AI News Archive: May 13, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- Expert reveals phrases to avoid chatbots and ‘speak to a human’
An expert has revealed the key phrases to use to avoid customer service chatbots and speak to a real person instead.
- EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments
The integration of large language models (LLMs) in economic simulations has significantly enhanced agent-based modeling, yet existing frameworks struggle to capture the interplay between short-term optimization and long-term strategic planning. Conventional approaches rely on static data-driven pred...
- The Global Intelligence Corridor: TGI AMIRON Alliance Unveils Multi-Continental Sovereign AI Expansion from the Silk Road to the Panama Canal
The Global Intelligence Corridor: TGI AMIRON Alliance Unveils Multi-Continental Sovereign AI Expansion from the Silk Road to the Panama Canal The Arizona Republic
- Samsung Galaxy Z Fold 8 and Flip 8 may debut with Gemini Intelligence
Gemini Intelligence may finally find its dream home on Samsung's upcoming foldables.
- ‘Gemini Intelligence’ reportedly launching with Galaxy Z Fold 8, Flip 8
Google’s “Gemini Intelligence” updates for Android might arrive on Samsung’s new Galaxy Z Fold 8 and other foldables first, a new report claims. more…
- Governments may shape what AI chatbots say by shaping the web they learn from
Ask an AI model the same political question in two different languages, and you may get two very different responses. A new study in Nature suggests one reason why: governments can indirectly influence large language models (LLMs) by shaping the online media environment, and thus the text those systems learn from.
- Governments may shape what AI chatbots say by shaping the web they learn from (IMAGE)
Governments may shape what AI chatbots say by shaping the web they learn from (IMAGE) EurekAlert!
- AI generates first complete models of proteins in motion (VIDEO)
AI generates first complete models of proteins in motion (VIDEO) EurekAlert!
- SkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems
Large language model agents increasingly rely on skill libraries for multi-step tasks, yet these libraries can accumulate persistent defects as skills are added, reused, patched, and linked to changing dependencies. We call this failure mode skill technical debt: library-level defects that may not b...
- RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation
Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, underscoring a clear need for reliable AI decision support. Existing ICU benchmarks typically treat historical clinician actions...
- Finding the Weakest Link: Adversarial Attack against Multi-Agent Communications
Multi-agent systems rely on communication for information sharing and action coordination, which exposes a vulnerability to attacks. We investigate single-victim communication perturbation attacks against Multi-Agent Reinforcement Learning-trained systems and propose methods that use gradient inform...
- Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation
Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic relevance or surface-level similarity, which are often misaligned with the actual utility of visual evidence for downstream reasoning. We reformulate ...
- D-VLA: A High-Concurrency Distributed Asynchronous Reinforcement Learning Framework for Vision-Language-Action Models
The rapid evolution of Embodied AI has enabled Vision-Language-Action (VLA) models to excel in multimodal perception and task execution. However, applying Reinforcement Learning (RL) to these massive models in large-scale distributed environments faces severe systemic bottlenecks, primarily due to t...
- Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning
On-policy self-distillation trains a reasoning model on its own rollouts while a teacher, often the same model conditioned on privileged context, provides dense token-level supervision. Existing objectives typically weight the teacher's token-level signal uniformly across a chain-of-thought sequence...
- A Multi-Agent Orchestration Framework for Venture Capital Due Diligence
We present a fully automated multi-agent framework for corporate due diligence and market analysis in venture capital. The system runs on an event-driven orchestration architecture, combining Large Language Models (LLMs) with real-time web retrieval to synthesize unstructured data into structured in...
- SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring
Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing specialized algorithms, while effective, often face high implementation barriers, limited interoperability and complex training procedures. To overcome ...
- ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, rely on closed-source APIs incompatible with chip vendors' air-gapped security requirements, and cannot be trained on vend...
- EgoForce: Robust Online Egocentric Motion Reconstruction via Diffusion Forcing
With recent advances in embodied agents and AR devices, egocentric observations are readily available as input for real-world interactive online applications. However, egocentric viewpoints can only sporadically observe hands, in addition to the estimated head trajectory. We propose EgoForce, an onl...
- CoGE: Sim-to-Real Online Geometric Estimation for Monocular Colonoscopy
Geometric estimation including depth estimation and scene reconstruction is a crucial technique for colonoscopy which can provide surgeons with 3D spatial perception and navigation. However, geometric ground truth in colonoscopy is difficult to obtain due to narrow and enclosed space of the colon, w...
- PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution
Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character identity and break readability. Existing methods improve text fidelity with stronger recognition-based or generative priors, yet they still face two unre...
- EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents
Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses two core evaluation challenges: generating realistic simulated conversations, and measuring qu...
- QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through attention mechanisms, but their quadratic complexity with respect to sequence length limits scalability to long contexts. State-space models (SSMs) provide...
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Free & Private AI meeting notes for you Mac
- Unweighted ranking for value-based decision making with uncertainty
As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concerns. Their alignment with human values remains a critical challenge because it can jeopardise the integrity and security of citizens. For this reason,...
- Defense at AI speed: Microsoft’s new agentic security system tops leading industry benchmark
The post Defense at AI speed: Microsoft’s new agentic security system tops leading industry benchmark appeared first on Source .
- Our new multi-model agentic security system brings together more than 100 specialized agents across frontier and custom models to find exploitable bugs, delivering top performance on the CyberGym benchmark. We used it ahead of Patch Tuesday to help find and fix 16 vulnerabilities. Today we’re announcing that customers can sign up to test it in private preview. Read more…
The post Our new multi-model agentic security system brings together more than 100 specialized agents across frontier and custom models to find exploitable bugs, delivering top performance on the CyberGym benchmark. We used it ahead of Patch Tuesday to help find and fix 16 vulnerabilities. Today we’re announcing that customers can sign up to test it in private preview. Read more… appeared first on Source .
