AI News Archive: April 28, 2026 — Part 17
Sourced from 500+ daily AI sources, scored by relevance.
- BloClaw
AI workspace for scientific discovery
- ACLAS Neuro-Edu SDK
Official AI neuroscience SDK by ACLAS College
- Fixalyze
AI audits your Shopify store in 30 seconds, free
- KKCCP EU AI Act Classification Tool
Classify any AI system in 3 minutes. For Free.
- OnScreen QR built by ToolBoxesAi
Scan on-screen qr codes and barcodes directly from screen.
- Thiki
Your AI memory for products, receipts, and warranties
- GPT image 2
The easiest way to create viral AI images with GPT Image 2
- MiaoCut
Free AI background remover — no watermark, no signup
- BuddyPro
Build your own AI Expert and offer it to your audience.
- AI Core Updates
Your daily dose of AI news, breakthroughs, and core updates
- GeniLoop | AI Image to Video Generator
Transform any photo into stunning dynamic videos.
- SEOForge.ai
Get Customers from SEO & AI Search
- img2.ai
Create consistent photo variations in any style.
- Thumix AI
Generate viral YouTube thumbnails with AI in seconds.
- TrajectoryAI
Real-time company monitoring with AI insights on competitors and clients
- Anijam.ai
Simplified AI animation tool for stable, character-consistent anime videos with automatic lip-sync.
- Workflows Mistral
Workflows Mistral
- TRELLIS.2
TRELLIS.2
- Miadance
Miadance
- Scaling Biomolecular Modeling Using Context Parallelism in NVIDIA BioNeMo
For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,...
- Pythia: Toward Predictability-Driven Agent-Native LLM Serving
As LLM applications grow more complex, developers are increasingly adopting multi-agent architectures to decompose workflows into specialized, collaborative components, introducing structure that constrains agent behavior and exposes useful semantic predictability. Unlike traditional LLM serving, wh...
- Volitional Multiagent Atomic Transactions: Describing People and their Machines
Formal models for concurrent and distributed systems describe machines; the people who operate them are either ignored or treated as external environment. Yet key distributed systems -- notably grassroots platforms -- include people operating their personal machines (smartphones), and their faithful...
- Should I Replan? Learning to Spot the Right Time in Robust MAPF Execution
During the execution of Multi-Agent Path Finding (MAPF) plans in real-life applications, the MAPF assumption that the fleet's movement is perfectly synchronized does not apply. Since one or more of the agents may become delayed due to internal or external factors, it is often necessary to use a robu...
- Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents
Embodied agents in safety-critical applications such as Vision-Language Navigation (VLN) rely on multiple interdependent capabilities (e.g., perception, memory, planning, decision), making failures difficult to localize and attribute. Existing testing methods are largely system-level and provide lim...
- Lexical Anthropomorphization Influences on Moral Judgments of AI Bad Behavior
Anthropomorphic language describing artificial intelligence (AI) is widespread in media, policy, and everyday discourse; so too are discussions of AI bad behavior, from hallucinations to inappropriate comments. How does humanizing language about AI shape moral judgments when AI behaves badly? Across...
- Recommending Usability Improvements with Multimodal Large Language Models
Usability describes quality attributes of application user interfaces that determine how effectively users can interact with them. Traditional usability evaluation methods require considerable expertise and resources, which can be challenging, especially for small teams and organizations. Automating...
- Designing and Evaluating Next-Generation Learning Interfaces: Linking AI, HCI, and the Learning Sciences
This workshop addresses this gap by bringing together researchers and practitioners from AI, HCI, and the learning sciences to explore how interactive systems can better support learning. We focus on the design and evaluation of human-AI collaborative learning interfaces that are technically robust,...
- ClayScape: A GenAI-Supported Workflow for Designing Chinese Style Ceramics with Clay 3D Printing
Chinese ceramic-making involves complex and interdependent steps, making it technically demanding. Digital fabrication methods attempt to make the process more accessible, but for craft-creators, technical challenges such as CAD and CAM skills remain major obstacles. To address this, we designed a h...
- Generative UI as an Accessibility Bridge: Lessons from C2C E-Commerce
Web accessibility rests on static standards and developer compliance. That model frays in platforms where content is user-generated: photos arrive blurry or off-frame, descriptions skip size and condition, and page structure shifts from listing to listing. Drawing on six studies conducted between 20...
- One-shot emergency psychiatric triage across 15 frontier AI chatbots
AI chatbots are increasingly used for health advice, but their performance in psychiatric triage remains undercharacterized. Psychiatric triage is particularly challenging because urgency must often be inferred from thoughts, behavior, and context rather than from objective findings. We evaluated ...
- Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows
Despite AI tools becoming increasingly embedded in academic practice, little is known about how university students integrate them into their writing processes. We examine how students engage with AI across different writing tasks, and how this engagement is shaped by individual factors including AI...
