Search: “pretraining”

SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving

arXiv:2512.10719v2 Announce Type: replace Abstract: End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual under

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Model Releases

Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL

DGX agent

arXiv:2605.22217v1 Announce Type: cross Abstract: Self-play reinforcement learning trains language models on their own generated tasks, co-evolving a proposer and solver without human labels. Recent s

model-releasesarxiv-cs-cl

22 May 2026

Thermo-VL: Extending Vision-Language Models to Thermal Infrared Perception

arXiv:2605.21882v1 Announce Type: new Abstract: Vision-language models (VLMs) often fail under low illumination because their visual grounding is learned predominantly from RGB imagery, whereas therma

Translating Signals to Languages for sEMG-Based Activity Recognition

arXiv:2605.22403v1 Announce Type: new Abstract: Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG

Understanding Data Temporality Impact on Large Language Models Pre-training

arXiv:2605.22769v1 Announce Type: new Abstract: Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal groun

Unifying Masked Diffusion Models with Various Generation Orders and Beyond

arXiv:2602.02112v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) are a potential alternative to autoregressive models (ARMs) for language generation, but generation quality dep

safetyarxiv-cs-cl

AnimeAdapter: Fine-grained and Consistent Zero-shot Anime Character Generation

arXiv:2605.20237v1 Announce Type: new Abstract: We present a lightweight appearance adapter for Stable Diffusion that enables controllable and consistent anime character generation under diverse editi

Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models

arXiv:2605.20915v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data from a model while preserving reliable behavior on the remaining data, making

Comparative Evaluation of Deep Learning Models for Fake Image Detection

arXiv:2605.20971v1 Announce Type: new Abstract: The growing sophistication of GAN-based image manipulation presents significant challenges for digital forensics. This study compares the performance of

safetyarxiv-cs-cv

DIVE: Embedding Compression via Self-Limiting Gradient Updates

arXiv:2605.20689v1 Announce Type: new Abstract: High-dimensional embeddings from large language models impose significant storage and computational costs on vector search systems. Recent embedding com

Local Ai

DNACHUNKER: Learnable Tokenization for DNA Language Models

arXiv:2601.03019v4 Announce Type: replace-cross Abstract: DNA language models are increasingly used to represent genomic sequence, yet their effectiveness depends critically on how raw nucleotides are

local-aiarxiv-cs-cl

Local Ai

End-to-End Unmixing with Material Prompts for Hyperspectral Object Tracking

arXiv:2605.20569v1 Announce Type: new Abstract: Hyperspectral imagery encodes rich material properties that can improve tracking robustness under appearance ambiguity, illumination change, and backgro

local-aiarxiv-cs-cv

HADS-Net:A Hybrid Attention-Augmented Dual-Stream Network with Physics-Informed Augmentation for Breast Ultrasound Image Classification

arXiv:2605.20536v1 Announce Type: new Abstract: Accurate classification of breast ultrasound images into benign, malignant, and normal categories is a critical clinical task complicated by speckle noi

HAPS: Rethinking Image Similarity for Virtual Staining

arXiv:2605.20362v1 Announce Type: new Abstract: Virtual staining of histopathology images (e.g., H&E-IHC) is an emerging tool in digital pathology, enabling faster and cheaper workflows by synthesizin

Inference Time Policy Optimization for Offline RL with Differentiable World Models

arXiv:2603.22430v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without f

safetyarxiv-cs-lg

Hardware

Instant GPU Efficiency Visibility at Fleet Scale

arXiv:2605.20799v1 Announce Type: cross Abstract: We present Overall FLOP Utilization (OFU), a hardware-level, precision-agnostic GPU efficiency metric for AI workloads on HPC systems, derived from tw

hardwarearxiv-cs-lg

Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory

arXiv:2605.20948v1 Announce Type: new Abstract: Scaling conditional memory offers a promising way to increase language-model capacity, but existing methods such as Engram learn large memory tables fro

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor

arXiv:2605.20798v1 Announce Type: cross Abstract: Narang et al. (2021) evaluated 40+ Transformer modifications at T5-base scale and concluded that most did not transfer. Five years later, the typical

researcharxiv-cs-cl

Neural Collapse by Design: Learning Class Prototypes on the Hypersphere

arXiv:2605.20302v1 Announce Type: cross Abstract: Supervised classification has a theoretical optimum, Neural Collapse (NC), yet neither of its two dominant paradigms reaches it in practice. Cross ent

safetyarxiv-cs-cv

Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

arXiv:2605.20187v1 Announce Type: new Abstract: Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these mode

Agents

ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning

arXiv:2605.20342v1 Announce Type: new Abstract: Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promisin

agentsarxiv-cs-cv

Rethinking Cross-Layer Information Routing in Diffusion Transformers

arXiv:2605.20708v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization,

safetyarxiv-cs-cv

Local Ai

Semantic Granularity Navigation in Image Editing

arXiv:2605.21190v1 Announce Type: new Abstract: Despite the generative capabilities of diffusion and flow models, real-image editing remains constrained by a persistent trade-off between semantic edit

local-aiarxiv-cs-cv

SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass

arXiv:2602.06358v2 Announce Type: replace Abstract: We propose SHINE (Scalable Hyper In-context NEtwork), a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapte

SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning

arXiv:2605.21147v1 Announce Type: cross Abstract: As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large langua

