Tue 28 April 2026

How to price your AI product in a post-SaaS world? | This is what raising $1.5M really looked like: 250 meetings & 171 rejections.

What people really want from AI? - Anthropic Survey.

Disney’s $60 billion bet on the one thing AI can’t replace

Disney’s CEO is facing an existential crisis brought about by an emerging technology. The year was 1955, the CEO was Walt Disney and the tech was television.

GoTo Posts First-Ever Net Income After Cost Cuts Bear Fruit

GoTo Group reported its first-ever net income, a major milestone in the Indonesian ride-hailing and food delivery company’s turnaround effort as it seeks to gain fresh momentum in its long-standing rivalry with Grab Holdings Ltd.

Tech’s ‘New Normal’ Trade Pair: Long Chip Stock, Short Software

In a choppy year for tech investors, one trade has stood out as a success: buy chip stocks, sell software shares. And the divide between winners and losers is getting bigger as 2026 moves along.

Is OpenAI Falling Further Behind in the A.I. Race?

The artificial intelligence giant has reportedly fallen behind on its own user and revenue targets, raising questions about its data center and I.P.O. plans

Citigroup lifts AI market view to over $4 trillion on enterprise adoption | Reuters

Citigroup raised its global artificial intelligence market forecast, citing faster-than-expected enterprise adoption of artificial intelligence tools for coding ​and automation, with companies such as Anthropic showing strong ‌revenue growth.

Data Center-Linked Bonds Slide as OpenAI Report Fuels Worries

US corporate bonds linked to data-center firms fell Tuesday after the Wall Street Journal reported that OpenAI recently failed to meet its own goals for new user acquisition and sales, fueling internal concerns that the company may struggle to support its spending on artificial intelligence infrastructure.

CrowdStrike to rally as Anthropic spotlights AI cybersecurity threats, Mizuho says

CrowdStrike should see its shares rise as Project Glasswing calls attention to cybersecurity threats posed by the growing use of AI, per Mizuho.

STMicroelectronics Q1 FY 2026 Earnings Show Early AI and Satellite Upside

Brendan Burke, Research Director, analyzes STMicroelectronics’ Q1 FY 2026 earnings, focusing on AI data center and LEO satellite momentum, photonics ramp timing.

AI Infrastructure Stocks Drop Amid OpenAI Growth Concerns

On April 28, 2026, shares of Super Micro Computer Inc (SMCI) experienced a notable decline in pre-market trading, alongside other companies linked to artificial

The Economic Imperative of Predicting AI Capability and Scaling Velocity

All downstream economic impacts of AI, including labor displacement and productivity shifts, depend on the ultimate capability and scaling speed of models. Focusing on the S-curve of AI development is essential for strategic planning.

AI-Driven Energy Demand and the Emerging Impact on Credit Risk - Business Information

AI-driven energy demand is reshaping credit risk. Learn how rising energy costs may impact small business performance and lending strategies.

The Future Is Shrouded in an AI Fog

AI’s rapid advance is creating new limits on leaders’ visibility into the short-term future and challenging the criteria they use to commit to forward-looking investments. Staring into this fog, leaders will be tempted to trade the potential future gains from skyscrapers and railways for ...

Anthropic’s Little Brother

OpenAI is racing to catch up to its greatest rival.

CIOs move to reclaim value as AI shakes up outsourcing contracts

As AI reshapes outsourcing models, CIOs are renegotiating contracts, reworking governance, and rethinking risk management.

The Impact of Dodd-Frank and the Huawei Shock on DRC Tin Exports

arXiv:2512.21645v2 Announce Type: replace Abstract: This paper investigates the structural transformation of the Democratic Republic of the Congo (DRC) tin market induced by the U.S. Dodd-Frank Act. Focusing on the breakdown of the pricing mechanism, we estimate the price elasticity of export demand from 2010 to October 2022 using a structural identification strategy that overcomes the lack of reliable unit value data. Our analysis reveals that the regulation effectively destroyed the price mechanism, with demand elasticity dropping to zero. This indicates the formation of a ``captive market'' driven by certification requirements rather than price competitiveness. Also, we find strong hysteresis; deregulation alone failed to restore market flexibility. The structural rigidity was finally broken not by policy suspension, but by the 2019 ``Huawei shock,'' an external demand surge that forced supply chain diversification.

AI's Role in Modern Pricing Strategies: Unlocking ai-driven pricing solutions

Pricing is one of the most powerful levers in retail. But with thousands of SKUs and ever-changing market conditions, setting the right price can feel like trying to hit a moving target in the dark. That’s where AI steps in. Today, I want to share how AI's role in modern pricing strategies ...

Advertisers seek to capitalise on the promise of AI

Marketers need to balance the efficiency offered by automation with the authenticity that consumers demand

Should your board appoint a bot?

New AI tools help chairs and directors with prep and research but are unlikely to be granted a vote

r/automation on Reddit: What are some automations that actually got 10x better due to advancements in AI?

Data Drive Content Creation & Repurposing : Our team has used AI tools like Frizerly to automate the process of coming up of a content strategy using our Google search data. It also spies on our competitors to identify keyword gaps. This is then used to automatically publish a blog on our website every day using our internal data like customer testimonials, case studies etc.

‘AI deflation’ comes to India’s tech services giants and puts downward pressure on revenue

Headcounts, however, are mostly holding up AI is beginning to make a dent in the business models of India’s big four technology services giants…

AI & Tech Brief: The Pentagon goes VC - The Washington Post

Plus, a sit-down with Evan Smith, the CEO of Altana, on global AI supply chains

Meta, Google, OpenAI among Big Tech firms seeing top staff leaving to launch AI startups

Former employees at AI giants are raising hundreds of millions of dollars from investors months on from launching.

