AI News Archive: May 18, 2026 — Part 20
Sourced from 500+ daily AI sources, scored by relevance.
- BidBolt
Create, send, and track professional quotes powered by AI.
- ShivHit.ai — AI Consulting Platform
The world’s first AI‑powered consulting platform.
- SaathiMed
AI-powered health companion with longitudinal care tracking
- Mobile Vision Automation Framework
Zero-Root AI Android UI & Gaming Automation Pipeline
- linkedin photo AI
Turn any photo into a LinkedIn-ready headshot in seconds
- Spokio
Offline text-to-speech for Mac with local voice cloning
- The CEO guide to AI Transformation
Overcome the critical issues of AI business transformation
- Speak2Easy
Hear it. Say it. Perfect it. AI language pronunciation.
- Syndicate
Drop your idea. Let AI fight over it.
- Landing Doctor
Get an AI doctor's diagnosis for your landing page
- AIThesaurus.io
Multilingual AI thesaurus with integrated SEO search volume
- Cockpyt AI
Cockpyt AI est un outil de suivi de visibilité dans les IA
- Oylo
Live AI video transformation. No rendering. Just playing.
- Efficient Gradient Methods for Distributed Saddle Problems
The distributed setting for Saddle Problems (SPs) has recently emerged as a framework for various modern applications in machine learning and multiagent systems. Despite its relevance, the theoretical foundations of this setting have not yet been thoroughly established. In this paper, we advance thi...
- LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information. To address this, we propose LLM-driven Multi-Agent Communication (...
- Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior robust MARL methods have primarily considered value-oriented attacks, leaving a gap in robustness when interaction structur...
- Evaluating Multi-turn Human-AI Interaction
Large language models (LLMs) are increasingly used as collaborative assistants, yet dominant NLP evaluation practices remain centered on aggregate metrics such as accuracy and fluency. These approaches often overlook behaviors that are critical in human-facing settings (e.g., consistency across mult...
- The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration
Large Language Models (LLMs) are increasingly used in educational settings as interactive tools for collaboration. However, their tendency toward sycophancy, aligning with user beliefs even when incorrect, raises concerns for learning and decision-making, especially for less knowledgeable users. Thi...
- Distorted Perspectives of LLM-Simulated Preferences: Can AI Mislead Design?
Designers of digital solutions increasingly consult Large Language Models (LLMs) for their work. However, it remains unclear how this may affect the user experiences they produce and there are no established practices. We investigate how design preferences expressed by LLM-driven simulation methods ...
- What Would GPT Click: Practical Effects of Human-AI Behavioral Misalignment and the Cost of Synthetic Participants in User Experience
Synthetic participants represent a methodologically concerning concept that threatens the integrity of UX research. Findings from previous experiments specify how AI outputs are misaligned with the behaviors and thoughts of real humans in various ways. However, industry voices keep underestimating t...
- DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
Foundation models pre-trained through masked reconstruction on large-scale EEG data have emerged as a promising paradigm for learning generalizable neural representations across diverse brain-computer interface applications. However, a critical yet overlooked challenge is that EEG encoders must lear...
- MEEDAV: A Synchronous Web Viewer for EEG, Eye-Tracking and Speech Data
MEEDAV is an open-source web-based application for the synchronised visualisation of electroencephalography (EEG), eye-tracking, and audio data collected in psycholinguistic research. While originally developed for the Eyetracked Multi-Modal Translation (EMMT) corpus, which uses four-channel EEG dat...
- A Brief Overview: On-Policy Self-Distillation In Large Language Models
On-Policy Self-Distillation (OPSD) introduces a unified learning framework in which a single large language model simultaneously serves as both teacher and student. Unlike conventional knowledge distillation that relies on a separate, often larger teacher model, OPSD operates under different context...
- An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments
LLM-based chatbot agents increasingly process user requests by combining natural-language reasoning with external tools such as web browsing. These capabilities improve usability, but they also create attack surfaces when untrusted external content is processed as part of a user' s task. This paper ...
- PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows
Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficult to debug and refine. Failures can originate from subtle errors in intermediate outputs that propagate to downstream nodes, requiring developers to ...
- Contextualized Dynamic Explanations: A Vision
Asynchronous data-driven explanations often fail because the content and presentation are not tailored to the target audience, and they provide limited opportunities for active audience engagement. We present a vision for Contextualized Dynamic Explanations (CODEX), an agentic approach to dynamicall...
- In-Vehicle Human-Machine Interface to Support Drivers in Conditionally Automated Platooning
Vehicle platooning enables close-gap driving and offers potential benefits for traffic efficiency and safety. In conditionally automated platooning, drivers remain responsible for supervising the system and intervening when necessary, making effective Human-Machine Interfaces (HMIs) critical for mai...
- Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
Trust calibration -- aligning user trust judgment with model capability -- is crucial for safe deployment of explainable AI (XAI), yet is often evaluated via global trust ratings detached from objective performance evidence. We present a preregistered, incentivized between-subject online study (N=41...
- See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding
We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual prompts, such as masks or points, SWIM leverages mask supervis...
