Search: “inference”

Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

arXiv:2605.03849v1 Announce Type: new Abstract: Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching

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TACO: Trajectory Aligning Cross-view Optimisation

DGX agent

arXiv:2605.03315v1 Announce Type: new Abstract: Cross-View Geo-localisation (CVGL) matches ground imagery against satellite tiles to give absolute position fixes, an alternative to GNSS where signals

local-aiarxiv-cs-cv

6 May 2026

The Detector Teaches Itself: Lightweight Self-Supervised Adaptation for Open-Vocabulary Object Detection

arXiv:2605.03642v1 Announce Type: new Abstract: Open-vocabulary object detection aims to recognize objects from an open set of categories, which leverages vision-language models (VLMs) pre-trained on

local-aiarxiv-cs-cv

Toward a Science of Intent: Closure Gaps and Delegation Envelopes for Open-World AI Agents

model-releasesarxiv-cs-ai

arXiv:2604.25000v2 Announce Type: replace Abstract: Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems w

Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection

arXiv:2605.02958v1 Announce Type: cross Abstract: Representation Engineering typically relies on static refusal vectors derived from terminal representations. We move beyond this paradigm, demonstrati

local-aiarxiv-cs-cl

Unified Multimodal Visual Tracking with Dual Mixture-of-Experts

arXiv:2605.03716v1 Announce Type: new Abstract: Multimodal visual object tracking can be divided into to several kinds of tasks (e.g. RGB and RGB+X tracking), based on the input modality. Existing met

Universal Smoothness via Bernstein Polynomials: A Constructive Approximation Approach for Activation Functions

arXiv:2605.02591v1 Announce Type: new Abstract: The efficacy of deep neural networks is heavily reliant on the design of non-linear activation functions, yet existing approaches often struggle to bala

researcharxiv-cs-ai

Valley3: Scaling Omni Foundation Models for E-commerce

model-releasesarxiv-cs-ai

arXiv:2605.01278v1 Announce Type: new Abstract: In this work, we present Valley3, an omni multimodal large language model (MLLM) developed for diverse global e-commerce tasks, with unified understandi

Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms

arXiv:2603.28489v2 Announce Type: cross Abstract: The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as pot

agentsarxiv-cs-cv

Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition

arXiv:2605.02920v1 Announce Type: cross Abstract: Standard transformer architectures learn fixed slow-weight representations during training and lack mechanisms for rapid adaptation within an episode.

A Meta Reinforcement Learning Approach to Goals-Based Wealth Management

arXiv:2605.02300v1 Announce Type: new Abstract: Applying concepts related to zero-shot meta-learning and pre-training of foundation models, we develop a meta reinforcement learning approach (denoted M

A Multimodal Dataset for Visually Grounded Ambiguity in Machine Translation

arXiv:2605.02035v1 Announce Type: new Abstract: Ambiguity resolution is a key challenge in multimodal machine translation (MMT), where models must genuinely leverage visual input to map an ambiguous e

researcharxiv-cs-cl

Hardware

A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory

arXiv:2605.02525v1 Announce Type: new Abstract: Autonomous indoor mobile robots can navigate reliably to metric coordinates using established frameworks such as ROS 2 Navigation 2, yet they lack the a

hardwarearxiv-cs-ro

Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication

model-releasesarxiv-cs-cl

arXiv:2605.02472v1 Announce Type: new Abstract: Legal texts often contain computational legal clauses--provisions whose understanding requires complex logic. While frontier Large Reasoning Models (LRM

Act2See: Emergent Active Visual Perception for Video Reasoning

arXiv:2605.01657v1 Announce Type: new Abstract: Vision-Language Models (VLMs) typically rely on static initial frames for video reasoning, restricting their ability to incorporate essential dynamic in

Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis

arXiv:2605.01741v1 Announce Type: new Abstract: Cone Beam Computed Tomography (CBCT) is pivotal for 3D diagnostic imaging in dentistry. However, the development of robust AI models for volumetric anal

Tutorials

AlbumFill: Album-Guided Reasoning and Retrieval for Personalized Image Completion

arXiv:2605.02892v1 Announce Type: new Abstract: Personalized image completion aims to restore occluded regions in personal photos while preserving identity and appearance. Existing methods either rely

tutorialsarxiv-cs-cv

Anomaly-Preference Image Generation

arXiv:2605.02439v1 Announce Type: new Abstract: Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization. However, existing methods struggle to r

safetyarxiv-cs-cv

Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents

arXiv:2605.00136v1 Announce Type: new Abstract: Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we

agentsarxiv-cs-ai

Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation

arXiv:2601.18569v2 Announce Type: replace-cross Abstract: In this letter, we propose an Attention-Based Neural-Augmented Kalman Filter (AttenNKF) for state estimation in legged robots. Foot slip is a

safetyarxiv-cs-lg

CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation

arXiv:2605.02657v1 Announce Type: new Abstract: Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Des