Stockholm’s Redpine raises €6.8 million to unlock licensed premium data for AI agents

Redpine, a Stockholm-based AI startup, has raised €6.8M in Seed funding to power AI companies and agents with access to licensed, high-quality and multimodal data, securely and at scale. The round was led by NordicNinja, with participation from fellow Nordic firms Luminar Ventures and node.vc. Alongside the Seed funding, Redpine has received investment from strategic […]

Copenhagen’s Performativ raises €11.9 million Series A to scale its AI-native wealth management operating system

Performativ, a Copenhagen-based startup building the next-generation operating system for wealth management, has raised €11.96 million ($14 million) in its Series A funding round. The round was led by Deutsche Börse Group, with participation from Rabo Investments, the investment arm of Rabobank, Jacob Dahl, former Senior Partner and Co-leader of Global Banking Sector, McKinsey & […]

fDi Intelligence – Your source for foreign direct investment information - fDiIntelligence.com

Ban of $2bn deal puts firms and founders falling within Beijing’s AI ambitions on notice

Here’s the contentious history behind OpenAI.

Amsterdam’s QDNL Participations rebrands as Ground State Ventures, raises over €75.2 million for quantum tech fund

Amsterdam-based quantum technology-focused VC firm QDNL Participations today announced its rebranding to Ground State Ventures. It is also preparing for the final close of its early-stage quantum technology fund, having already raised over €75.2 million ($88 million), far exceeding the original target of €59.8 million ($70 million). As per the VC firm, the rebrand reflects […]

Top 10 AI Companies Leading New Zealand's Tech Boom in 2026

AUCKLAND — New Zealand's artificial intelligence sector is experiencing explosive growth in 2026, with a wave of innovative startups and established players driving advancements in agritech, healthcare, customer experience, and maritime intelligence, positioning the country as a rising force ...

‘They’re supposed to be handmade’: zine creators fight to resist AI influence

Artists and writers argue scrappy nature of self-published booklets is incompatible with artificial intelligence The self-published zine has long been central to cultural revolutions, from queer activism to Black feminism and the riot grrrl punk movement, producing titles such as Sniffin’ Glue and Sweet-Thang along the way. But now the traditionally analogue art form faces a new shift: artificial intelligence. AI may seem incompatible with the these cult DIY booklets, but some creatives, designers and artists have begun to experiment with the technology, causing alarm in parts of the underground publishing world. It has been their Dylan-goes-electric moment. Continue reading...

Microsoft researchers have revealed the 40 jobs most exposed to AI—and even teachers make the list | Fortune

Sorry, Gen Z: AI is expected to soon reshape dozens of popular professions—and possibly make some tasks obsolete.

The 85/5 Enterprise AI Paradox: Why Almost... | Metaintro

85% of enterprises run AI agents, only 5% ship them. Here's what the trust gap means for AI hiring, job security, and the roles employers actually need in 2026.

Despite the hype, AI is not replacing the customer service workforce | HR Dive

The hype says “agentless” service is imminent, but data shows most teams are still staffing up while trying to make artificial intelligence actually function in real workflows.

Nearly half of London jobs at risk of AI disruption and women will be hardest hit, new report finds | Euronews

According to a new report by the Mayor of London's office, nearly half of the UK capital's workers could see their jobs transformed by generative AI.

AI job losses could now exceed 4pc of workforce, says skills tsar

New government-backed forum to examine rules to protect workers is to meet for the first time on Wednesday.

9 Cash and Retraining Lifelines for... | Metaintro

AI-driven layoffs in 2026? Tap nine cash stipends, retraining grants, and employer-funded programs for displaced workers. Get the help you have earned.

Employees Petition Google CEO To Block Classified Military Use of AI Technology - The Media Line

Google employees have signed a petition opposing the […]

AI Attack on Black: Tech Lynchings & Cyber Discrimination 101 | EURweb | Black News, Culture, Entertainment & More

AI shows measurable bias against African American English. Tech lynching is real. The evidence is clear and urgent.

What fighter pilots can teach us about enterprise AI decisions | TechRadar

Decision traceability and human judgment in enterprise AI

Peer Identity Bias in Multi-Agent LLM Evaluation: An Empirical Study Using the TRUST Democratic Discourse Analysis Pipeline

arXiv:2604.22971v1 Announce Type: new Abstract: The TRUST democratic discourse analysis pipeline exposes its large language model (LLM) components to peer model identity through multiple structural channels -- a design feature whose bias implications have not previously been empirically tested. We provide the first systematic measurement of identity-dependent scoring bias across all active identity exposure channels in TRUST, crossing four model families with two anonymization scopes across 30 political statements. The central finding is that single-channel anonymization produces near-zero bias effects, because individual channels act in opposite directions and cancel each other out -- a result that would lead an evaluator to conclude that identity bias is absent when it is not. Only full-pipeline anonymization reveals the true pattern: homogeneous ensembles amplify identity-driven sycophancy when model identity is fully visible, while the heterogeneous production configuration shows the reverse. Model choice matters independently: one tested model exhibits baseline sycophancy two to three times higher than the others and near-zero deliberative conflict on ideological topics, making it structurally unsuitable for pipelines where genuine inter-role disagreement is the intended quality mechanism. Three practical conclusions follow. First, heterogeneous model ensembles are structurally more robust than homogeneous ones, achieving higher consensus rates and lower identity amplification. Second, full-pipeline anonymization is required for valid bias measurement -- partial anonymization is insufficient and actively misleading. Third, these findings have direct implications for the validation of multi-agent LLM systems in quality-critical applications: a system validated under partial anonymization or with a homogeneous ensemble may pass validation while retaining structural identity bias invisible to single-channel measurement.

Training for the Wrong Job | American Enterprise Institute - AEI

Workforce development is not being displaced by AI. It is being asked to solve one of the defining problems of the next decade: how to grow judgment when the experiences that produce it are increasingly pressured by automation.

Gen Z faces rising job fears as former Meta executive launches AI skills nonprofit - Tech Edition

Former Meta executive launches nonprofit to help Gen Z gain AI skills as fears grow over automation and job security.

This trial comes amid a growing national backlash against A.I.

Humanoid robots to become baggage handlers in Japan airport experiment

Japan Airlines will introduce the robots for trial run at a Tokyo airport amid country’s surge in inbound tourism and worsening labour shortages Japan’s famously conscientious but overburdened baggage handlers will soon be joined by extra staff at Tokyo’s Haneda airport – although their new colleagues will need to take regular recharging breaks. Japan Airlines will introduce humanoid robots on a trial basis from the beginning of May, with a view to deploying them permanently as a solution to the country’s chronic labour shortage. Continue reading...

Competitive Analysis of Enterprise AI Agent Integration in Productivity Software

A critique of Microsoft's Outlook agent implementation suggests that current enterprise AI integrations often suffer from poor UX and limited cross-platform visibility. The analysis compares these shortcomings against more agile, third-party agentic alternatives.

Council Post: How AI Agents Can Help Small Businesses Compete

AI agents are more than an emerging trend to monitor; they offer operational advantages that can allow small teams to perform like large ones.

I used Claude’s new Dispatch feature for a month. Here’s everything I was able to do

The new AI feature is less “chatbot on your phone” and more a way to send your computer errands while you’re away.

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents

arXiv:2604.23194v1 Announce Type: new Abstract: Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide long-term actions in dynamic environments. However, current planning approaches face a fundamental limitation that they operate at a fixed granularity level. Specifically, they either provide excessive detail for simple tasks or insufficient detail for complex ones, failing to achieve an optimal balance between simplicity and complexity. Drawing inspiration from the principle of \textit{progressive refinement} in cognitive science, we propose \textbf{AdaPlan-H}, a self-adaptive hierarchical planning mechanism that mimics human planning strategies. Our method initiates with a coarse-grained macro plan and progressively refines it based on task complexity. It generates self-adaptive hierarchical plans tailored to the varying difficulty levels of different tasks, which can be optimized by imitation learning and capability enhancement. Experimental results demonstrate that our method significantly improves task execution success rates while mitigating overplanning at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks. To contribute to the community, our code and data will be made publicly available at https://github.com/import-myself/AHP.

Open source Xiaomi MiMo-V2.5 and V2.5-Pro are among the most efficient (and affordable) at agentic 'claw' tasks

Xiaomi's new open-source MiMo-V2.5 models are setting new benchmarks for efficiency and affordability in agentic robotic tasks.

Nvidia-Supplier Victory Giant’s Sales Surge on Solid AI Demand

Victory Giant Technology Huizhou Co. reported a 28% increase in quarterly sales on stronger demand for printed circuit boards critical for development of AI servers.

AI-Driven CPU Shortage Saves Intel’s Financial Cookies

If you have a few pallets of datacenter CPUs sitting in a barn somewhere, and they have a reasonable ...

Chip Startup Aims to Shatter AI’s Dreaded Memory Wall

Huge AI models are overwhelming servers and leaving high-powered chips idle. Google and Meta veterans say they have the solution.

Tenstorrent’s Galaxy Blackhole AI servers escape the event horizon

RISC-V-based systems pack 32 Blackhole accelerators in a 6U, $110K chassis Tenstorrent on Tuesday announced the general availability of its Galaxy Blackhole AI compute platform.…

AI-Driven Power Demand Reshapes US Energy Consulting Market - Business Insider

Consulting firms like Boston Consulting and McKinsey see AI-driven electricity demand surge, complicating energy transition efforts.

NVIDIA's Rubin Lands Inside Google's Virtual Machine, Stretching Multi Site Clusters to Nearly 1 Million GPUs

Google and NVIDIA have teamed up to provide users with access to as much as one million NVIDIA GPUs to power up the freshly launched A5X instances. The announcement is part of the pair's latest collaboration to reduce inference costs and improve token throughput.

Intellectia

Capital Expenditure Expectations: ... for AI infrastructure alongside shifting global dynamics. Practical Limitations Emerging: Despite a strong willingness to deploy more capital, Terry highlighted that practical limitations such as power capacity shortages, skilled labor deficits, and regulatory approvals are beginning to surface, potentially capping how quickly firms can expand amid intensifying competitive pressure. Shifting Market Focus: Investor ...

