AI News Archive: May 14, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- Helport AI Launches New ‘AI Labor’ Corporate Website
Helport AI Launches New ‘AI Labor’ Corporate Website Toronto Star
- SPARC AI Inc. (OTC: SPAIF) Leading Out in Filling Gap, Developing Software-Only Drone System
SPARC AI Inc. (OTC: SPAIF) Leading Out in Filling Gap, Developing Software-Only Drone System Toronto Star
- Quantexa selected by HMRC for Landmark £175M Sovereign Data and AI Transformation
Quantexa selected by HMRC for Landmark £175M Sovereign Data and AI Transformation Toronto Star
- Infoblox and GoDaddy Support Open Standards for AI Agent Discovery, Identity and Verification
Infoblox and GoDaddy Support Open Standards for AI Agent Discovery, Identity and Verification Toronto Star
- MindBio Therapeutics Corp. (CSE: MBIO) (OTCQB: MBQIF) Harnesses Key AI, Voice Analysis to Offer Impairment Solution
MindBio Therapeutics Corp. (CSE: MBIO) (OTCQB: MBQIF) Harnesses Key AI, Voice Analysis to Offer Impairment Solution Toronto Star
- reAlpha (NASDAQ: AIRE) Regains Compliance with Nasdaq Minimum Bid Price Requirement
reAlpha (NASDAQ: AIRE) Regains Compliance with Nasdaq Minimum Bid Price Requirement Toronto Star
- Calif team details how Anthropic Mythos helped build a working macOS exploit in five days
The team behind the first public macOS kernel memory corruption exploit on M5 silicon has shared fresh details on how Mythos Preview helped bypass a five-year Apple security effort in five days. more…
- New macOS vulnerabilities were exposed by Anthropic’s Mythos: report
Anthropic’s Mythos AI model has famously been kept fairly secret due to its apparent risk to software systems around the world. And today a new report says Mythos was used to expose macOS security vulnerabilities that Apple is investigating now. more…
- Apple Alerted to macOS Security Vulnerability Uncovered With AI Tool
Anthropic recently announced Project Glasswing , an initiative that enables tech companies like Apple to use its new frontier AI model Claude Mythos Preview to find security vulnerabilities across operating systems and web browsers. The Wall Street Journal today reported that researchers at cybersecurity firm Calif used Claude Mythos Preview to uncover a new macOS security vulnerability last month. Specifically, they used the model to write code that links together two macOS bugs in a way that resulted in what is known as a privilege escalation exploit. The security researchers said the exploit would not have been possible with Mythos alone, as it still required their human expertise on top, but it nevertheless proves that AI can assist with discovering software vulnerabilities. Apple said it was reviewing Calif's report to validate the findings. "Security is our top priority, and we take reports of potential vulnerabilities very seriously," an Apple spokesperson told The Wall Street J
- House Homeland panel gets briefing on Anthropic’s Mythos
The conversation was “productive and focused on a range of AI security and competitiveness issues,” according to one person familiar with the meeting.
- Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses significant challenges due to the inherently interactive and unpredictable nature of sports, where abrupt changes in veloc...
- XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference
We introduce XFP, a dynamic weight quantizer for LLM inference that inverts the conventional workflow: the operator specifies reconstruction quality floors on per-channel cosine similarity (one strict floor for attention and shared experts, one lazy floor for routed-expert MoE); XFP determines codeb...
- GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning
Low-rank adaptation (LoRA) has become the dominant paradigm for parameter-efficient fine-tuning (PEFT) of large language models (LLMs). However, its bilinear structure introduces a critical limitation: the mapping from trainable parameters to weight updates is not distance-preserving, distorting the...
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- MediaClaw: Multimodal Intelligent-Agent Platform Technical Report
MediaClaw is a multimodal agent platform built on the OpenClaw ecosystem. Its core design follows a three-layer architecture of unified abstraction, pluginized extension, and workflow orchestration. The system is intended to address practical deployment pain points in AIGC adoption, including fragme...
- Streaming Speech-to-Text Translation with a SpeechLLM
Normally, a system that translates speech into text consists of separate modules for speech recognition and text-to-text translation. Combining those tasks into a SpeechLLM promises to exploit paralinguistic information in the speech and to reduce cascaded errors. But existing SpeechLLM systems are ...
- Compositional Sparsity as an Inductive Bias for Neural Architecture Design
Identifying the structural priors that enable Deep Neural Networks (DNNs) to overcome the curse of dimensionality is a fundamental challenge in machine learning theory. Existing literature suggests that effective high-dimensional learning is driven by compositional sparsity, where target functions d...
- AI Outperforms Humans in Personalized Image Aesthetics Assessment via LLM-Based Interviews and Semantic Feature Extraction
Accurately predicting individual aesthetic evaluation for images is a fundamental challenge for AI. Various deep learning (DL)-based models have been proposed for this task, training on image evaluation data to extract objective low-level features. However, aesthetic preferences are inherently subje...
- Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
History-dependent policies induced by recurrent neural networks (RNNs) rely on latent hidden state dynamics, making verification in partially observable reinforcement learning (RL) challenging. Existing RNN verification tools typically rely on restrictive modeling assumptions or coarse over-approxim...
- XDomainBench: Diagnosing Reasoning Collapse in High-Dimensional Scientific Knowledge Composition
Large Language Models (LLMs) are increasingly deployed for knowledge synthesis, yet their capacity for compositional generalization in scientific knowledge remains under-characterized. Existing benchmarks primarily focus on single-turn restricted scenarios, failing to capture the capability boundari...
- Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions
Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distribution, where authentic student reasoning is difficult to synthesize; (2) fuzzy boundaries between error categories with high annotation noise; (3) d...
- EVA: Editing for Versatile Alignment against Jailbreaks
Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks, where adversaries exploit textual or visual triggers to bypass safety guardrails. Recent defenses typically rely on safety fine-tuning or external f...
- Non-linear Interventions on Large Language Models
Intervention is one of the most representative and widely used methods for understanding the internal representations of large language models (LLMs). However, existing intervention methods are confined to linear interventions grounded in the Linear Representation Hypothesis, leaving features encode...
- Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarcity of large-scale training data spanning diverse real-world applications. Existing datasets rely heavily on costly manu...
- Mechanical Enforcement for LLM Governance:Evidence of Governance-Task Decoupling in Financial Decision Systems
Large language models in regulated financial workflows are governed by natural-language policies that the same model interprets, creating a principal--agent failure: outputs can appear compliant without being compliant. Existing evaluation measures task accuracy but not whether governance constrains...
- Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model
Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) encode broad clinical knowledge and can reason over guidelines, they are not inherently grounded in action-conditioned patient dynamics. We introduce ...
- Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning
Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directly from bright-field morphology remains challenging. We present a unified Deep Learning (DL) framework that jointly performs White Blood Cell (WBC) cla...
- IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context. Existing frame-conditioned VLA policies infer each chunk from the current o...
- Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke
Deep learning and multi-modal fusion have demonstrated transformative potential in medical diagnosis by integrating diverse data sources. However, accurate prognosis for ischemic stroke remains challenging due to limitations in existing multi-modal approaches. First, current methods are predominantl...
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- Spontaneous symmetry breaking and Goldstone modes for deep information propagation
In physical systems, whenever a continuous symmetry is spontaneously broken, the system possesses excitations called Goldstone modes, which allow coherent information propagation over long distances and times. In this work, we study deep neural networks whose internal layers are equivariant under a ...
- AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents
Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by limited budgets and staffing. In terminology-dense domains such as rock art, translation quality depends on accurate, consistent specialised terms, a...
- Falkor-IRAC: Graph-Constrained Generation for Verified Legal Reasoning in Indian Judicial AI
Legal reasoning is not semantic similarity search. A court judgment encodes constrained symbolic reasoning: precedent propagation, procedural state transitions, and statute-bound inference. These are properties that vector-based retrieval-augmented generation (RAG) cannot faithfully represent. Hallu...
- Vision-Based Water Level and Flow Estimation
With the rapid evolution of computer vision, vision-based methodologies for water level and river surface velocity estimation have reached significant maturity. Compared to traditional sensing, these techniques offer superior interpretability, automated data archiving, and enhanced system robustness...
- How to Evaluate and Refine your CAM
Class attribution maps (CAMs) provide local explanations for the decisions of convolutional neural networks. While widely used in practice, the evaluation of CAMs remains challenging due to the lack of ground-truth explanations, making it difficult to evaluate the soundness of existing metrics. Inde...
- Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning
Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that became available only later. We study this failure through the lens of ex-ante reasoning, where a model must rely exclusively on infor...
- MultiEmo-Bench: Multi-label Visual Emotion Analysis for Multi-modal Large Language Models
This paper introduces a multi-label visual emotion analysis benchmark dataset for comprehensively evaluating the ability of multimodal large language models (MLLMs) to predict the emotions evoked by images. Recent user studies report an unintuitive finding: humans may prefer the predictions of MLLMs...
- Do Coding Agents Understand Least-Privilege Authorization?
As coding agents gain access to shells, repositories, and user files, least-privilege authorization becomes a prerequisite for safe deployment: an agent should receive enough authority to complete the task, without unnecessary authority that exposes sensitive surfaces.To study whether current models...
- A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions
Harmonized System (HS) tariff classification is a high-stakes, expert-level task in which a free-form product description must be mapped to a specific six- or eight-digit code under the General Interpretive Rules (GIR), section notes, chapter notes, and Explanatory Notes. The difficulty lies not in ...
- FactorizedHMR: A Hybrid Framework for Video Human Mesh Recovery
Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evidence. This ambiguity is not uniform across the body, as torso pose and root structure are often relatively well constrained, whereas distal articulations such a...
- IFPV: An Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification
Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional generation and verification methods still respectively face the challenges of generation infeasibility and verification insufficiency. To alleviate these limita...
- Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training
Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume as the target ratio throu...
- TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability
Recent work has promoted task-aware layer pruning as a way to improve model performance on particular tasks, as shown by TALE. In this paper, we investigate when such improvements occur and why. We show first that, across controlled polynomial regression tasks and large language models, such pruning...
- SceneFunRI: Reasoning the Invisible for Task-Driven Functional Object Localization
In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from context and commonsense knowledge, this capability remains a major challenge for vision-language models (VLMs). To address this gap, we introduce Scene...
- NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field. Published evaluations differ in datasets, in the ...
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- $π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs,...
- Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications
Autoresearch offers a flexible paradigm for automating scientific tasks, in which an AI agent proposes, implements, evaluates, and refines candidate solutions against a quantitative objective. Here, we use composition-based materials-property prediction to test whether such agents can perform a task...
- How Sensitive Are Radiomic AI Models to Acquisition Parameters?
A main barrier for the deployment of AI radiomic systems in clinical routine is their drop in performance under heterogeneous multicentre acquisition protocols. This work presents a performance-oriented framework for quantifying scan parameter sensitivity of radiomic AI models, while identifying cli...