Tutorials

CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets

arXiv:2401.04139v4 Announce Type: replace Abstract: Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but criti

tutorialsarxiv-cs-lg

CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction

arXiv:2605.01346v1 Announce Type: new Abstract: Standard selective prediction methods typically estimate uncertainty from the output of a single predictive branch. While effective for general uncertai

local-aiarxiv-cs-cv

CoFrGeNet: Continued Fraction Architectures for Language Generation

arXiv:2601.21766v3 Announce Type: replace Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new fun

researcharxiv-cs-cl

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay

arXiv:2605.01330v1 Announce Type: new Abstract: Low-bit quantization is a practical route for efficiently deploying vision Transformers, yet activation outliers complicate fully quantized deployment.

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Comparative Evaluation of Convolutional and Transformer-Based Detectors for Automated Weed Detection in Precision Agriculture

arXiv:2605.00908v1 Announce Type: new Abstract: This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in realistic

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

arXiv:2603.07615v2 Announce Type: replace-cross Abstract: Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixel

Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks

model-releasesarxiv-cs-lg

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

DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns

arXiv:2603.16969v2 Announce Type: replace-cross Abstract: This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent

safetyarxiv-cs-lg

Hardware

DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning

arXiv:2510.09883v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve state-of-the-art performance on challenging benchmarks by generating long chains of intermediate steps, but th

hardwarearxiv-cs-cl

Democratizing the medieval English legal tradition

model-releasesarxiv-cs-cl

arXiv:2605.00977v1 Announce Type: cross Abstract: The record of the beginning of the most widespread legal system in the world is contained in millions of pages of handwritten text. Most of the record

Detecting Adversarial Data via Provable Adversarial Noise Amplification

arXiv:2605.02109v1 Announce Type: new Abstract: The nonuniform and growing impact of adversarial noise across the layers of deep neural networks has been used in the literature, without a formal mathe

Differentiable Kernel Ridge Regression for Deep Learning Pipelines

arXiv:2605.02313v1 Announce Type: new Abstract: Deep neural networks dominate modern machine learning, while alternative function approximators remain comparatively underexplored at scale. In this wor

local-aiarxiv-cs-lg

Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis

arXiv:2605.01848v1 Announce Type: new Abstract: Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pat

Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching

arXiv:2605.00941v1 Announce Type: cross Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem. Existing

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

arXiv:2603.19675v2 Announce Type: replace Abstract: Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically

agentsarxiv-cs-cv

EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion

arXiv:2605.01916v1 Announce Type: new Abstract: Infrared-visible image fusion aims to create an information-rich fused image by integrating the complementary thermal saliency from infrared sensing and

agentsarxiv-cs-cv

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness

arXiv:2605.01024v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown prom

Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities

arXiv:2605.00960v1 Announce Type: cross Abstract: We introduce energy-based constraint networks -- a modality-agnostic architecture that learns structural coherence from contrastive pairs. The system

local-aiarxiv-cs-cl

Evidence-Based Landing Site Selection and Vison-Based Landing for UAVs in Unstructured Environments

arXiv:2605.01432v1 Announce Type: new Abstract: Autonomous landing in cluttered or unstructured environments remains a safety-critical challenge for unmanned aerial vehicles (UAVs), particularly under

safetyarxiv-cs-ro

Evolving Token Communication with Parametric Memory Network

arXiv:2605.01869v1 Announce Type: cross Abstract: Token communication has emerged as a promising framework for efficient wireless transmission by representing source data as compact semantic tokens. H

Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2508.15202v2 Announce Type: replace Abstract: Process Reward Models (PRMs) supervise intermediate reasoning steps in large language models (LLMs), but existing PRMs are mainly trained on general

Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection

arXiv:2605.01596v1 Announce Type: new Abstract: This paper describes the system submitted by team extbf{Archaeology} to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of

researcharxiv-cs-cl

FractalMamba++: Scaling Vision Mamba Across Resolutions via Hilbert Fractal Geometr

arXiv:2505.14062v3 Announce Type: replace Abstract: Vision Mamba offers linear complexity for long visual sequences, yet its performance depends critically on how a two-dimensional patch grid is seria

local-aiarxiv-cs-cv

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

arXiv:2605.01367v1 Announce Type: cross Abstract: High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlate

GD-FPS: Growth-Driven Feedforward Parameter Selection for Efficient Fine-Tuning

arXiv:2510.27359v2 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key strategy for adapting large-scale pre-trained models to downstream tasks, but existing a