Navigating the Connectivity Bottleneck in AI Infrastructure

Navigating the Connectivity Bottleneck in AI Infrastructure

When VLMs 'Fix' Students: Identifying and Penalizing Over-Correction in the Evaluation of Multi-line Handwritten Math OCR

arXiv:2604.22774v1 Announce Type: new Abstract: Accurate transcription of handwritten mathematics is crucial for educational AI systems, yet current benchmarks fail to evaluate this capability properly. Most prior studies focus on single-line expressions and rely on lexical metrics such as BLEU, which fail to assess the semantic reasoning across multi-line student solutions. In this paper, we present the first systematic study of multi-line handwritten math Optical Character Recognition (OCR), revealing a critical failure mode of Vision-Language Models (VLMs): over-correction. Instead of faithfully transcribing a student's work, these models often "fix" errors, thereby hiding the very mistakes an educational assessment aims to detect. To address this, we propose PINK (Penalized INK-based score), a semantic evaluation metric that leverages a Large Language Model (LLM) for rubric-based grading and explicitly penalizes over-correction. Our comprehensive evaluation of 15 state-of-the-art VLMs on the FERMAT dataset reveals substantial ranking reversals compared to BLEU: models like GPT-4o are heavily penalized for aggressive over-correction, whereas Gemini 2.5 Flash emerges as the most faithful transcriber. Furthermore, human expert studies show that PINK aligns significantly better with human judgment (55.0% preference over BLEU's 39.5%), providing a more reliable evaluation framework for handwritten math OCR in educational settings.

‘World models’ are AI’s latest sensation: what are they and what can they do?

Training AI world models on data about physical environments could improve their real-world capabilities in technologies such as robotics. Training AI world models on data about physical environments could improve their real-world capabilities in technologies such as robotics.

New AI framework autonomously optimizes training data, architectures and algorithms — outperforming human baselines

A new AI framework has been developed that autonomously optimizes its own training data and architecture, surpassing human-designed baselines.

FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean

arXiv:2604.23002v1 Announce Type: new Abstract: Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specific machinery (\textit{e.g.} Dirac notation, vector calculus) imposes additional formalisation challenges that modern LLMs and agentic approaches have yet to tackle. To aid autoformalisation in scientific domains, we present FormalScience; a domain-agnostic human-in-the-loop agentic pipeline that enables a single domain expert (without deep formal language experience) to produce \textit{syntactically correct} and \textit{semantically aligned} formal proofs of informal reasoning for low economic cost. Applying FormalScience to physics, we construct FormalPhysics, a dataset of 200 university-level (LaTeX) physics problems and solutions (primarily quantum mechanics and electromagnetism), along with their Lean4 formal representations. Compared to existing formal math benchmarks, FormalPhysics achieves perfect formal validity and exhibits greater statement complexity. We evaluate open-source models and proprietary systems on a statement autoformalisation task on our dataset via zero-shot prompting, self-refinement with error feedback, and a novel multi-stage agentic approach, and explore autoformalisation limitations in modern LLM-based approaches. We provide the first systematic characterisation of semantic drift in physics autoformalisation in terms of concepts such as notational collapse and abstraction elevation which reveals what formal language verifies when full semantic preservation is unattainable. We release the codebase together with an interactive UI-based FormalScience system which facilitates autoformalisation and theorem proving in scientific domains beyond physics.https://github.com/jmeadows17/formal-science

Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis

arXiv:2604.23072v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet their reasoning suffers from stochastic instability and lacks a verifiable, compositional structure. To address this, we introduce Analytica, a novel agent architecture built on the principle of Soft Propositional Reasoning (SPR). SPR reframes complex analysis as a structured process of estimating the soft truth values of different outcome propositions, allowing us to formally model and minimize the estimation error in terms of its bias and variance. Analytica operationalizes this through a parallel, divide-and-conquer framework that systematically reduces both sources of error. To reduce bias, problems are first decomposed into a tree of subpropositions, and tool-equipped LLM grounder agents are employed, including a novel Jupyter Notebook agent for data-driven analysis, that help to validate and score facts. To reduce variance, Analytica recursively synthesizes these grounded leaves using robust linear models that average out stochastic noise with superior efficiency, scalability, and enable interactive "what-if" scenario analysis. Our theoretical and empirical results on economic, financial, and political forecasting tasks show that Analytica improves 15.84% accuracy on average over diverse base models, achieving 71.06% accuracy with the lowest variance of 6.02% when working with a Deep Research grounder. Our Jupyter Notebook grounder shows strong cost-effectiveness that achieves a close 70.11% accuracy with 90.35% less cost and 52.85% less time. Analytica also exhibits highly noise-resilient and stable performance growth as the analysis depth increases, with a near-linear time complexity, as well as good adaptivity to open-weight LLMs and scientific domains.

Don't Make the LLM Read the Graph: Make the Graph Think

arXiv:2604.23057v1 Announce Type: new Abstract: We investigate whether explicit belief graphs improve LLM performance in cooperative multi-agent reasoning. Through 3,000+ controlled trials across four LLM families in the cooperative card game Hanabi, we establish four findings. First, integration architecture determines whether belief graphs provide value: as prompt context, graphs are decorative for strong models and beneficial only for weak models on 2nd-order Theory of Mind (80% vs 10%, p<0.0001, OR=36.0); when graphs gate action selection through ranked shortlists, they become structurally essential even for strong models (100% vs 20% on 2nd-order ToM, p<0.001). Second, we identify "Planner Defiance," a model-family-specific failure where LLMs override correct planner recommendations at partial competence (90% override, replicated N=20); Gemini models show near-zero defiance while Llama 70B shows 90%, and models distinguish factual context (deferred to) from advisory recommendations (overridden). Third, full-game evidence confirms inter-agent conventions (+128% over baseline, p=0.003) outperform all single-agent interventions, and individual belief-graph components must be combined to produce gains. Fourth, preliminary scaling analysis (N=10/cell, exploratory) suggests graph depth has diminishing returns: shallow graphs provide the best cost-benefit ratio, while deeper ToM graphs appear harmful at larger player counts (-1.5 pts at 5-player, p=0.029).

StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning

arXiv:2604.23198v1 Announce Type: new Abstract: Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see \textit{what is happening} but fail to reason \textit{why it matters}. This semantic gap stems from the lack of \textbf{Theory of Mind (ToM)}: the cognitive ability to infer implicit intentions, mental states, and narrative causality from surface-level observations. We introduce \textbf{StoryTR}, the first video moment retrieval benchmark requiring ToM reasoning, comprising 8.1k samples from narrative short-form videos (shorts/reels). These videos present an ideal testbed. Their high information density encodes meaning through subtle multimodal cues. For instance, a glance paired with a sigh carries entirely different semantics than the glance alone. Yet multimodal perception alone is insufficient; ToM is required to decode that a character ``smiling'' may actually be ``concealing hostility.'' To teach models this reasoning capability, we propose an \textbf{Agentic Data Pipeline} that generates training data with explicit three-tier ToM chains (intent decoding, narrative reasoning, boundary localization). Experiments reveal the severity of the reasoning gap: Gemini-3.0-Pro achieves only 0.53 Avg IoU on StoryTR. However, our 7B \textbf{Shorts-Moment} model, trained on ToM-guided data, improves +15.1\% relative IoU over baselines, demonstrating that \textit{narrative reasoning capability matters more than parameter scale}.

An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement

arXiv:2604.22777v1 Announce Type: new Abstract: Fault diagnosis of general aviation aircraft faces challenges including scarce real fault data, diverse fault types, and weak fault signatures. This paper proposes an intelligent fault diagnosis framework based on multi-fidelity digital twin, integrating four modules: high-fidelity flight dynamics simulation, FMEA-driven fault injection, multi-fidelity residual feature extraction, and large language model (LLM)-enhanced interpretable report generation. A digital twin is constructed using the JSBSim six-degree-of-freedom (6-DoF) flight dynamics engine, generating 23-channel engine health monitoring data via semi-empirical sensor synthesis equations. A three-layer fault injection engine based on failure mode and effects analysis (FMEA) models the physical causal propagation of 19 engine fault types. A multi-fidelity residual computation framework comprising paired-mirror residuals and GRU surrogate prediction residuals is proposed: the high-fidelity path obtains clean fault deviation signals using nominal mirror trajectories with identical initial conditions, while the low-fidelity path achieves online real-time residual computation through a multi-step prediction GRU surrogate model. A 1D-CNN classifier performs end-to-end diagnosis of 20 fault classes. An LLM diagnostic report engine enhanced with FMEA knowledge fuses classification results, residual evidence, and domain causal knowledge to generate interpretable natural language reports. Experiments show the paired-mirror residual scheme achieves a Macro-F1 of 96.2% on the 20-class task, while the GRU surrogate scheme achieves 4.3x inference acceleration at only 0.6% performance cost. Comparison across 24 schemes reveals that residual feature quality contributes approximately 5x more to diagnostic performance than classifier architecture, establishing the "residual quality first" design principle.

PExA: Parallel Exploration Agent for Complex Text-to-SQL

arXiv:2604.22934v1 Announce Type: new Abstract: LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. We validated our framework on a state-of-the-art benchmark for text-to-SQL, Spider 2.0, achieving a new state-of-the-art with 70.2% execution accuracy.

DeepSeek V4 is Here. What Does it Mean for Enterprise Productivity? - UC Today

UC Today delivers insights for IT leaders and buyers covering Agentic AI, Agentic AI in the Workplace​, AI Agents, Generative AI, Workflow Automation, collaboration, employee experience and workspace tech.

Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings

arXiv:2604.22776v1 Announce Type: new Abstract: A chef's intuition about flavor, texture, and cultural identity represents tacit knowledge that is difficult to articulate yet central to culinary practice. We show that this knowledge is already encoded in FlavorGraph's 300-dimensional ingredient embeddings, trained on recipe cooccurrence and food chemistry, and that it can be systematically recovered. An LLM-augmented curation pipeline consolidates 6,653 raw FlavorGraph ingredients into 1,032 canonical entries, substantially strengthening the recoverable structure. We identify at least fifteen independently classifiable dimensions spanning taste, texture, geography, food processing, and culture.

Evaluating Historical Data Constraints on Modern Large Language Model Reasoning Capabilities

A small-scale language model trained exclusively on pre-1931 text explores the limits of historical data in modern reasoning tasks. This experiment tests whether models can derive contemporary inventions or coding skills from archaic datasets.

Assessing On-Device Efficiency and Utility of Small-Scale Historical Language Models

A small language model trained on pre-1931 text demonstrates the feasibility of running specialized AI on local hardware. While technically efficient, the model's limited reasoning capabilities highlight the trade-offs between model size and utility.

System Prompt Anomalies in OpenAI's Codex Model Highlight AI Development Unpredictability

The discovery of unusual instructions in the system prompt for OpenAI's Codex model illustrates the opaque nature of AI development. These anomalies underscore the challenges in controlling and interpreting large-scale model behavior.

Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction

arXiv:2604.22979v1 Announce Type: new Abstract: We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI) under the joint requirements of causal interpretability, symbolic controllability, and direct operation on high-dimensional raw signals. Deep neural models achieve strong predictive performance on CSI-based HAR (CHAR), yet rely on continuous latent representations that are opaque and difficult to modify; purely symbolic approaches, in contrast, cannot process raw CSI streams. We propose a fully automatic and strictly decoupled pipeline in which CSI magnitude windows are compressed by a categorical variational autoencoder with Gumbel-Softmax latent variables under a capacity-controlled objective, yielding a compact discrete representation. The encoder is then frozen and used as a deterministic mapping to one-hot latent trajectories. Causal discovery is performed on these trajectories to estimate class-conditional temporal dependency graphs. Statistically supported lagged dependencies are translated into Linear Temporal Logic (LTL) rules, producing a fully symbolic and deterministic classifier based solely on rule evaluation and aggregation, without any learned discriminative head. Because rules are defined over discrete latent variables, antenna-specific rule sets can in principle be combined at the symbolic level, enabling structured multi-antenna fusion without retraining the encoder. Results from CHAR Latent Temporal Rule Extraction (CHARL-TRE) indicate competitive performance while preserving explicit temporal and causal structure, showing that deterministic symbolic classification grounded in unsupervised discrete latent representations constitutes a viable alternative to end-to-end black-box models for wireless HAR.

On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation

arXiv:2604.22958v1 Announce Type: new Abstract: Preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such preferences control the transformation of attacks into defeats, and different approaches to doing so result in different reductions from a PAF to an AAF. In this paper we consider a PAF inverse problem which takes an argumentation graph, a labelling and a semantics as an input, and outputs a ``yes" or ``no" as to whether there is a preference relation between the arguments which can yield the desired labelling. This inverse problem has applications in areas including preference elicitation and explainability. We consider this problem in the context of the four most widely-used preference based reductions under the complete semantics. We show that in most cases, the problem can be answered in polynomial time.

Opinion | After Mythos, Nobody Is Safe From Cybersecurity Threats - The New York Times

Nobody can afford to be relaxed about their digital security anymore.

Malicious AI Prompt Injection Attacks Increasing, but Sophistication Still Low: Google - SecurityWeek

Google has analyzed AI indirect prompt injection attempts involving sites on the public web and noticed an increase in malicious attacks.

A Decoupled Human-in-the-Loop System for Controlled Autonomy in Agentic Workflows

arXiv:2604.23049v1 Announce Type: new Abstract: AI agents are increasingly deployed to execute tasks and make decisions within agentic workflows, introducing new requirements for safe and controlled autonomy. Prior work has established the importance of human oversight for ensuring transparency, accountability, and trustworthiness in such systems. However, existing implementations of Human-in-the-Loop (HITL) mechanisms are typically embedded within application logic, limiting reuse, consistency, and scalability across multi-agent environments. This paper presents a decoupled HITL system architecture that treats human oversight as an independent system component within the agent operating environment. The proposed design separates human interaction management from application workflows through explicit interfaces and a structured execution model. In addition, a design framework is introduced to formalize HITL integration along four dimensions: intervention conditions, role resolution, interaction semantics, and communication channel. This framework enables selective and context-aware human involvement while maintaining system-level consistency. The approach supports alignment with emerging agent communication protocols, allowing HITL to be implemented as a protocol-level concern. By externalizing HITL and structuring its integration, the system provides a foundation for scalable governance and progressive autonomy in agentic workflows.

Mistral AI launches Workflows, a Temporal-powered orchestration engine already running millions of daily executions

