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SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg

safetyarxiv-cs-cl

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Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems

DGX agent

arXiv:2605.02526v1 Announce Type: cross Abstract: Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The exi

safetyarxiv-cs-ai

6 May 2026

Agents

Sheaf-Theoretic Planning: A Categorical Foundation for Resilient Multi-Agent Autonomous Systems

arXiv:2605.01879v1 Announce Type: new Abstract: The challenge of engineering autonomous agents capable of navigating the stochastic and adversarial nature of the physical world has historically reside

agentsarxiv-cs-ai

Local Ai

SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification

arXiv:2605.03301v1 Announce Type: new Abstract: De-identification of clinical text remains essential for secondary use of electronic health records (EHRs), yet public benchmarks such as i2b2 2006/2014

local-aiarxiv-cs-cl

Should We Still Pretrain Encoders with Masked Language Modeling?

arXiv:2507.00994v4 Announce Type: replace Abstract: Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked

researcharxiv-cs-cl

SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision

arXiv:2605.03846v1 Announce Type: new Abstract: Designing an open-world quadrupedal loco-manipulation system is highly challenging. Traditional reinforcement learning frameworks utilizing exteroceptio

safetyarxiv-cs-ro

Simulated Students in Tutoring Dialogues: Substance or Illusion?

model-releasesarxiv-cs-cl

arXiv:2601.04025v2 Announce Type: replace Abstract: Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires

Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization

arXiv:2512.16504v3 Announce Type: replace Abstract: The self-supervised pretraining paradigm has achieved great success in learning 3D action representations for skeleton-based action recognition usin

researcharxiv-cs-cv

SMoE: An Algorithm-System Co-Design for Pushing MoE to the Edge via Expert Substitution

arXiv:2508.18983v3 Announce Type: replace Abstract: The Mixture of Experts (MoE) architecture has emerged as a key technique for scaling Large Language Models by activating only a subset of experts pe

safetyarxiv-cs-ai

Agents

SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems

arXiv:2605.03842v1 Announce Type: cross Abstract: Robotic Mobile Fulfillment Systems (RMFS) rely on mobile robots for automated inventory transportation, coordinating order allocation and robot schedu

agentsarxiv-cs-ro

SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification

arXiv:2605.03371v1 Announce Type: new Abstract: Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning vari

safetyarxiv-cs-cv

Soft Tournament Equilibrium

model-releasesarxiv-cs-lg

arXiv:2604.04328v3 Announce Type: replace-cross Abstract: The evaluation of general-purpose artificial agents, particularly those based on LLMs, presents a significant challenge due to the non-transit

Sparse Data Tree Canopy Segmentation: Fine-Tuning Leading Pretrained Models on Only 150 Images

arXiv:2601.10931v2 Announce Type: replace Abstract: Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real

researcharxiv-cs-cv

Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning

model-releasesarxiv-cs-cl

arXiv:2605.03229v1 Announce Type: new Abstract: Adapting a pretrained language model to a new task often hurts the general capabilities it already had, a problem known as catastrophic forgetting. Spar

Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets

arXiv:2605.03874v1 Announce Type: new Abstract: Classification of EEG signals using shallow Convolutional Neural Networks (CNNs) is a prevalent and successful approach across a variety of fields. Most

Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach

arXiv:2605.01307v1 Announce Type: cross Abstract: This paper investigates coordinated downlink transmission in a multi-base station (multi-BS) multi-reconfigurable intelligent surface (multi-RIS)-assi

researcharxiv-cs-ai

Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation

arXiv:2510.24211v2 Announce Type: replace Abstract: Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrain

researcharxiv-cs-cv

Speculative Speculative Decoding

arXiv:2603.03251v3 Announce Type: replace Abstract: Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a

SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction

arXiv:2508.05691v3 Announce Type: replace-cross Abstract: Detecting the source model of AI-generated images is a growing accountability problem. AI fingerprinting techniques address this by detecting

Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection

model-releasesarxiv-cs-lg

arXiv:2605.03303v1 Announce Type: new Abstract: Graph unlearning remains a critical technique for supporting privacy-preserving and sustainable multimodal graph learning. However, we observe that exis

StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing

model-releasesarxiv-cs-lg

arXiv:2605.02904v1 Announce Type: new Abstract: We present StateSMix, a fully self-contained lossless compressor that couples an online-trained Mamba-style State Space Model (SSM) with sparse n-gram c

StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning

model-releasesarxiv-cs-cv

arXiv:2605.03927v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown remarkable performance in various robotic tasks, as they can perceive visual information and understand natural