- Cerebras is coming to AWS
Cerebras integrates with AWS for AI computing
- SAP’s AI offer to legacy customers comes with a catch
More than 20,000 SAP customers are “stuck” on legacy ECC systems due to customizations, according to the CEO of SAP partner MyWave, Geraldine McBride, and many won’t migrate anytime soon. At Sapphire 2026, SAP offered them a path to AI, but only if they commit half their maintenance spend to the cloud first. The offer, confirmed by SAP Chief Strategy Officer Sebastian Steinhaeuser during a media briefing, enables SAP to extend limited AI capabilities to customers still running its software on-premises during their transition to cloud ERP. But it comes with a significant condition: Customers must shift at least 50% of their maintenance spending to the cloud before they can enable Joule assistants on premises. It’s a another small crack in SAP’s resolve: For years, it has been upsetting customers using its legacy systems by telling them the only way to access its latest innovations is to migrate to its latest platform , S/4HANA, in the cloud. “There’s no confusion at all” about the lates
- SAP’s AI promises last year? Most are still rolling out
SAP made bold promises about AI at Sapphire 2025 : Knowledge Graph, Joule Studio, and AI Agent Hub would ship by the end of the year. Those tools are now technically available, but adoption has lagged, and SAP is already announcing version 2.0. “Joule Studio adoption has been minimal compared to what we’d like,” said Manoj Swaminathan , SAP’s chief product officer for Business Suite, in a briefing ahead of this year’s Sapphire. The tool “was limited to content-based experiences,” he said. “Anytime more complex agents were involved, it had limited capabilities.” The issue, according to SAP’s chief AI officer Jonathan von Rüden , was that SAP had favored ease of use over power in its original architecture. “People wanted to see more pro-code flexibility,” he said in an interview at Sapphire 2026. “We had gone with a low-code approach. You could give it extension points and tools, but you couldn’t touch the core of it. Now you can build a custom agent, connect it to your own GitHub.” Cust
- SAP customers say migration is eating their budgets—and AI is next in line
SAP has unveiled its most ambitious AI vision yet at Sapphire 2026 this week: more than 50 Joule Assistants, 200 specialized agents, and a new “Autonomous Enterprise” framework. But for many SAP customers, who are still navigating costly migrations to SAP’s S/4HANA cloud ERP, the more pressing question isn’t what AI can do. It’s whether they can get there from here. Budget constraints are now the top challenge facing SAP customers, cited by 61% of respondents in the Americas’ SAP Users’ Group (ASUG) 2026 Pulse of the SAP Customer survey, 7 percentage points higher than last year. And the culprit isn’t macroeconomic pressure, according to ASUG. It’s the migrations themselves. “From our research, it’s more so that S/4HANA projects are creating the budget pressures,” said ASUG’s research director Marissa Gilbert . “And we’ll see AI impacting them next.” Stuck in pilot mode Separate ASUG research on AI adoption , conducted in February 2026 in collaboration with Microsoft and Intel, reveale
- Introducing Managed Deep Agents
Introducing Managed Deep Agents
- New in Deep Agents v0.6
New features in Deep Agents v0.6
- Cerebras — Faster Tokens Please
// OpenAI and AWS Partnerships, Tokenomics Explainer, Architecture Deep Dive, Datacenter Ramp, Technical Roadmap
- Google's new AI will make your Android phone faster and smarter
Google's new AI will make your Android phone faster and smarter
- Googles big Android update goes all-in on AI: Everything to know
Google’s Android update adds Gemini Intelligence, bringing more advanced AI features and app-based assistance to phones.
- Make cleaning more efficient with $700 off the Dreame X50 Ultra robot vacuum and mop
As of May 13, save $700 on the Dreame X50 Ultra robot vacuum and mop.
- Normally over $1,000, the EcoVacs Deebot T30S robot vacuum is just $449 right now at Amazon
As of May 13, the Ecovacs Deebot T30S robot vacuum and mop is back to its lowest-ever price of $449 at Amazon. This is 63% off its list price of $1,199.99.
- Intel, Qualcomm confirm Googlebook AI laptop partnerships, opening ARM andx86 possibilities for new OS — Google VP says devices to also ship with MediaTek chips
Intel has officially confirmed its partnership with Googlebook as Google prepares a new lineup of Gemini-powered AI laptops.
- Query-Conditioned Test-Time Self-Training for Large Language Models
Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at inference time. While such test-time scaling can be effective, it cannot correct model misconceptions or adapt the model to the specific structure of a...
- Probing Persona-Dependent Preferences in Language Models
Large language models (LLMs) can be said to have preferences: they reliably pick certain tasks and outputs over others, and preferences shaped by post-training and system prompts appear to shape much of their behaviour. But models can also adopt different personas which have radically different pref...
- Tracing Persona Vectors Through LLM Pretraining
How large language models internally represent high-level behaviors is a core interpretability question with direct relevance to AI safety: it determines what we can detect, audit, or intervene on. Recent work has shown that traits such as evil or sycophancy correspond to linear directions in the in...
- CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution
LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet automating their configuration remains a structural challenge, as scores are available only at the system lev...
- What properties of reasoning supervision are associated with improved downstream model quality?
Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning dataset can be reliably predicted prior to training using intrinsic data metrics. We propose a suite of quantitative measures...
- The Readability Spectrum: Patterns, Issues, and Prompt Effects in LLM-Generated Code
As Large Language Models (LLMs) are transforming software development, the functional quality of generated code has become a central focus, leaving readability, one of critical non-functional attributes, understudied. Given that LLM-generated code still needs human review before adoption, it is impo...
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