Mistral AI, the Paris-based artificial intelligence company valued at €11.7 billion ($13.8 billion), today released Workflows in public preview — a production-grade orchestration layer designed to move enterprise AI systems out of proofs of concept and into the business processes that generate revenue. The product, which launches as part of Mistral's Studio platform, is the company's clearest articulation yet of a thesis that is quietly reshaping the enterprise AI market: that the bottleneck for organizations adopting AI is no longer the model itself, but the infrastructure required to run it reliably at scale. "What we're seeing today is that organizations are struggling to go beyond isolated proofs of concept," Elisa Salamanca, head of product at Mistral AI, told VentureBeat in an exclusive interview ahead of the launch. "The gap is operational. Workflows is the infrastructure to run AI systems reliably across business-critical processes." The release arrives at a pivotal moment for both Mistral and the broader AI industry. The dedicated agentic AI market has been valued at approximately $10.9 billion in 2026 and is projected to reach $199 billion by 2034. Yet despite that staggering growth trajectory, industry research points to a stark reality: over 40% of agentic AI projects will be aborted by 2027 due to high costs, unclear value, and complexity. Mistral is betting that Workflows can help its enterprise customers avoid becoming one of those statistics. Mistral's new orchestration layer separates execution from control to keep enterprise data private At its core, Workflows provides a structured system for defining, executing, and monitoring multi-step AI processes — from simple sequential tasks to complex, stateful operations that blend deterministic business rules with the probabilistic outputs of large language models. Salamanca described Workflows as containing several key components. The first is a development kit that allows engineers to build orchestration logic in just a few lines of Python code. "We have also been able to expose MCP servers," she explained, referring to the Model Context Protocol standard for connecting AI systems to external tools, "so that they can actually do this with agent authoring." The second — and arguably more technically significant — component is an architecture that separates orchestration from execution. "We're decorrelating the orchestration from the execution," Salamanca said. "Execution can happen close to the customer's data — their critical systems — and orchestration can happen on the cloud or wherever they want to run it." This means the data never has to leave the customer's perimeter, a design decision with enormous implications for regulated industries where data sovereignty is non-negotiable. "Enterprises do not have to worry about us having access to the data," she added. The third pillar is observability. According to Mistral's blog post announcing the release, every branch, retry, and state change within a workflow is recorded in Studio with native support for OpenTelemetry. Salamanca noted that this is not an afterthought: "You can easily see what decisions have been taken by the workflow, by the agent, and you can deep dive into where problems are happening." Workflows is fully customizable across models — engineers can select which model handles which step and can inject arbitrary code, allowing them to blend deterministic pipelines with agentic sections. The system also supports connectors that integrate directly with CRMs, ticketing systems, support platforms, and other enterprise tools, with built-in authentication and secrets management. Why Mistral chose a code-first approach over low-code drag-and-drop builders Unlike some competitors offering drag-and-drop workflow builders, Mistral has deliberately targeted developers and engineers rather than business users. "There are a couple of solutions out there that have click-and-drag, drag-and-drop solutions for workflows," Salamanca acknowledged. "This is not the approach that we've been taking. We've been really focused towards developers and critical systems that will not scale if you're doing these drag-and-drop workflows." The decision is part of a broader philosophy at Mistral: that enterprise AI systems handling mission-critical operations — cargo releases, compliance reviews, financial transactions — require the precision and version control that only code can provide. Business users are not excluded from the picture, but their role is downstream. Once engineers write a workflow in Python, it can be published to Le Chat, Mistral's chatbot platform, so anyone in the organization can trigger it. Every step remains tracked and auditable in Studio. Under the hood, Workflows runs on Temporal's durable execution engine — a platform whose $5 billion valuation reflects how its durable execution capabilities, originally built for cloud workflow orchestration, have become essential infrastructure for AI agents requiring reliable, long-running, stateful processes. Temporal's customers include OpenAI, Snap, Netflix, and JPMorgan Chase, and its technology powers orchestration at companies like Stripe and Salesforce. Mistral extended Temporal's core engine for AI-specific workloads by adding streaming, payload handling, multi-tenancy, and observability that the base engine does not provide out of the box. "Workflows is built on top of Temporal," Salamanca confirmed. "We added all the AI requirements to make these AI workflows reliable. It provides out of the box durability, retries, state management. Whenever there's a failure, it starts again wherever it stopped." Originally spun out of Uber's Cadence project, Temporal transparently handles retries, state persistence, and timeouts, providing durable execution across failures. In late 2025, Temporal joined the newly formed Agentic AI Foundation as a Gold Member and announced an official OpenAI Agents SDK integration. By building on this infrastructure rather than creating a proprietary alternative, Mistral inherits battle-tested reliability while focusing its own engineering efforts on the AI-specific layer that sits above it. From cargo ships to KYC reviews, customers are already running millions of daily executions Mistral is not launching Workflows as a concept — the company says customers are already running the product in production, processing millions of executions daily across three primary use cases. The first is cargo release automation in the logistics sector. Global shipping still runs on paperwork, and a single cargo release can involve customs declarations, dangerous goods classifications, safety inspections, and regulatory checks spanning multiple jurisdictions. Salamanca described the scope of the problem: "Their global shipping today runs on paperwork. They have to involve customs declaration, Dangerous Goods classification, safety inspections, regulatory checks, and Workflows is now powering that with our models and business rules inside." Critically, the system keeps humans in the loop at the right moments. According to Mistral's blog, the human approval step in a workflow is a single line of code — wait_for_input() — that pauses the workflow indefinitely with no compute consumption, notifies the reviewer, and resumes exactly where it left off once approval is given. "Humans are still in the loop, but they're in the loop at the right time," Salamanca said. "They just get the validation — I don't have to go into multiple tools — and the shipment gets released." The second production use case is document compliance checking for financial institutions, specifically Know Your Customer reviews. These reviews are manual, repetitive, and traditionally require hours of analyst time per case. Salamanca said Workflows now processes these reviews in minutes and provides outputs in an auditable manner — a requirement for meeting regulatory obligations. The third example involves customer support in the banking sector. "You'd have millions of users actually asking to have credit cards blocked, or feedbacks on their account situation, on their credit feedbacks," Salamanca said. With Workflows, incoming support tickets are analyzed, categorized by intent and urgency, and routed automatically. Each routing decision is visible and traceable in Studio, and when the system gets a categorization wrong, the team can correct it at the workflow level without retraining the model. How Workflows fits into Mistral's three-layer enterprise AI platform