Static Analysis of Recursive SHACL

arXiv:2605.02787v1 Announce Type: cross Abstract: SHACL (Shapes Constraint Language) expresses constraints on RDF data by means of so-called shapes. Its central service is validation: verifying whethe

researcharxiv-cs-ai

Steer Like the LLM: Activation Steering that Mimics Prompting

arXiv:2605.03907v1 Announce Type: new Abstract: Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform

researcharxiv-cs-cl

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

arXiv:2509.20102v2 Announce Type: cross Abstract: Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often

safetyarxiv-cs-ro

Local Ai

Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention

arXiv:2604.00754v2 Announce Type: replace Abstract: The whole-brain connectome of a fruit fly comprises over 130K neurons connected with a probability of merely 0.02%, yet achieves an average shortest

local-aiarxiv-cs-cl

Stochastic Schrodinger Diffusion Models for Pure-State Ensemble Generation

arXiv:2605.03573v1 Announce Type: cross Abstract: In quantum machine learning (QML), classical data are often encoded as quantum pure states and processed directly as quantum representations, motivati

Strategy-Aware Optimization Modeling with Reasoning LLMs

arXiv:2605.02545v1 Announce Type: new Abstract: Large language models (LLMs) can generate syntactically valid optimization programs, yet often struggle to reliably choose an effective modeling strateg

applicationsarxiv-cs-ai

StreakMind: AI detection and analysis of satellite streaks in astronomical images with automated database integration

arXiv:2605.03429v1 Announce Type: cross Abstract: Artificial satellites and space debris increasingly contaminate astronomical images, affecting scientific surveys and producing large volumes of strea

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

safetyarxiv-cs-cv

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid

arXiv:2605.01359v1 Announce Type: new Abstract: In this paper, we employ the Minimal Cognitive Grid (MCG), a framework created to evaluate the cognitive plausibility of artificial systems, to offer a

researcharxiv-cs-ai

Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges

arXiv:2605.02973v1 Announce Type: new Abstract: Modality translation is inherently under-constrained, as multiple cross-modal mappings may yield the same marginals. Recent work has shown that diffusio

safetyarxiv-cs-lg

Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition

arXiv:2505.08262v2 Announce Type: replace Abstract: We study the classical binary classification problem for hypothesis spaces of Deep Neural Networks (DNNs) under Tsybakov's low-noise condition with

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

model-releasesarxiv-cs-cl

arXiv:2605.03534v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, but retrieval is not verification: a passage can be topical and still fail t

Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions

arXiv:2605.03221v1 Announce Type: new Abstract: Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically u

applicationsarxiv-cs-cv

T^2PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning

arXiv:2605.02178v1 Announce Type: new Abstract: Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite

safetyarxiv-cs-ai

TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis

arXiv:2605.03944v1 Announce Type: new Abstract: Survival analysis on tabular data is a well-studied problem. However, existing deep learning methods are often highly task-specific, which can limit the

applicationsarxiv-cs-lg

Local Ai

TACO: Trajectory Aligning Cross-view Optimisation

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

Taking the GP Out of the Loop

arXiv:2506.12818v3 Announce Type: replace Abstract: Bayesian optimization (BO) has traditionally solved black-box problems where function evaluation is expensive and, therefore, observations are few.

Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation

arXiv:2605.01750v1 Announce Type: cross Abstract: Grounding is the collaborative process of establishing mutual belief sufficient for the current communicative purpose. While static grounding maps lan

safetyarxiv-cs-ai

Agents

Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation

arXiv:2605.03125v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) holds great potential but faces robustness challenges due to environmental uncertainty. To address this, distr

agentsarxiv-cs-lg

Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing

model-releasesarxiv-cs-cv

arXiv:2605.03909v1 Announce Type: cross Abstract: Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor conf

Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers

arXiv:2605.03780v1 Announce Type: cross Abstract: Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to

researcharxiv-cs-cl

TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations

arXiv:2605.03045v1 Announce Type: new Abstract: Causal Discovery (CD) is a powerful framework for scientific inquiry. Yet, its practical adoption is hindered by a reliance on strong, often unverifiabl

applicationsarxiv-cs-lg

TeamUp: Semantic Project Matching and Team Formation for Learning at Scale

arXiv:2605.03237v1 Announce Type: cross Abstract: Project-based learning improves student engagement and learning outcomes, yet allocating students to appropriately challenging projects while forming

safetyarxiv-cs-cl

Tempered Guided Diffusion

arXiv:2605.03712v1 Announce Type: cross Abstract: Training-free conditional diffusion provides a flexible alternative to task-specific conditional model training, but existing samplers often allocate

Tenability and Weak Semantics: Modeling Non-uniform Defense -- Extended Version

model-releasesarxiv-cs-ai

arXiv:2605.02024v1 Announce Type: new Abstract: In Dung-style abstract argumentation, various semantics capture notions of acceptability of arguments. The admissibility semantics capture the notion th

Hardware

Test-Time Training with KV Binding Is Secretly Linear Attention

arXiv:2602.21204v3 Announce Type: replace-cross Abstract: Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a

hardwarearxiv-cs-cv