Matei Zaharia and Reynold Xin discuss why the Frontier Ecosystem must remain open for advancements in AI. They detail Databricks' push beyond the lakehouse into a comprehensive data-and-AI operating system. Key topics include Omnigent, an open-source meta-harness for managing coding agents, and LTAP to unify the storage layer for modern databases. The discussion addresses challenges like agent portability, security, and the growing importance of robust data foundations as AI agents become integral to business workflows. Ultimately, they argue traditional software will be re-written once data is centralized and agents are built atop it.
A discussion analyzing New York primary results noted that every candidate backed by Zohran Mamdani secured a victory, raising questions about Democratic Party control. The segment also covered growing divisions within Trump's coalition as Republicans aligned with Democrats against the Iran conflict. Additionally, it addressed conflicting White House and Tehran accounts regarding nuclear negotiations. Other topics included Pete Hegseth's actions against military leaders and Tucker Carlson and Marjorie Taylor Greene distancing themselves from the GOP. Furthermore, Trump reportedly blocked a bipartisan housing bill amid affordability concerns.
Prof G Media has launched a live video podcast titled "Raging Perspective." The program features participants Jessica Tarlov and Aaron Parnas. Viewers can watch the current broadcast via Substack. The content is available as a live stream from Prof G Media.
An upcoming series, The Storyteller’s Toolkit, will release storytelling knowledge from the Prof G Media team exclusively for paid subscribers. Katherine Dillon will host a masterclass on Design as Differentiator tomorrow at 12:30 p.m. ET. Additionally, the Wednesday newsletter, Extra Credit, provides in-depth business insights paired with narrative techniques from their playbook. A public livestream is scheduled featuring Rep. Josh Turek regarding Iowa's political future. Finally, a "Predictions Mid-Year Check-In" will occur on June 30 at 3 p.m. ET for Prof G+ members to review forecasts.
Anthropic launched Claude Tag, a Slack-based workflow allowing teams to assign tasks to Claude while retaining context across conversations. ByteDance released Seedance 2.5, an AI model capable of generating 30-second, 4K videos from a single prompt using up to 50 reference materials. Mistral introduced OCR 4, an advanced document intelligence tool supporting 170 languages with structured content extraction and high accuracy. Airbyte Agents address high AI operational costs by providing a Context Store, reducing token usage and cost for agents accessing business data. Overall, the updates cover advancements in workflow integration, video generation, optical character recognition, and agent efficiency.
New York City saw a sweep of victories for Democratic candidates backed by Mayor Zohran Mamdani. Congressional Republicans and Democrats united to pass a resolution opposing the Iran conflict, checking Trump's authority in that area. Despite calls regarding inflation and housing affordability, Trump blocked passage of a bipartisan housing bill. Further political flux included Pete Hegseth removing a military leader and Tucker Carlson stating his disinterest in the Republican Party. The segment also noted conflicting reports from the White House and Tehran concerning nuclear negotiations.
A proposed method for artificially cooling Earth involves an uncrewed aircraft operating in the stratosphere, about 20 kilometers high. This plane would release materials designed to reflect sunlight back into space, mimicking the effect of large volcanic eruptions. Research on solar geoengineering is shifting focus from computer models to practical engineering challenges. These necessary tasks include designing specialized, high-altitude aircraft and mastering the precise chemistry for aerosol dispersal. However, significant unknowns remain regarding safety, cost, and the potential unintended consequences of such massive climate manipulation.
Google Wallet now allows users to utilize their Digital ID for TSA PreCheck screening touchlessly. This update enhances the travel experience by integrating digital identification directly into the mobile wallet. Travelers can now use this feature at participating airports equipped with TSA PreCheck technology. The implementation streamlines the security process, promoting faster passage through airport checkpoints. More travelers will benefit from this convenient, contactless verification method for air travel.
