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Slide Deck Q&A Quality Assurance App: A Multi-Stage Pipeline for Pedagogical Question Generation

arXiv:2605.26428v1 Announce Type: new Abstract: Generating high-quality, pedagogically useful questions from lecture slide decks is difficult because important instructional content is distributed acr

researcharxiv-cs-cl

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Small Object Detection in Industrial Recycling: A New Dataset and YOLO Performance Evaluation

DGX agent

arXiv:2605.26884v1 Announce Type: new Abstract: In this paper, we address the problem of detecting small, dense, and overlapping objects, a major challenge in computer vision. Our focus is on reviewin

researcharxiv-cs-cv

27 May 2026

SoftCap: Soft-Budget Control for Diffusion Transformer Acceleration

arXiv:2605.27075v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve strong visual quality, but their iterative denoising process requires many costly Transformer evaluations. Trainin

SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning

arXiv:2603.28730v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown impressive capabilities across diverse tasks, motivating efforts to leverage these models to supervis

SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens

arXiv:2508.05305v2 Announce Type: replace Abstract: The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-s

Local Ai

Source-Free Domain Adaptation for Geospatial Point Cloud Semantic Segmentation

arXiv:2601.08375v2 Announce Type: replace Abstract: Semantic segmentation of 3D geospatial point clouds is fundamental to remote sensing applications, yet domain shifts caused by regional and acquisit

local-aiarxiv-cs-cv

SpaceVista: All-Scale Visual Spatial Reasoning from mm to km

arXiv:2510.09606v2 Announce Type: replace Abstract: With the current surge in spatial reasoning explorations, researchers have made significant progress in understanding indoor scenes, but still strug

Sparse-LiDAR Prompting of Monocular Geometry Foundations: An Empirical Study Toward Long-Range Driving Depth

arXiv:2605.26456v1 Announce Type: new Abstract: Sparse-LiDAR-prompted depth foundation models (PromptDA, Prior Depth Anything, DMD3C) have shown strong results on indoor scenes or within KITTI's stand

SpatialBench: Is Your Spatial Foundation Model an All-Round Player?

arXiv:2605.27367v1 Announce Type: new Abstract: While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round pla

SPEAR: Code-Augmented Agentic Prompt Optimization

arXiv:2605.26275v1 Announce Type: new Abstract: Automatic prompt engineering (APE) rewrites prompts to improve downstream task performance, but existing APE loops treat the optimizer itself as a fixed

agentsarxiv-cs-cl

Spectral Principal Paths: A Spectral Perspective on Linear Representation Formation in LLMs

arXiv:2506.08543v3 Announce Type: replace Abstract: High-level representations have become a central focus in enhancing AI transparency and control, shifting attention from individual neurons or circu

safetyarxiv-cs-cv

Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training

arXiv:2605.26606v1 Announce Type: cross Abstract: Reinforcement learning (RL) is the dominant paradigm for post-training large language models. However, in the online, on-policy setting, rollout gener

safetyarxiv-cs-ai

SPHERE-JEPA: Spherical Prediction with Homogeneous Embeddings

arXiv:2605.26900v1 Announce Type: new Abstract: A fundamental open question in self-supervised learning (SSL) is the explicit characterization of the optimal geometry of the learned representations. R

researcharxiv-cs-lg

SQARL: A Size-Agnostic Reinforcement Learning approach for Circuit Allocation in Distributed Quantum Architectures

arXiv:2605.27027v1 Announce Type: new Abstract: The scaling of quantum processors is currently limited by technical challenges such as decoherence and cross-talk. As the number of qubits grows, interf

safetyarxiv-cs-lg

Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling

arXiv:2604.18103v2 Announce Type: replace Abstract: Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long-context set

Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models

arXiv:2605.26733v1 Announce Type: cross Abstract: Looped Language Models (LoopLMs) enable efficient latent reasoning through depth recurrence, yet exhibit unreliable test-time scaling behavior: perfor