- Low Latency Gaze Tracking via Latent Optical Sensing
We present a real-time gaze tracking system that directly acquires task-relevant latent features using a fully passive optical encoder. Instead of forming and processing full-resolution images, our approach leverages a microlens array with a co-designed binary chromium mask to perform spatially mult...
- Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mixing of these chunk FCMs gives a representative cyclic FCM knowledge graph. The text chunks can have different levels of...
- Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study
Generative AI creates new opportunities for programming education, but many existing systems remain overly directive, producing lengthy explanations and premature solutions that can overwhelm K-12 novices. In this paper, we present a participatory design study of how an adaptive tutorial system, Soc...
- Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science
AI systems are becoming active participants in organizational and knowledge work. They increasingly interact with humans, coordinate workflows, and operate in multi-agent arrangements. Understanding their effects therefore requires more than measuring output accuracy; it requires evidence about mech...
- UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation
Manually annotating accurate 3D hand poses is extremely time-consuming and labor-intensive. Existing self-supervised hand pose estimation methods leverage the discrepancy between input images and rendered outputs, or multi-view consistency constraints, as the driving force to optimize networks and p...
- Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection
Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model. In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a governmen...
- Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control
Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when unauthorized tools are visible in an agent's context, models select them in adversarial scenarios -- even when explicitly instructed otherwise. We prop...
- Safety Geometry Collapse in Multimodal LLMs and Adaptive Drift Correction
Multimodal large language models (MLLMs) often fail to transfer safety capabilities learned in the text modality to semantically equivalent non-text inputs, revealing a persistent multimodal safety gap. We study this gap from a representation-geometric perspective by analyzing a text-aligned refusal...
- Babel: Jailbreaking Safety Attention via Obfuscation Distribution Optimized Sampling
Despite rigorous safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuristic templates or exhaustive trials, lacking mechanistic interpretability and query efficiency. In this study, we investigate an intrinsic vulnerabili...
- From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation
Machine-learning-based Intrusion Detection Systems (IDS) have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes ...
- Explainable Machine Learning for Phishing Detection on Heterogeneous Datasets with MCP-Enabled Deployment
With the growth in digital transformation and Internet usage, the Social Engineering techniques such as Phishing have become a major concern for the users and the organizations. Phishing attacks involve deceptive techniques to trick users into revealing confidential information that causes financial...
- Acoustic Interference: A New Paradigm Weaponizing Acoustic Latent Semantic for Universal Jailbreak against Large Audio Language Models
The integration of audio modality into Large Audio Language Models (LALMs) significantly expands their attack surface. Existing jailbreak paradigms predominantly treat audio as a carrier for malicious payloads, relying on semantic optimization, acoustic parameter control, or additive perturbation to...
- LivePI: More Realistic Benchmarking of Agents Against Indirect Prompt Injectio
AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI) risk: an agent may execute harmful instructions embedded in untrusted inputs such as email, downloaded files, webpages, repositories, or group-chat mess...
- Speed Kills: Exploring Confused Deputy Attacks Through Edge AI Accelerators
AI Accelerator (AIA) are specialized hardware e.g., Tensor Processing Unit (TPU), that enable optimal and efficient execution of AI applications and on-device inference. The growing demand for AI applications has led to the widespread adoption of AIAs on Edge or embedded devices on Edge or embedded ...
- Same Signal, Different Semantics: A Cross-Framework Behavioral Analysis of Software Engineering Agents
Behavioral studies of LLM-based software engineering agents extract operational rules about which trajectory shapes correlate with higher resolution rates: that a test step follows a code modification, that error cascades are short, or that trajectories are compact. Each rule is typically derived fr...
- CommitDistill: A Lightweight Knowledge-Centric Memory Layer for Software Repositories
Software repositories accumulate large amounts of unstructured knowledge in commit messages, pull-request discussions, and issue threads, but developers and AI coding assistants rarely reuse this history effectively. Recent work on typed-memory architectures for LLM agents (MemGPT, generative agents...
- Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby reducing the reliance on labor-intensive manual code audits. Recent advancements have shifted towards leveraging large language models (LLMs) for vulnerab...
- A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback
Large Language Models (LLMs) demonstrate strong potential for automated code generation, yet their ability to iteratively refine solutions using execution feedback remains underexplored. Competitive programming offers an ideal testbed for this investigation, as it demands end-to-end algorithmic reas...
- LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems
Production log analytics in self-hosted, resource-constrained environments requires natural-language access to massive log streams without the cost of routing every query through a large language model. We present LogRouter, an end-to-end log question-answering system deployed on TUBITAK BILGEM's na...
- BLAgent: Agentic RAG for File-Level Bug Localization
Bug localization remains a key bottleneck in downstream software maintenance tasks, including root cause analysis, triage, and automated program repair (APR), despite recent advances in large language model (LLM)-based repair systems. File-level bug localization is especially critical in hierarchica...
- Contextualized Code Pretraining for Code Generation
As code generation becomes increasingly central to improving software development efficiency, modern code models are largely trained and evaluated on code with natural-language descriptions. In real projects, developers often implement missing functions under limited project-specific artifacts, whil...