Qualcomm plans to acquire Modular, an AI platform developer. This acquisition significantly broadens the chipmaker's reach within artificial intelligence infrastructure. Previously focused on edge devices, Qualcomm will now expand its capabilities into data center environments. The move solidifies Qualcomm's commitment to becoming a major player in comprehensive AI solutions across various computing scales.
Blackstone has pledged $30 billion toward Japan's intensifying Artificial Intelligence sector competition. This major commitment aligns with numerous other multi-billion-dollar investments in AI data centers within the country. The funding underscores significant corporate interest in Japan's technology infrastructure growth. Such large-scale capital influx signals a robust and accelerating race for AI dominance in Japan.
Shopify implemented an LLM proxy enabling engineers to seamlessly switch between multiple AI providers with automatic failover capabilities. To boost efficiency, they utilize distillation, creating specialized, smaller language models (SLMs) that can be faster and cheaper than general-purpose alternatives. This infrastructure approach allows Shopify users to experiment broadly across various "harnesses" rather than being tied to one model. Additionally, an internal dashboard tracks usage patterns, preventing excessive token spending through built-in circuit breakers. The overall strategy aims to shift focus from AI experimentation ("reflexivity") toward practical, deep workflow implementation ("leverage").
Datacenters and the rise of AI are becoming increasingly frequent targets for global environmental lawsuits. An analysis of approximately 3,600 climate-related lawsuits filed since 2015 reveals this trend. The latest review by the London School of Economics found mounting legal challenges against datacenters' operations. These cases specifically question the energy sources utilized, water consumption levels, and resultant air pollution. Such litigation is occurring in diverse jurisdictions, including the US, UK, Chile, and Ireland.
Metropolitan police will receive a twelve-month extension for its pilot project utilizing Palantir's technology. This extension allows the force time to procure a long-term supplier for the system. The decision follows Mayor Sadiq Khan blocking an initial £50 million deal between the Met and Palantir. The stalled agreement was intended to automate intelligence analysis within criminal investigations.
Spending on government contracts utilizing AI-powered tools for tracking immigrants is rapidly increasing under proposed Trump 2.0 policies. A recent report analyzed agreements between ICE/CBP and eleven surveillance technology companies. The funding allocated to these firms was projected to double between 2024 and 2025. Furthermore, the awarded funds were forecast to reach a record $513 million by 2026. This indicates an unprecedented growth in the US government's technological immigration surveillance capabilities.
Fund manager Geoff Wilson removed an AI-generated video he had shared from a rightwing nationalist account. The video depicted Anthony Albanese and Jim Chalmers allegedly accepting money from white Australians for recently arrived migrants wearing Islamic face coverings. Wilson admitted to reposting the content without viewing it in its entirety or vetting other sources. He also deleted other posts that morning after receiving contact from Guardian Australia. The removed content included material related to the QAnon conspiracy theory.
Self-driving vehicles, once science fiction, are now being tested on London's streets by companies like Waymo. While initial tests presented hiccups, such as getting stuck or driving into crime scenes, the technology continues to advance. Currently, these automated vehicles rely on trained drivers for backup control, but human oversight is expected to decrease soon. Waymo and a local rival, Wayve, plan to launch driverless minicabs in London this year pending necessary government approvals. Despite the challenges of navigating dense urban areas like London, these technologies offer significant potential benefits, particularly for individuals with disabilities.
Examines the current landscape and anticipated trajectory of Artificial Intelligence up to 2026. Discusses key advancements shaping various sectors through sophisticated technical analysis. Provides insights into ethical considerations that must accompany further AI development. Forecasts major shifts in how industries will integrate and rely on increasingly advanced AI tools. Offers a balanced perspective on both the immense potential and necessary precautions regarding future AI technologies.
Matei Zaharia and Reynold Xin discuss the necessities for companies aiming to build "Agent Clouds." They emphasize that the Frontier ecosystem must remain open for this development to succeed. Building such advanced cloud environments requires a commitment to openness across the industry. The conversation focuses on the architectural principles needed for widespread enterprise adoption of these agent-based platforms. Ultimately, an open frontier is presented as the critical enabler for future innovation in AI infrastructure.
Meta-harnesses are gaining significant traction in the AI infrastructure space, building on a history of predecessor tools like Conductor and Flue. Matei Zaharia, CTO of Databricks, is betting heavily on Omnigent, an open-source, pluggable architecture designed to standardize and secure diverse coding or knowledge work agents. Simultaneously, OpenAI announced Jalapeño, its first custom AI chip built with Broadcom for inference across various services. This push toward custom silicon indicates that advanced, specialized hardware is becoming essential infrastructure for frontier AI labs. The sector remains highly competitive, with announcements regarding unified inference endpoints and compiler advancements further fueling the buildout of complete AI stacks.
Strong storytelling skills are highlighted as a valuable asset in today's job market. A series of exclusive content called "The Storyteller's Toolkit" will be released for Prof G+ paid subscribers to help users improve these skills. The first event is an executive producer masterclass on "Design as Differentiator." Additionally, the "Extra Credit" newsletter provides deep dives into business topics alongside storytelling techniques. Finally, the author will host a live "Predictions Mid-Year Check-In" for subscribers only.
Anthropic released Claude Tag, a Slack-based workflow enabling teams to assign tasks directly to Claude. This system connects Claude to various tools and codebases while maintaining context across different channels. The company noted its product team uses Claude Tag for generating code, analytics support, and debugging assistance. Separately, ByteDance launched Seedance 2.5, an AI video model capable of creating 30-second, 4K videos from one prompt. Mistral also released OCR 4, a document intelligence tool supporting 170 languages with high accuracy and speed for structured content extraction.
OpenClaw is an open-source framework for building personal AI assistants capable of automating tasks across various platforms like WhatsApp, Discord, and web chats. Its core strength lies in deep personalization, allowing configuration based on the user's specific context and tools. The project gained significant traction, notably accumulating over 200,000 GitHub stars rapidly. The technical deep dive explores its architecture, detailing components such as SOUL.md and HEARTBEAT.md. Furthermore, the article provides readers with several lightweight alternatives for constrained hardware or simpler deployment needs.
Artificial intelligence demands increasingly fast and powerful semiconductor chips. ASML currently maintains a near-monopoly on the expensive equipment required for patterning these advanced components. To manage rising energy consumption from data centers, some companies propose utilizing software solutions rather than solely building more power plants. Research is also beginning to investigate technologies aimed at cooling the planet in response to climate change. Beyond technology, humanity continues exploring deep-sea engineering feats and fundamental physics questions regarding the universe's composition.
A unified "Context Store" allows AI agents to synthesize knowledge from disparate business systems like Salesforce, Zendesk, and Stripe. Instead of limiting answers to a single tool, agents can now process information holistically—for instance, answering a query by cross-referencing deal stages, support tickets, and call records simultaneously. Airbyte provides this through live, searchable indexing across numerous connected applications with one authentication process. This capability significantly improves accuracy by giving agents a complete, real-time view of the customer or project context. Benchmarks show using the Context Store can reduce query tokens and tool calls while realizing substantial cost savings on multi-source queries.
ASML has developed a massive lithography machine, comparable in size to a double-decker bus, essential for advanced microchip manufacturing. This technology uses extreme-ultraviolet (EUV) light, generated by lasers striking molten tin drops. The new apparatus boasts an unprecedented resolution of eight nanometers, allowing for the creation of increasingly dense circuitry on silicon wafers. These sophisticated machines are crucial for maintaining Moore's Law and supporting the growing demand from AI development. Currently, each unit is selling to chipmaking factories at a price point of $400 million.
IBM has developed a new prototype chip featuring approximately 100 billion transistors on a fingernail-sized area. This represents twice the density of their prior state-of-the-art technology from 2021. Such advanced design aims to facilitate the development of faster and more energy-efficient computing systems for an extended period. The breakthrough suggests a potential extension of Moore's Law for another decade.
