1 Announce Type: cross Abstract: Grasping the semantics of rare constructions (form-meaning pairings) has been shown to be a challenging problem that has currently only been solved by