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arXiv:2605.08717v1 Announce Type: cross Abstract: Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad

arXiv:2605.08263v1 Announce Type: cross Abstract: This work studies decentralized novelty detection with global false discovery rate (FDR) control across heterogeneous composite null distributions, wi

arXiv:2605.10738v1 Announce Type: cross Abstract: Decentralized collision avoidance remains challenging, particularly when agents do not communicate any information related to planned trajectories. Mo

arXiv:2605.10295v1 Announce Type: new Abstract: This paper aims to construct a linguistic resource of Korean Multiword Expressions for Feature-Based Sentiment Analysis (FBSA): DECO-MWE. Dealing with m

arXiv:2605.10933v1 Announce Type: cross Abstract: While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates sign

arXiv:2503.18273v3 Announce Type: replace Abstract: In recent years, Islamophobia has gained significant traction across Western societies, fueled by the rise of digital communication networks. This p

arXiv:2605.08942v1 Announce Type: new Abstract: Large language models (LLMs) increasingly exhibit behaviors suggesting awareness of their evaluation context, often adapting their reasoning strategies

arXiv:2605.08389v1 Announce Type: cross Abstract: Zero-shot composed image retrieval (ZS-CIR) retrieves a target image from a reference image and a text modification without human-annotated CIR triple

Deep Agents ship with durable execution out of the box: every agent step is checkpointed, so you get observability, fault tolerance, and human-in-the-loop for free. But as agents run longer and contex

arXiv:2605.10569v1 Announce Type: new Abstract: Deep learning has become the dominant approach for creating high capacity, scalable models across diverse data modalities. However, because these models

arXiv:2605.08218v1 Announce Type: cross Abstract: This paper proposes latent visualization by optimization (LVO), a mechanistic interpretability technique that extends feature visualization by optimiz

arXiv:2505.05740v3 Announce Type: replace Abstract: This paper introduces the first globally optimal algorithm for the empirical risk minimization problem of two-layer maxout and ReLU networks, i.e.,

Deep learning hit a wall. Neurosymbolic AI rescued it. 🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 symbolic tools, 5

arXiv:2605.09890v1 Announce Type: cross Abstract: Differentially private stochastic gradient descent (DP-SGD) is a standard approach to privacy-preserving learning based on per-example clipping, subsa

arXiv:2509.25646v2 Announce Type: replace Abstract: Learning operators from data is central to scientific machine learning. While DeepONets are widely used for their ability to handle complex domains,

arXiv:2508.06248v4 Announce Type: replace Abstract: The generalization of deepfake detectors to unseen manipulation techniques remains a challenge for practical deployment. Although many approaches ad

arXiv:2605.10364v1 Announce Type: new Abstract: Modeling uncertainty in heavy-tailed time series remains a critical challenge for deep probabilistic forecasting models, which often struggle to capture

arXiv:2605.10279v1 Announce Type: new Abstract: DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic s

arXiv:2605.10488v1 Announce Type: cross Abstract: Agent-compiled knowledge bases provide persistent external knowledge for large language model (LLM) agents in open-ended, knowledge-intensive downstre

arXiv:2605.10564v1 Announce Type: new Abstract: End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual rea

arXiv:2605.09679v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) and AI agents have made significant progress in learning to analyze and reason about clinical images. However, e

arXiv:2605.08442v1 Announce Type: cross Abstract: Persistent memory attacks against LLM agents achieve high attack success rates against open-source models. In these attacks, malicious instructions in

arXiv:2605.09586v1 Announce Type: new Abstract: World models for deformable objects should recover not only geometry and appearance, but also underlying physical dynamics, interaction grounding, and m

arXiv:2605.09628v1 Announce Type: new Abstract: Depth super-resolution (DSR) aims to recover a high-resolution (HR) depth map from its low-resolution (LR) counterpart. With color image guidance, this

Delighted to announce our 3rd world modeling workshop! After NYC and Montreal, we are now headed to Chicago! - August 31st to September 2nd - CfP and details on the website: https://wm-booth.org - @yl

arXiv:2605.08396v1 Announce Type: new Abstract: The increasing complexity of modern computational environments often burdens researchers with infrastructure management, authentication protocols, and c

arXiv:2605.09269v1 Announce Type: new Abstract: Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exp

arXiv:2605.08135v1 Announce Type: new Abstract: Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation (BP), but its effectiveness can degrade in deeper and more chall

arXiv:2605.09065v1 Announce Type: new Abstract: Scene graphs (SGs) represent objects and their relationships as structured graphs, enabling applications in image generation, robotics, and 3D understan

arXiv:2605.09222v1 Announce Type: cross Abstract: Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hinderi

arXiv:2604.01151v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that may evade standard forms of human o

arXiv:2512.06673v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are rapidly expanding from general video understanding to finer-grained understanding such as spatio-tempor

arXiv:2605.08794v1 Announce Type: cross Abstract: Modern generative models can be understood as probability transport from a simple base distribution to a target data distribution. Deterministic trans

arXiv:2603.08065v2 Announce Type: replace-cross Abstract: Structured pruning reduces LLM inference cost by removing low-importance architectural components. This can be viewed as learning a multiplica

arXiv:2605.10190v1 Announce Type: new Abstract: Open-vocabulary object detection (OVOD) aims to detect both seen and unseen categories, yet existing methods often struggle to generalize to novel objec

arXiv:2605.10184v1 Announce Type: cross Abstract: We develop a foundation model using 1.2m high resolution satellite images of the Netherlands. By combining a Convolutional Neural Network and a Vision

arXiv:2507.01008v3 Announce Type: replace Abstract: Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, these end-effectors are often paired with bulky

arXiv:2605.09291v1 Announce Type: new Abstract: Discrete flow models (DFMs) are a class of flexible generative models for generating discrete data, and diffusion large language models (dLLMs) can be v

arXiv:2605.10863v1 Announce Type: new Abstract: Although Large Language Models (LLMs) have made remarkable progress, current preference optimization methods still struggle to align directional consist

arXiv:2605.09028v1 Announce Type: new Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perfor

arXiv:2605.08286v1 Announce Type: cross Abstract: We introduce a spectral-injection diagnostic for measuring which angular frequencies a trained equivariant force-field backbone preserves: inject a co

arXiv:2605.08614v1 Announce Type: new Abstract: Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perfor

Did Jensen Huang catch conflict of interest disease from Sam? HUANG FOUNDATION SIGNS GPU COMPUTE DEAL WITH COREWEAVE $NVDA proxy says the charitable foundation tied to Jensen and Lori Huang entered an

arXiv:2605.09275v1 Announce Type: new Abstract: Direct diffusion modeling of high-resolution spatiotemporal fields is computationally challenging. Parameter-efficient primitives address this by repres

arXiv:2605.08568v1 Announce Type: new Abstract: Large language models (LLMs) have rapidly grown in scale, creating substantial memory and computational costs that hinder efficient deployment. Singular

arXiv:2502.06044v3 Announce Type: replace-cross Abstract: Hyperparameter tuning is a key component of machine learning procedures, but when validation data contain sensitive user information, search m