AI News Archive: May 13, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- AudaBook
AI-powered WhatsApp receptionist for businesses
- Aura: Anxiety & Fear Relief
AI app for anxiety, overthinking & emotional support
- ALAN — AI Fitness Trainer
Your 24/7 AI fitness coach
- Axora
AI Automation for Modern Creators.
- BlackRabbit — Read in Any Language
Read anything. Understand everything.
- Adgine
Get Recommended by AI, Drive Global Business Growth.
- capllio.com
create captions, translate subtitle. audio , video to text
- Streamlit Proposal AI
AI agent that writes winning freelance proposals
- 3RD PRSN AI
Enterprise AI Infrastructure & Automation
- GrowthPILOT
AI content platform in 6 languages — half the price
- FloTorch
Build. Evaluate. Scale. Enterprise AI, redefined.
- TrendingTide
Hacker News trends with AI key takeaways
- EcomGeo
Optimize your e-commerce store for OpenAI Ads one dashboard.
- SevenTwelvez
Replace your agency with an autonomous creative engine
- POLYWINS
AI Trading Agents for Polymarket
- AI Analytics Platform | Aviras
Real-time AI-powered insights for smarter business decisions
- GenieTools
AI image tools — no signup, no uploads, no hassle
- Loop Interviewer
The Intelligent AI Voice Interviewer for LeetCode
- Prompt Mart
Prompt image generator App
- Professional Prompt Builder
Free AI Prompt Generator for Photographers
- OSS Guardian — Spec-Compliant AI Review
The AI that reviews your PRs like a senior maintainer, 24/7
- Decoupled Planning for Multiple Omega-Regular Objectives
We study the problem of generating paths on a graph that satisfy a collection of ω-regular objectives. We propose a decoupled framework in which each objective is assigned to an independent agent that selects a local policy, while a scheduler -- oblivious to the graph and objective -- dynamically co...
- When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling
Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benchmarks mostly evaluate task completion in weakly coupled environments, and provide limited support for studying coordination in shared, dynamically ev...
- Occlusion-Based Object Transportation Around Obstacles With a Swarm of Miniature Robots
Swarm robotics utilises decentralised self-organising systems to form complex collective behaviours built from the bottom-up using individuals that have limited capabilities. Previous work has shown that simple occlusion-based strategies can be effective in using swarm robotics for the task of trans...
- "Like Taking the Path of Least Resistance": Exploring the Impact of LLM Interaction on the Creative Process of Programming
Creativity is fundamentally human. As AI takes on more of the generative work that once required human imagination, despite documented limitations in creative ability, a critical question emerges: How does GenAI affect users' creativity? Through a within-subject study followed by retrospective inter...
- Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers
Measuring the creativity of large language models (LLMs) is essential for designing methods that can improve creativity and for enhancing our scientific understanding of this ability. To accomplish this, it has become common in recent years to administer tests of human creativity to LLMs. Although t...
- AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
Anti-facial recognition (AFR) image filters alter images in ways that are subtle to people but blinding to computer vision. Yet, despite widespread interest in these technologies to subvert surveillance, users rarely use them in practice -- because the ``subtle'' alterations are visible enough to co...
- Seed Bank, Co-op, Stoop Swap: Metaphors for Governing Language Model Data for Creative Writing
How might we govern a language model run for and by creative writers? While generative AI use is on the rise, many language models are created and owned in ways that limit writers' consent, participation, and control. We report on four workshops where over one hundred creative writers came up with a...
- Beyond Anthropomorphism: Exploring the Roles of Perceived Non-humanity and Structural Similarity in Deep Self-Disclosure Toward Generative AI
This study investigates deep self-disclosure toward generative AI by examining perceived non-humanity and structural similarity as psychological factors beyond anthropomorphism. Perceived non-humanity may reduce evaluation apprehension, whereas structural similarity refers to the perceived logical a...
- AI-Generated Slides: Are They Good? Can Students Tell?
As generative AI (GenAI) tools become easily accessible, there is promise in using such tools to support instructors. To that end, this paper examines using GenAI to help generate slides from instructor authored course notes, emphasizing instructor and student perceptions. We examine an end-to-end e...
- Doppler Prompting for Stable mmWave-based Human Pose Estimation
Millimeter-wave (mmWave) enables privacy-preserving, illumination-robust human pose estimation (HPE), with each mmWave frame represented as a range-angle-Doppler tensor, providing spatial magnitude for localization and Doppler signatures for motion-related cues. However, existing mmWave-based HPE me...
- Identifying AI Web Scrapers Using Canary Tokens
From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large language models (LLMs). However, large-scale web scraping to feed LLMs can affect site stability and raise legal, privacy, or ethics concerns. If website...
- Model-Agnostic Lifelong LLM Safety via Externalized Attack-Defense Co-Evolution
Large language models remain vulnerable to adversarial prompts that elicit harmful outputs. Existing safety paradigms typically couple red-teaming and post-training in a closed, policy-centric loop, causing attack discovery to suffer from rapid saturation and limiting the exposure of novel failure m...
- Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks
Recent cryptographic results establish that neural networks can be backdoored such that no efficient algorithm can distinguish them from a clean model. These guarantees, however, have been confined to stylised architectures of limited practical relevance, leaving open whether comparable undetectabil...
- Code-Centric Detection of Vulnerability-Fixing Commits: A Unified Benchmark and Empirical Study
Automated detection of vulnerability-fixing commits (VFCs) is critical for timely security patch deployment, as advisory databases lag patch releases by a median of 25 days and many fixes never receive advisories. We present a comprehensive evaluation of code language model based VFC detection throu...
- No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills
LLM-powered agents can silently delete documents, leak credentials, or transfer funds on a routine user request, not because the agent was attacked, but because the skill it invoked broke its own declared safety rules. We call these specification violations: benign inputs cause a skill to breach the...
- From Compression to Accountability: Harmless Copyright Protection for Dataset Distillation
Large-scale datasets have been a key driving force behind the rapid progress of deep learning, but their storage, computational, and energy costs have become increasingly prohibitive. Dataset distillation (DD) mitigates this problem by synthesizing compact yet informative datasets, thereby enabling ...
- Do Skill Descriptions Tell the Truth? Detecting Undisclosed Security Behaviors in Code-Backed LLM Skills
Programmatic skills in LLM ecosystems consist of a natural-language description and executable implementation files. Users and LLMs rely on the description to understand the skill's scope. However, the implementation may perform security-relevant operations, such as credential access, network commun...
- Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis
Large language models (LLMs) are increasingly deployed in a wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that circumvent their safety guardrails. Existing evaluation frameworks typically report binary success/failure metrics, failing to capture the temporal dyna...
- Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI
Embodied intelligent robots rely on tactile sensors to interact with the physical world safely. While the security of visual perception systems has been studied (e.g., adversarial samples), the integrity of the tactile sensory channel remains unexplored. This work explores a vulnerability in Hall-ef...
- Sleeper Channels and Provenance Gates: Persistent Prompt Injection in Always-on Autonomous AI Agents
Always-on AI agents (OpenClaw, Hermes Agent) run as a single persistent process under the owner's identity, folding messaging, memory, self-authored skills, scheduling, and shell into one authority boundary. This configuration opens what we call \emph{sleeper channels}: an untrusted input to one sur...
- DiffusionHijack: Supply-Chain PRNG Backdoor Attack on Diffusion Models and Quantum Random Number Defense
Diffusion models depend on pseudo-random number generators (PRNGs) for latent noise sampling. We present DiffusionHijack, a supply-chain backdoor attack that hijacks the PRNG to deterministically control generated images. A malicious PRNG, injected via compromised packages, forces pixel-perfect repr...
- Watermarking Should Be Treated as a Monitoring Primitive
Watermarking is widely proposed for provenance, attribution, and safety monitoring in generative models, yet is typically evaluated only under adversaries who attempt to evade detection or induce false positives at the level of individual samples. We argue that watermarking should be treated as a mo...
- Language-Based Agent Control
This paper introduces language-based agent control (LBAC), a new programming model for agentic applications that brings techniques from programming languages and language-based security to the problem of agent control. In conventional programming, combinations of static typing and runtime enforcemen...
- HE-PIM: Demystifying Homomorphic Operations on a Real-world Processing-in-Memory System
Homomorphic encryption (HE) enables computation over encrypted data, offering strong privacy guarantees for untrusted computing environments. Practical adoption remains limited by high computational complexity, large ciphertext sizes, and substantial data movement. Processor-centric architectures (C...
- (How) Do Large Language Models Understand High-Level Message Sequence Charts?
Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether these tasks are performed consistently with respect to the semantics of the artefacts being handled. This question is particularly under-researched con...
- SieveFL: Hierarchical Runtime-Aware Pruning for Scalable LLM-Based Fault Localization
Automated fault localization requires connecting an observed test failure to the responsible method across thousands of candidates--a task that purely statistical approaches handle with limited precision and that LLMs cannot yet handle at full project scale due to prohibitive token cost and signal d...
- Neurosymbolic Auditing of Natural-Language Software Requirements
Natural-language software requirements are often ambiguous, inconsistent, and underspecified; in safety-critical domains, these defects propagate into formal models that verify the wrong specification and into implementations that ship unsafe behavior. We show that large language models, equipped wi...
- UIBenchKit: A unified toolkit for design-to-code model evaluation
Recent years have seen substantial progress in automated design-to-code generation, with many methods proposed for generating HTML and CSS from webpage screenshots. However, the absence of a standardized evaluation platform makes it difficult to compare these methods fairly, limiting both practical ...
- SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
As autonomous code agents move toward end-to-end software development, evaluating their practical autonomy becomes critical. Current benchmarks hide friction by testing agents in pre-configured environments, and their static evaluation pipelines frequently fail when parsing fully autonomous trajecto...