AI News Archive: April 30, 2026 — Part 13
Sourced from 500+ daily AI sources, scored by relevance.
- Translate My Subtitle
Translate subtitles into 100+ languages with AI
- Solene Prompt
Ready-to-use AI prompt packages
- Singkla
Free AI Vocal Remover & Karaoke Maker from YouTube
- MarkDown Agent PRO for Wordpress
Make your WordPress site visible to AI agents
- Videoly
AI-Powered Video Generation in One Click With seedance2.0
- FundFlow
AI-powered startup-investor matching for Africa
- BetLogic
The Best AI for Sports Betting
- QuickRec
AI audio recording app for Amazift watch
- Pictura Gen.
Generate free Ai Images
- ZosyAI
Say goodbye to AI hallucinations , get accurate answers.
- AI Happy Horse
AI Happy Horse
- Pingy
Write personalized cold messages from website insights
- Voxatar
Clone your voice, generate podcasts.
- MyPlayBoo
AI Girlfriend, AI Companion, NSFW AI, and Virtual Assistant
- Fakivo
Detect AI-generated & fake videos in 30 seconds
- RoboResponder
AI email assistant automates replies with human fallback
- TalkScaled
The AI Receptionist that books dental patients 24/7
- AI Document Translation in Seconds
Upload, Translate, Done, Fast, Accurate, Secure, Instant
- DishGuise, a game of culinary deduction
Daily food puzzle with a built-in AI recipe engine.
- MiDash AI
Invest naturally with conversational AI.
- AirMusic
AI music generator and music video creator in one platform.
- AI Resale Pro
Turn liquidation manifests into profit predictions instantly.
- Hair AI Changer
Try any hairstyle before you cut.
- Nya AI
All-in-one AI workspace for teams and individuals.
- IG Follower Export by SoLeads.ai
Export Instagram followers to CSV instantly.
- AI Visibility Monitor by Work-Smart.ai
Track AI citations of your site.
- Vimi
Vimi
- Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making
This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed by agentic AI to ensure real-time autonomous decisions. The framework relies on physical sensors and edge computing to measure real-time traffic information and simulat...
- Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
This paper is a continuation work of Ren et al. (2026) aiming to further devise q-learning algorithms for mean-field control (MFC) with controlled common noise. Based on the relaxed control formulation, we first establish the martingale condition of the value function and the Iq-function by evaluati...
- Continuous-time q-learning for mean-field control with common noise, part-I: Theoretical foundations
This paper investigates the continuous-time counterpart of the Q-function for entropy-regularized mean-field control (MFC) with controlled common noise, coined as q-function by Jia and Zhou (2023) in the single agent's model. We first show that, under discretely sampled actions, the value function i...
- Enhancing multimodal affect recognition in healthcare: the robustness of appraisal dimensions over labels within age groups and in cross-age generalisation
The integration of artificial intelligence (AI) into healthcare has advanced significantly, yet affect recognition remains a major challenge, particularly in AI-assisted interventions such as Computerized Cognitive Training (CCT). The THERADIA-WoZ corpus was developed to enable multimodal affect rec...
- Beyond One-Size-Fits-All Exercises: Personalizing Computer Science Worksheets with Large Language Models
Large Language Models (LLMs) have been widely applied to student-facing educational tools, this work explores their use in supporting instructors by presenting a practical adaptation of the Framework for Adaptive Content using Educational Technology (FACET) system to generate personalized instructio...
- Evaluating Epistemic Guardrails in AI Reading Assistants: A Behavioral Audit of a Minimal Prototype
Large language model (LLM) reading assistants are increasingly used in settings that require interpretation rather than simple retrieval. In these contexts, the central risk is not only error or unsafe output, but interpretive displacement: the transfer of meaning-making work from reader to system. ...
- Normativity and Productivism: Ableist Intelligence? A Degrowth Analysis of AI Sign Language Translation Tools for Deaf People
Sign languages, of any geographical or accentual variation, understandably face continuous scrutiny under the ever present popularity of verbal dictation and audism. Through this, many potential problems arise with the current lack of accessible communication for those who rely on such sign language...
- When and How AI Should Assist Brainstorming for AI Impact Assessment
A key task in AI practice is to assess potential impacts to prevent harm. Current AI tools assisting AI impact assessment have not been designed or evaluated for collaborative team brainstorming, and they do not capture the range of views in diverse teams. We studied how AI can support team brainsto...