Stateful Inference for Low-Latency Multi-Agent Tool Calling

arXiv:2605.26289v1 Announce Type: new Abstract: Multi-agent tool calling is becoming the dominant interaction pattern for LLM-based systems, yet existing inference frameworks treat each tool call as a

agentsarxiv-cs-lg

SteelDS: A High-Resolution Video Dataset of E40 Steel Scrap for Object Detection and Instance Segmentation

arXiv:2605.26682v1 Announce Type: cross Abstract: This dataset provides high-resolution, annotated video sequences of shredded E40-grade steel and copper scrap on a conveyor belt. Captured in a contro

Step-Size Stability in Stochastic Optimization: A Theoretical Perspective

arXiv:2602.09842v2 Announce Type: replace-cross Abstract: We present a theoretical analysis of stochastic optimization methods in terms of their sensitivity with respect to the step size. We identify

researcharxiv-cs-lg

StepOPSD: Step-Aware Online Preference Distillation for Agent Reinforcement Learning

arXiv:2605.27140v1 Announce Type: new Abstract: Reinforcement learning for multi-turn agents suffers from a credit-assignment mismatch: rewards are sparse and trajectory-level, while success often hin

safetyarxiv-cs-ai

Stochastic Decision Horizons for Constrained Reinforcement Learning

arXiv:2602.04599v2 Announce Type: replace Abstract: We propose stochastic decision horizons (SDH), a theoretically grounded framework for solving constrained RL problems with every-step constraint sat

safetyarxiv-cs-lg

Stochastic global optimization of continuous functions via random walks on Grassmannians

model-releasesarxiv-cs-lg

arXiv:2605.14151v1 Announce Type: cross Abstract: We introduce a stochastic global optimization method based on random walks on Grassmannian manifolds. To minimize a continuous objective ell:R^drighta

Strategies for Guiding LLMs to Use Software Design Patterns: A Case of Singleton

arXiv:2605.26898v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate functional source code from natural-language prompts, but often fail to consistently follow higher-level arc

Local Ai

StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting

arXiv:2605.26523v1 Announce Type: cross Abstract: Large-batch Contrastive Learning (CL), the foundation of modern representation learning, is fundamentally incompatible with the volatile resource cons

local-aiarxiv-cs-ai

Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing

arXiv:2605.26429v1 Announce Type: cross Abstract: This paper addresses structured out-of-distribution (OOD) testing in high-stakes machine learning applications. Traditional conformal methods rely on

Structured Relational Reasoning for Group Activity Assessment

arXiv:2508.07996v2 Announce Type: replace Abstract: Group Activity Detection (GAD) involves recognizing social groups and their collective behaviors in videos. Vision Foundation Models (VFMs), like DI

Stylistic Evolution and LLM Neutrality in Singlish Language

arXiv:2601.06580v2 Announce Type: replace Abstract: Singlish is a creole rooted in Singapore's multilingual environment that continues to evolve alongside social and technological change. We examine d

researcharxiv-cs-cl

SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking

arXiv:2511.04711v2 Announce Type: replace-cross Abstract: Large-scale vision-language models, especially CLIP, have demonstrated remarkable performance across diverse downstream tasks. Soft prompts, a

SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution

arXiv:2603.01327v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strong performance on self-contained programming tasks. However, they still struggle with repository-leve

Symbolic Regression via Latent Iterative Refinement

arXiv:2605.27245v1 Announce Type: new Abstract: Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder

researcharxiv-cs-lg

Synergetic Empowerment: Wireless Communications Meets Embodied Intelligence

arXiv:2509.10481v2 Announce Type: replace-cross Abstract: Wireless communication is evolving into an agent era, where large-scale agents with inherent embodied intelligence are not just users but acti

agentsarxiv-cs-ro

TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews

arXiv:2605.26911v1 Announce Type: new Abstract: LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well

TAG: Tangential Amplifying Guidance for Hallucination-Resistant Sampling

arXiv:2510.04533v2 Announce Type: replace Abstract: Diffusion models achieve state-of-the-art image generation but often produce semantic inconsistencies, or hallucinations. Existing inference-time gu

TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment

arXiv:2601.05729v2 Announce Type: replace Abstract: Recent studies have demonstrated the efficacy of integrating Group Relative Policy Optimization (GRPO) into flow matching models, particularly for t

safetyarxiv-cs-cv

Tail-Aware HiFloat4: W4A4 Post-Training Quantization for Wan2.2

arXiv:2605.26628v1 Announce Type: new Abstract: This report describes Tail-Aware HiFloat4, our submission to the low-bit text-to-video generation quantization challenge. Our method adapts the public V

TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection

arXiv:2504.02775v2 Announce Type: replace Abstract: We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective r

Targeted Remasking: Replacing Token Editing with Token-to-Mask Refinement in Discrete Diffusion Language Models

arXiv:2605.26436v1 Announce Type: cross Abstract: Discrete masked diffusion language models such as LLaDA generate text through iterative denoising, where mask tokens are progressively replaced with p

TCBiRRT: Rapid Motion Planning for Tightly Coupled Dual-arm Space Manipulator Using Task-space Random Expansion

arXiv:2605.27167v1 Announce Type: new Abstract: Planning the motion path for a tightly coupled dual-arm space manipulator under closed-chain constraints is a fundamental yet challenging problem in on-

researcharxiv-cs-ro

Applications

TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph

arXiv:2605.26984v1 Announce Type: new Abstract: Tax evasion causes severe losses of government revenues and disturbs the economic order of fair competition. To help alleviate this problem, the latest

applicationsarxiv-cs-lg

Telenor Nordics Customer Service self-help corpus

arXiv:2605.26891v1 Announce Type: new Abstract: This paper presents a multilingual customer service self-help corpus comprising 1,122 manually validated documents in Finnish, Danish, Norwegian, and Sw

agentsarxiv-cs-cl

Temporal Simultaneity Predicts Annotation Quality in Sentiment Corpora

arXiv:2605.27239v1 Announce Type: new Abstract: Annotation quality is difficult to sustain when campaigns span weeks or months with small annotator pools. We present a Setswana sentiment dataset of 3,

Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking

arXiv:2605.25538v2 Announce Type: replace Abstract: Track materialization converts raw video into reusable object tracks that downstream queries can run against without rerunning tracking, but extract

The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance

arXiv:2601.07085v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding mi

safetyarxiv-cs-ai

The Attribution Blind Spot: Detecting When Language Models Rely on Memory Rather Than Retrieved Context

arXiv:2605.26778v1 Announce Type: new Abstract: Retrieval-augmented generation promises to ground language model outputs in external evidence, yet the field has no reliable way to verify whether retri

The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works

model-releasesarxiv-cs-lg

arXiv:2605.26246v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers knowledge from a large teacher model to a smaller student. In language modeling, the student is trained either on

The Compressive Knowledge Graph Hypothesis: Which Graph Facts Matter for Scientific Hypothesis Generation?

arXiv:2605.27176v1 Announce Type: new Abstract: Knowledge graphs (KGs) can provide structured scientific context to language models, but it remains unclear which graph facts actually shape the generat

Local Ai

The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models

arXiv:2605.26128v1 Announce Type: new Abstract: Production LLM systems increasingly require machine-readable outputs: JSON objects, typed traces, regex-constrained fields, and tool-call schemas. This

local-aiarxiv-cs-lg

The Coverage Illusion: From Pre-retrieval Routing Failure to Post-retrieval Cascades in a Production RAG System

arXiv:2605.27220v1 Announce Type: new Abstract: In modern RAG pipelines, query augmentation methods such as HyDE and query expansion are applied to every query, resulting in substantial LLM inference

safetyarxiv-cs-cl