- Value-Sensitive AI for Prayer: Balancing the Agencies Between Human and AI Agents in Spiritual Context
We present four conceptual value-sensitive AI systems to examine how the presence of AI could influence praying experiences. Drawing on key values and practices associated with praying identified through a diary study, we designed AI systems intended to "assist" prayer practices. These designs were ...
- The Dynamics of Delusion: Modeling Bidirectional False Belief Amplification in Human-Chatbot Dialogue
There is growing concern that AI chatbots might fuel delusional beliefs in users. Some have suggested that humans and chatbots mutually reinforce false beliefs over time, but quantitative evidence is lacking. Using a unique dataset of chat logs from individuals who exhibited delusional thinking, we ...
- From CRUD to Autonomous Agents: Formal Validation and Zero-Trust Security for Semantic Gateways in AI-Native Enterprise Systems
Enterprise software engineering is shifting away from deterministic CRUD/REST architectures toward AI-native systems where large language models act as cognitive orchestrators. This transition introduces a critical security tension: probabilistic LLMs weaken classical mechanisms for validation, acce...
- MARD: A Multi-Agent Framework for Robust Android Malware Detection
With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models (LLMs) demons...
- R-CoT: A Reasoning-Layer Watermark via Redundant Chain-of-Thought in Large Language Models
Large language models (LLMs) are widely deployed in multiple scenarios due to reasoning capabilities. In order to prevent the models from being misused, watermarking is generally employed to ensure ownership. However, most existing watermarking methods rely on superficial modifications to the model'...
- Making AI-Assisted Grant Evaluation Auditable without Exposing the Model
Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical governance problem: the model and scoring rubric should not be exposed in a way that allows applicants to optimize against them, yet the evaluation process ...
- SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection attacks that embed malicious instructions into webpage content to induce unintended actions. This threat is further amplified for screenshot-based web...
- ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography
Generative linguistic steganography (GLS) enables covert communication by embedding secret messages into the natural language generation process. In practical deployment, however, GLS is vulnerable to tokenization ambiguity: the same surface text may be re-tokenized into a different token sequence a...
- AgentDID: Trustless Identity Authentication for AI Agents
AI agents are autonomous entities that can be instantiated on demand, migrate across platforms, and interact with other agents or services without continuous human supervision. In such environments, identity is critical for establishing reliable interaction semantics among agents that may lack prior...
- MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors
We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detection, existing evaluations are often fragmented across datasets, preprocessing, attacks, and metrics, making results hard to compare and reproduce. MGTEV...
- Structured Security Auditing and Robustness Enhancement for Untrusted Agent Skills
Agent Skills package SKILL.md files, scripts, reference documents, and repository context into reusable capability units, turning pre-load auditing from single-prompt filtering into cross-file security review. Existing guardrails often flag risk but recover malicious intent inconsistently under sema...
- From Threads to Trajectories: A Multi-LLM Pipeline for Community Knowledge Extraction from GitHub Issue Discussions
Resolution of complex post-production issues in large-scale open-source software (OSS) projects requires significant cognitive effort, as developers need to go through long, unstructured and fragmented issue discussion threads before that. In this paper, we present SWE-MIMIC-Bench, an issue trajecto...
- Using Large Language Models for Black-Box Testing of FMU-Based Simulations
We propose a human in the loop approach for black-box testing of Functional Mock-up Units (FMUs) using Large Language Models (LLMs). The goal is to reduce the manual effort in defining test scenarios for dynamic simulation models and to improve the interpretability of results. The approach takes the...
- CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction
Despite strong performance on code generation tasks, it remains unclear whether large language models (LLMs) genuinely reason about code execution. Existing code reasoning benchmarks primarily evaluate final output correctness under a single canonical implementation, leaving two critical aspects und...
- AI as Consumer and Participant: A Co-Design Agenda for MBSE Substrates and Methodology
AI tools are being deployed over MBSE models today, and those models were not designed for this kind of consumption. The problem is not simply that tools hallucinate: well-prompted frontier models produce competent, useful output over a conformant SysML model, but the reasoning they produce is drawn...
- DiRe-RAPIDS: Topology-faithful dimensionality reduction at scale
Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisation: top-performing em...
- ML-SAN: Multi-Level Speaker-Adaptive Network for Emotion Recognition in Conversations
To establish empathy with machines, it is essential to fully understand human emotional changes. However, research in multimodal emotion recognition often overlooks one problem: individual expressive traits vary significantly, which means that different people may express emotions differently. In ou...
- UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition
The joint training of speech enhancement and speaker embedding networks for speaker recognition is widely adopted under noisy acoustic environments. While effective, this paradigm often fails to leverage the generalization and robustness benefits inherent in large-scale speech enhancement pre-traini...
- K-CARE: Knowledge-driven Symmetrical Contextual Anchoring and Analogical Prototype Reasoning for E-commerce Relevance
This paper targets e-commerce search relevance. While Large Language Models (LLMs) have demonstrated significant potential in this field, they often encounter performance bottlenecks in persistent 'corner cases' within complex industrial scenarios. Existing research primarily focuses on optimizing r...