Spectral Unforgetting: Post-Hoc Recovery of Damaged Capabilities Without Retraining

model-releasesarxiv-cs-lg

arXiv:2605.20296v1 Announce Type: new Abstract: Fine-tuning a language model for a target task routinely degrades capabilities the training data never explicitly threatened. We study this phenomenon,

STEAM: A Training-Free Congestion-Aware Enhancement Framework for Decentralized Multi-Agent Path Finding

arXiv:2605.20929v1 Announce Type: new Abstract: We propose STEAM (Spatial, Temporal, and Emergent congestion Awareness for MAPF), a training-free test-time enhancement framework for learning-based dec

safetyarxiv-cs-ro

Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models

arXiv:2605.20356v1 Announce Type: new Abstract: Full-duplex spoken dialogue models (SDMs) can listen and speak simultaneously, enabling interaction dynamics closer to human conversation than turn-base

safetyarxiv-cs-cl

TriForces: Augmenting Atomistic GNNs for Transferable Representations

arXiv:2605.20581v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in p

VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026

arXiv:2605.20901v1 Announce Type: new Abstract: We propose VISTA, a V-JEPA Integrated StillFast Temporal Anticipator for the Ego4D Short-Term Object Interaction Anticipation (STA) Challenge at EgoVis

Applications

WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems

arXiv:2603.14392v2 Announce Type: replace Abstract: Trajectory world models play a crucial role in robotic dynamics learning, planning, and control. While recent works have explored trajectory world m

applicationsarxiv-cs-lg

WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents

arXiv:2605.20306v1 Announce Type: new Abstract: We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous

Awakening the Hydra: Stabilizing Multi-Concept Backdoor Injection in Text-to-Image Diffusion Models

arXiv:2605.19698v1 Announce Type: cross Abstract: Text-to-image diffusion models are increasingly developed through open-source reuse and repeated downstream fine-tuning, where reused checkpoints are

Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment

arXiv:2605.20127v1 Announce Type: cross Abstract: Artificial vision models are often evaluated against the human visual cortex by measuring how accurately their internal representations predict brain

Tutorials

Character-Centered Dialogue Generation from Scene-Level Prompts

arXiv:2505.16819v4 Announce Type: replace Abstract: Recent advances in scene-based video generation enable coherent visual narratives from structured prompts, yet a key aspect of storytelling -- chara

tutorialsarxiv-cs-cv

CounterFlow: A Two-Phase Inference-Time Sampling for Counterfactual Video Foley Generation

arXiv:2605.18916v1 Announce Type: cross Abstract: We investigate Counterfactual Video Foley Generation, which aims to adopt a sound-source identity that contradicts the visual evidence while remaining

researcharxiv-cs-ai

D^3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market

model-releasesarxiv-cs-lg

arXiv:2605.20036v1 Announce Type: new Abstract: Ride-hailing platforms like DiDi Chuxing operate in highly dynamic environments where balancing driver supply and passenger demand is critical. Although

Applications

Data-Efficient Self-Supervised Algorithms for Fine-Grained Birdsong Analysis

arXiv:2511.12158v3 Announce Type: replace Abstract: Research in bioacoustics, neuroscience, and linguistics often uses birdsong as a proxy to acquire knowledge across diverse areas. This requires audi

applicationsarxiv-cs-lg

Delta Attention Residuals

arXiv:2605.18855v1 Announce Type: cross Abstract: Attention Residuals replace standard additive residual connections with learned softmax attention over previous layer outputs, enabling selective cros

Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning

arXiv:2605.18871v1 Announce Type: cross Abstract: When Large Language Models produce structured outputs such as travel plans, code solutions, or multi-step proofs, individual reasoning steps may appea

Dr.LLM: Dynamic Layer Routing in LLMs

arXiv:2510.12773v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and i

researcharxiv-cs-ai

Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification

arXiv:2605.19527v1 Announce Type: new Abstract: Occluded person re-identification focuses on matching partially visible pedestrians across multiple camera views. However, occlusions disrupt body-regio

DynaSTy: A Framework for SpatioTemporal Node Attribute Prediction in Dynamic Graphs

arXiv:2601.05391v2 Announce Type: replace Abstract: Accurate multistep forecasting of node-level attributes on dynamic graphs is critical for applications ranging from financial trust networks to biol

safetyarxiv-cs-lg

Fast Tensorization of Neural Networks via Slice-wise Feature Distillation

arXiv:2605.19842v1 Announce Type: new Abstract: We propose a scalable tensorization framework for neural network compression based on slice-wise feature distillation. Unlike conventional tensor decomp

FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration

arXiv:2605.20022v1 Announce Type: new Abstract: Speculative decoding accelerates memory-bound LLM inference without quality degradation by using a fast drafter to propose multiple candidate tokens and

researcharxiv-cs-cl

HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models

arXiv:2605.18795v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning of large language models, yet most variants target dense architectures. Mixture-o

LLM Benchmark Datasets Should Be Contamination-Resistant

arXiv:2605.19999v1 Announce Type: cross Abstract: Benchmark datasets are critical for reproducible, reliable, and discriminative evaluation of LLMs. However, recent studies reveal that many benchmark

LoRA vs. Full Fine-Tuning: A Theoretical Perspective

arXiv:2605.19018v1 Announce Type: new Abstract: Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning