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Supervised Distributional Reduction via Optimal Transport and Dependence Maximization

arXiv:2605.27619v1 Announce Type: cross Abstract: Learning representations that capture both intrinsic data geometry and target-relevant structure remains a fundamental challenge, particularly in sett

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Supervised Semantic Differential for Cross-Cultural Concept Analysis: A Case Study of Human Affect

DGX agent

arXiv:2605.28225v1 Announce Type: new Abstract: Cross-cultural comparison of psychological meaning requires methods that go beyond word-level translation and examine how semantic dimensions are organi

safetyarxiv-cs-cl

28 May 2026

Surprising Performances of Students with Autism in Classroom with NAO Robot

arXiv:2407.12014v1 Announce Type: cross Abstract: Autism is a developmental disorder that manifests in early childhood and persists throughout life, profoundly affecting social behavior and hindering

applicationsarxiv-cs-ro

Surrogate modeling for Bayesian optimization beyond a single Gaussian process

arXiv:2205.14090v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions eme

applicationsarxiv-cs-lg

Hardware

SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks

arXiv:2605.28764v1 Announce Type: new Abstract: Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-a

hardwarearxiv-cs-ai

Syllabic-Structure Decoder for Automatic Speech Recognition in Vietnamese

arXiv:2605.27874v1 Announce Type: new Abstract: Most Automatic Speech Recognition (ASR) systems formulate transcription as a prediction problem over orthographic units such as characters, subwords, or

researcharxiv-cs-cl

SYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising

arXiv:2605.27790v1 Announce Type: new Abstract: Recent advances in large language models have accelerated open-vocabulary EEG-to-imagined-text decoding, where non-invasive neural activity recorded dur

safetyarxiv-cs-lg

Synthetic Emotions vs. Gamification: Exploring Engagement Strategies for Small Social Robots in Different Age Groups

arXiv:2605.27539v1 Announce Type: new Abstract: Many children experience challenges in emotional regulation and social interaction, which can limit their participation in everyday activities and thera

researcharxiv-cs-ro

Agents

SynthTools: A Framework for Scaling Synthetic Tools for Agent Development

arXiv:2511.09572v2 Announce Type: replace Abstract: For agentic systems to use external tools to solve complex, long-horizon tasks, we need a large set of diverse and controllable tool-use environment

agentsarxiv-cs-ai

T-GINEE: A Tensor-Based Multilayer Graph Representation Learning

arXiv:2605.28300v1 Announce Type: new Abstract: Traditional network analysis focuses on single-layer networks, real-world systems often form multilayer networks with multiple relationship types. Howev

applicationsarxiv-cs-lg

Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language

model-releasesarxiv-cs-ro

arXiv:2605.27886v1 Announce Type: new Abstract: Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exp

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

arXiv:2605.27431v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across dive

Tactile-Proprioceptive Sensor Fusion for Contact Wrench Estimation in Whole-Body Physical Human-Robot Interaction

arXiv:2605.28412v1 Announce Type: cross Abstract: Direct physical guidance is a natural means of teaching and interacting with robots, and robotic skins make a key contribution by enabling sensitive c

researcharxiv-cs-lg

TARQ: Tail-Aware Reconstruction Quantization for Rare-Word Robust Automatic Speech Recognition

arXiv:2605.27808v1 Announce Type: new Abstract: Data-aware post-training quantization (PTQ) minimizes a per-token reconstruction loss on a small calibration corpus, implicitly weighting positions by t

researcharxiv-cs-cl

TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems

arXiv:2605.27850v1 Announce Type: new Abstract: Effective multi-agent systems cannot be designed by selecting prompts or communication graphs in isolation. Agent behavior depends on the information an

Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning

arXiv:2605.28372v1 Announce Type: new Abstract: Imitation learning (IL) from a state-based reinforcement learning (RL) policy is a common approach to overcome the curse of dimensionality in complex an

safetyarxiv-cs-lg

Agents

Technical Report: Exploring the Emerging Threats of the Agent Skill Ecosystem

arXiv:2605.28588v1 Announce Type: cross Abstract: We analyzed 3,984 AI agent skills from major marketplaces and found 76 confirmed malicious payloads, including credential theft, backdoor installation

agentsarxiv-cs-ai

Tell Me a Story! Narrative-Driven XAI with Large Language Models

arXiv:2309.17057v3 Announce Type: replace Abstract: In many AI applications today, the predominance of black-box machine learning models, due to their typically higher accuracy, amplifies the need for

Temporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction

arXiv:2605.28155v1 Announce Type: new Abstract: Predicting Internet round-trip time (RTT) is critical for routing optimization, quality-of-service (QoS) provisioning, and traffic engineering, yet rema

applicationsarxiv-cs-lg

Tensor Memory: Fixed-Size Recurrent State for Long-Horizon Transformers

arXiv:2605.27686v1 Announce Type: cross Abstract: Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features,

Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms

arXiv:2605.27689v1 Announce Type: new Abstract: When machine learning systems under-perform for particular subgroups, affected users typically have no way to correct these disparities without relying

safetyarxiv-cs-lg

Text-Only Data Synthesis for Vision Language Model Training

arXiv:2503.22655v2 Announce Type: replace Abstract: Training vision-language models (VLMs) typically requires large-scale, high-quality image-text pairs, but collecting or synthesizing such data is co

The Abstraction Gap in Vision-Language Causal Reasoning

model-releasesarxiv-cs-cl

arXiv:2605.28779v1 Announce Type: new Abstract: Vision-language models (VLMs) generate fluent causal explanations, but current evaluations cannot distinguish linguistic plausibility from faithful caus

The Alignment Floor: When Persona Customization Is Safe

arXiv:2605.27382v1 Announce Type: cross Abstract: A key promise of pluralistic AI is behavioral adaptation: persona prompts like 'be creative' or 'be thorough' let systems respect diverse user values

The Attentional White Bear Effect in Transformer Language Models

arXiv:2605.28639v1 Announce Type: cross Abstract: Instruction-based suppression is widely used to prevent language models from generating prohibited content, yet it remains unclear whether suppression

The Cases LJP Never Sees: Prosecution Decision Prediction for More Complete Criminal Liability Assessment

arXiv:2605.28464v1 Announce Type: cross Abstract: Legal Judgment Prediction (LJP) has become a core benchmark for evaluating AI in the criminal legal domain, but it only sees criminal cases that have

The Computational Boundary of Inference: Capability Internalization, Training, and the Turing Jump

arXiv:2605.27381v1 Announce Type: cross Abstract: Claims about recursive self-improvement in AI often slide from repeated internal revision to the possibility of qualitatively stronger capability with

The Decision to Verify: How Warmth and User Characteristics Shape Reliance on Conversational Agents for Information Search

arXiv:2605.28498v1 Announce Type: cross Abstract: Conversational artificial intelligence (AI) provides an efficient and convenient gateway to information access. However, it can cause overreliance whe

Local Ai

The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution

arXiv:2605.27599v1 Announce Type: cross Abstract: Agentic AI workloads - where a single user goal triggers multi-step orchestration, tool calls, retries, and failure recovery - are being targeted for

local-aiarxiv-cs-ai

The Ethics of LLM Sandbox and Persona Dynamics

arXiv:2605.28647v1 Announce Type: new Abstract: It is well known that LLM guardrails and trained persona dynamics can produce a reality gap: the distance between the world a LLM is permitted or shaped

The Fragility of Chain-of-Thought Monitoring Across Typologically Diverse Languages

arXiv:2605.27901v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring has been proposed as a promising safety mechanism for detecting misaligned behavior in large language models. Howeve

The Fundamental Limits of Fraud Detection in Card Payment Networks

arXiv:2605.27557v1 Announce Type: new Abstract: Card payment fraud detection is usually framed as a supervised classification problem. Although this approach has generated practical progress, improvem

researcharxiv-cs-lg

The Future of Facts: Tracing the Factual Generation-Verification Gap

arXiv:2605.27564v1 Announce Type: cross Abstract: Language models are becoming the default interface to factual knowledge, yet they often verify outputs more reliably than they generate them. This gen

The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models

arXiv:2601.19926v2 Announce Type: replace-cross Abstract: We present a systematic review of 337 articles evaluating the syntactic abilities of Transformer-based language models (TLMs), reporting on ov

The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness

model-releasesarxiv-cs-cl

arXiv:2605.28190v1 Announce Type: new Abstract: Embedding benchmarks like MTEB report a single score per model, implicitly treating robustness as a static, scalar property. We argue that embedding rob

The Illusion of Opting in AI-Mediated Consequential Decisions

arXiv:2605.28210v1 Announce Type: new Abstract: Drawing on Ullmann-Margalit's concept of opting (transformative, irrevocable, and shadowed by foreclosed alternatives), we show that current AI systems

The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic

arXiv:2605.28700v1 Announce Type: new Abstract: The GSM-Symbolic benchmark (Mirzadeh et al., 2025) reported consistent performance drops across 25 Large Language Models (LLMs) when tested on template-

The Missing Piece in Pre-trained Model Evaluation: Reward-Guided Decoding Unlocks Task-Oriented Behavior Without Parameter Updates

model-releasesarxiv-cs-cl

arXiv:2605.28020v1 Announce Type: new Abstract: With the rapid progress of large language models (LLMs), reliably evaluating the capabilities of pre-trained LLMs has become increasingly important. The

The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes

arXiv:2602.15515v2 Announce Type: replace-cross Abstract: Training against white-box deception detectors has been proposed as a way to make AI systems honest. However, such training risks models learn

Agents

The Optimal Sample Complexity of Linear Contracts

arXiv:2601.01496v2 Announce Type: replace-cross Abstract: In this paper, we settle the problem of learning optimal linear contracts from data in the offline setting, where agent types are drawn from a

agentsarxiv-cs-ai

The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? A Bias-Controlled Study

arXiv:2504.04540v2 Announce Type: replace-cross Abstract: 3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention. Despite some

Tutorials

The Principles of Diffusion Models

arXiv:2510.21890v2 Announce Type: replace-cross Abstract: This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse for

tutorialsarxiv-cs-ai

The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation

arXiv:2601.17737v3 Announce Type: replace-cross Abstract: Recent advances in video generation have produced models capable of synthesizing stunning visual content from simple text prompts. However, th

Local Ai

The Shape of Overthinking: Backtracking Bursts in Long Reasoning Traces

arXiv:2605.27965v1 Announce Type: new Abstract: Reasoning models often generate long traces in which useful self-correction and unproductive revision are hard to distinguish. We study this distinction

local-aiarxiv-cs-ai

The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models

arXiv:2510.20665v3 Announce Type: replace Abstract: Evaluating the quality of reasoning traces from large language models remains understudied, labor-intensive, and unreliable: current practice relies

The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling

arXiv:2602.14862v2 Announce Type: replace-cross Abstract: Temperature scaling is a simple method that allows to control the uncertainty of probabilistic models. It is mostly used in two contexts: impr

Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method

arXiv:2605.28594v1 Announce Type: cross Abstract: In this article, we present an improved version of the PULSE method (Partition function Unsupervised Learning Sampling and Evaluation) for estimating

Thinking as Compression: Your Reasoning Model is Secretly a Context Compressor

arXiv:2605.28713v1 Announce Type: new Abstract: Context compression aims to shorten long context inputs with minimal information loss for LLM inference acceleration. While existing methods have shown