- Real-Time Control of a Virtual Orchestra by Recognition of Conducting Gestures
We present a museum installation in a 180° dome theater, which gives the museum visitor the experience of conducting a symphony orchestra. We have pre-recorded a short music piece performed by a professional orchestra. This recording is played back in the dome with the visitor standing in the conduc...
- "It depends on where AI is used": Players' attitude patterns and evaluative logics toward different AI applications in digital games
As AI becomes increasingly embedded in digital games, players' attitudes de-pend not only on whether AI is used, but also on where and how it intervenes in gameplay. This study examines players' evaluative patterns toward eight AI application contexts, including intelligent NPCs, emergent narrative,...
- AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments
A long-standing challenge in economics lies not in the lack of intuition, but in the difficulty of translating intuitive insights into verifiable research. To address this challenge, we introduce AgentEconomist, an end-to-end interactive system designed to translate abstract intuitions into executab...
- Knowledge Affordances for Hybrid Human-AI Information Seeking
As information ecosystems grow more heterogeneous, both humans and artificial agents increasingly face a simple yet unresolved question: when seeking knowledge, whom should we ask, and why? Inspired by how people intuitively "read a room", this paper introduces the concept of knowledge affordance (K...
- Examining discontinuance of AI-mediated informal digital learning of English (AI-IDLE) among university students: Evidence from SEM and fsQCA
This study examined university students' discontinuance intention towards AI-mediated informal digital learning of English (AI-IDLE). Drawing on the cognition-affect-conation framework, the study investigated how three cognitive factors, namely disconfirmation, perceived complexity, and perceived ri...
- Why Learners Drift In and Out: Examining Intermittent Discontinuance in AI-Mediated Informal Digital English Learning (AI-IDLE) Using SEM and fsQCA
This study examined intermittent discontinuance in AI-mediated informal digital learning of English (AI-IDLE) through the cognition-affect-conation framework. Survey data were collected from 632 Chinese university EFL learners with prior AI-IDLE experience and analysed using structural equation mode...
- FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption
Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as retrieval-augmented generation, autonomous agents, and AI assistants. However, security remains a major concern for their widespread deployment, with th...
- Latent Adversarial Detection: Adaptive Probing of LLM Activations for Multi-Turn Attack Detection
Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where individual turns appear benign. We show this attack path leaves an activation-level signature in the model's residual stream: each phase shift moves the a...
- MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
Mixture-of-Experts (MoE) architectures in Large Language Models (LLMs) have significantly reduced inference costs through sparse activation. However, this sparse activation paradigm also introduces new safety challenges. Since only a subset of experts is engaged for each input, model behavior become...
- How Code Representation Shapes False-Positive Dynamics in Cross-Language LLM Vulnerability Detection
How code representation format shapes false positive behaviour in cross-language LLM vulnerability detection remains poorly understood. We systematically vary training intensity and code representation format, comparing raw source text with pruned Abstract Syntax Trees at both training time and infe...
- VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
Large Language Model (LLM) watermarking is crucial for establishing the provenance of machine-generated text, but most existing methods rely on a centralized trust model. This model forces users to reveal potentially sensitive text to a provider for detection and offers no way to verify the integrit...
- Low Rank Adaptation for Adversarial Perturbation
Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved the training efficiency of Large Language Models (LLMs) by updating neural network layers using low-rank matrices. Since the generation of adversarial ex...
- Security Attack and Defense Strategies for Autonomous Agent Frameworks: A Layered Review with OpenClaw as a Case Study
Autonomous agent frameworks built upon large language models (LLMs) are evolving into complex, tool-integrated, and continuously operating systems, introducing security risks beyond traditional prompt-level vulnerabilities. As this paradigm is still at an early stage of development, a timely and sys...
- AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning
Federated learning (FL) is a popular distributed learning paradigm in machine learning, which enables multiple clients to collaboratively train models under the guidance of a server without exposing private client data. However, FL's decentralized nature makes it vulnerable to poisoning attacks, whe...
- Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although ''local offline fine-tuning'' is often viewed as a privacy boundary, we reveal that compromised model code is sufficient to steal them. Current passive pretrained-we...