strategy Workflows does not exist in isolation. It is the middle layer of a three-part enterprise platform that Mistral has been assembling at a rapid clip throughout 2026. At the bottom sits Forge, the custom model training platform Mistral launched in March at Nvidia’s GTC conference. Forge allows organizations to build, customize, and continuously improve AI models using their own proprietary data. At the top sits Vibe, Mistral's coding agent platform that provides the user-facing interaction layer — available on web, mobile, or desktop. Salamanca connected the three explicitly: "We just released Forge. It enables you to create your own models. But the question is, how do you put these models to do valuable work for your enterprise? That's where Workflows comes in, because this is the orchestration piece — how you blend in deterministic rules and agentic capabilities. And then if you really want to have your end users interact with these AI patterns, it's where Vibe comes into play." Forge is already seeing strong traction, Salamanca said, across two distinct patterns of enterprise demand. "First, they wanted to really build completely dedicated models to solve unique problems — transformers-based architecture for time series in the financial sector, adding new types of modalities to the LLMs," she explained. "And the second motion was about customers with really specific tasks they want to solve. Reinforcement learning really caught their attention as to how they can use Forge and Forge RL to actually have models do these tasks very well." This layered architecture — model customization, workflow orchestration, and end-user interfaces — positions Mistral as something more ambitious than a model provider. It is building a full-stack enterprise AI platform, a strategy that pits it directly against not just other AI labs like OpenAI and Anthropic, but also against the hyperscale cloud providers. The company's product portfolio now ranges, as Salamanca put it, "from compute to end-user interfaces," including data centers in Europe, document processing with its OCR model, and audio capabilities through its Voxtral models. Mistral's aggressive scaling campaign and the $14 billion valuation powering it The Workflows launch comes as Mistral executes one of the most aggressive scaling campaigns in the history of the European technology industry. The French AI startup has increased its revenue twentyfold within a year, with co-founder and CEO Arthur Mensch putting the company's annualized revenue run rate at over $400 million, compared to just $20 million the previous year. The Paris-based company aims to achieve recurring annual revenue of more than $1 billion by year-end. The company's fundraising trajectory has been equally dramatic. Mistral announced a €1.7 billion ($1.9 billion) Series C round at a €11.7 billion ($12.8 billion) valuation in September 2025. Bloomberg reported in September 2025 that the company was finalizing a €2 billion investment valuing it at €12 billion ($14 billion). ASML led the round and contributed €1.3 billion, a landmark investment that aligned chip manufacturing expertise with frontier AI development and underscored European industrial capital's commitment to building a sovereign AI ecosystem. Mistral then secured $830 million in debt in March 2026 to buy 13,800 Nvidia chips for a new data center near Paris. The financial picture illustrates why Workflows matters strategically. Mistral's revenue growth is being driven primarily by enterprise adoption, with approximately 60% of revenue coming from Europe, according to CEO Mensch's public statements. Those enterprise customers are not buying Mistral's models for casual chatbot applications — they are deploying them in regulated, mission-critical environments where reliability and data sovereignty are table stakes. Workflows gives those customers the production infrastructure they need to actually deploy AI systems that matter. In May 2025, Mistral released Mistral Medium 3, which was priced at $0.40 per million input tokens and $2 per million output tokens. The company said clients in financial services, energy, and healthcare had been beta testing it for customer service, workflow automation, and analyzing complex datasets. That model now becomes one of many that can be plugged into Workflows, creating a flywheel where better models drive more workflow adoption, which in turn drives more inference revenue. Where Mistral's orchestration play fits in an increasingly crowded competitive landscape Mistral's entry into workflow orchestration arrives in an increasingly crowded field. AI orchestration platforms are quickly becoming the backbone of enterprise AI systems in 2026, and as businesses deploy multiple AI agents, tools, and LLMs, the need for unified control, oversight, and efficiency has never been greater. Major cloud providers — Amazon with Bedrock AgentCore, Microsoft with Copilot Studio, Google with Vertex AI's agent tools, and IBM with WatsonX — all offer some form of workflow or agent orchestration. Open-source frameworks like LangChain, LlamaIndex, and Microsoft AutoGen provide developer-level building blocks. And dedicated orchestration startups are proliferating. Mistral's differentiation rests on three pillars. First, vertical integration: because Workflows is native to Studio, the orchestration layer and the components it orchestrates — models, agents, connectors, observability — are built to work together, eliminating the integration tax that enterprises pay when stitching together disparate tools. Second, deployment flexibility: the split control-plane/data-plane architecture means customers in regulated industries can run execution workers in their own environments while still benefiting from managed orchestration. Third, data sovereignty: Mistral's European roots and infrastructure investments give it a natural advantage with organizations wary of routing sensitive data through U.S.-headquartered cloud providers — a concern that has intensified amid ongoing geopolitical tensions and growing European anxiety about relying on foreign providers for over 80% of digital services and infrastructure. Still, the challenges are real. OpenAI and Anthropic both have significantly larger model ecosystems and developer communities. The hyperscalers control the cloud infrastructure where most enterprise workloads actually run. And the enterprise sales cycles for production-grade AI deployments remain long and complex, requiring deep technical integration work that even well-funded startups can struggle to staff. What comes next for Workflows — and why Mistral thinks orchestration is the real AI battleground Salamanca outlined three areas of near-term development. First, Mistral plans to release a more managed version of Workflows that abstracts deployment logic for developers who don't need granular control over worker placement. "Whenever you want to have this flexibility, you can, but if you want to be able to have this on a managed infrastructure, even if it's running in your own VPC, this is something that we're adding," she said. Second, the company intends to make Workflows accessible to business users, not just engineers. "With Vibe code, you can actually author a workflow. This can be executed at scale, and any end user, in the end, can actually do that with Workflows," Salamanca explained. The third area is enterprise guardrails and safety controls for agentic applications — ensuring agents use the correct tools, run with appropriate permissions, and that administrators can enforce policies at scale. "Making sure that we have all these enterprise controls to be able to scale the authoring and the building of these workflows is something we're actively working on," she said. The Python SDK for Workflows (v3.0) is now publicly available. Developers can try the product in Studio and access documentation and demo templates immediately. Mistral will be hosting its inaugural AI Now Summit in Paris on May 27–28, where the company is expected to provide additional details on its platform roadmap. For three years, the AI industry has been captivated by a single question: who can build the most powerful model? Mistral's Workflows launch suggests the company has moved on to a different question entirely — one that may prove far more consequential for the enterprises writing the checks. It's not about which model is smartest. It's about which one can actually show up for work.

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