OpenAI researchers detailed in a recent paper that AI agents are significantly reshaping professional workflows. These agents possess the capability to manage tasks requiring greater duration and complexity than previously possible. This advancement promises a substantial expansion of overall productivity across various job functions. Agents act as advanced assistants, augmenting human capabilities by handling intricate operational steps. Ultimately, this development points toward a fundamental shift in how work is performed.
An image featuring the Gemini 3.5 logo is presented against a solid blue backdrop. This visual suggests an introduction or announcement related to this specific iteration of the AI model. The context implies a focus on advancements associated with Gemini 3.5. No further descriptive text elaborates on the capabilities or functionality of the new version. Essentially, it serves as a straightforward branding reveal for Gemini 3.5 Flash.
OpenAI and Broadcom are introducing a new AI inference chip. This development aims to provide AI model creators with more options for pricing their services. Specifically, the chip could allow developers to offer lower per-token pricing. Such an introduction may alleviate cost concerns currently faced by businesses regarding high token expenditures.
Mistral AI has released OCR 4 to address challenges associated with unstructured data. This new iteration incorporates specific features designed to improve user understanding of complex data formats. Among these enhancements are detailed bounding boxes included in the output. These visual aids pinpoint and delineate elements within the unstructured content. Overall, OCR 4 provides users with greater clarity when processing varied types of raw information.
Mindstone launched Rebel, a local-first agentic AI operating system that uses markdown files for all state, prompts, and instructions. A key feature is its ability to automatically orchestrate tasks by dynamically routing steps between different AI models—local or cloud-based—to balance cost, security, and capability. The platform allows users to create reusable "Skills" and scheduled "Automations" for repeatable workflows. Rebel's architecture mitigates vendor lock-in by keeping instructions stored locally as plain text files. It utilizes a tiered memory structure to manage contextual information efficiently across projects.
Released Qwen-AgentWorld, Alibaba trained models to predict environment returns rather than instructing actions within them, covering seven domains like Web and Android. This method involves a language world model predicting the next state based on an agent's action, representing a departure from typical agent training objectives. Training in controlled simulations significantly improved performance across benchmarks, sometimes surpassing gains from real-world testing alone. The research argues that world modeling is a crucial element for building general-purpose agents. This suggests that foundational environment understanding, gained through simulation, can be a vital layer complementing direct real-environment reinforcement learning.
Researchers introduced HarnessX, a framework that treats AI agent scaffolding as a composable object capable of autonomous code improvements. This system uses an engine called AEGIS, which frames harness adaptation as a reinforcement learning problem over modular components. HarnessX addresses limitations in traditional, static harnesses by enabling dynamic adaptation to new application requirements. A key innovation is the co-evolution process, where improving the harness simultaneously updates the foundation model using cross-harness GRPO. Practical testing demonstrated that this automated evolution yielded substantial performance gains across diverse tasks, benefiting smaller models significantly.
Baidu open-sourced Unlimited OCR, a 3B-parameter Mixture of Experts (MoE) model designed for parsing multiple document pages in one pass. This model employs Reference Sliding Window Attention (R-SWA) to maintain a constant Key-Value (KV) cache. Keeping the KV cache flat ensures that memory usage and latency do not increase as the output length grows. Unlimited OCR achieved a high score of 93.23 on OmniDocBench v1.5. The model is made available under an MIT license.
Gradium has launched two new real-time speech translation models: stt-translate and s2s-translate. These models support English, French, German, Spanish, and Portuguese across twenty language pairs. They improve efficiency by combining transcription and translation into a single pass alongside a Gradium TTS stage via a duplex WebSocket. Comparative testing shows these new models outperform gpt-realtime-translate in both accuracy and latency metrics. Additionally, the system offers features such as output voice selection and cloning capabilities.
Guides building an agent harness mirroring the OpenHarness style from scratch for practical understanding. The recreation includes core components such as tool use, typed schemas, permissions, lifecycle hooks, memory, and skills. It also incorporates mechanisms like context compaction, retry logic, cost tracking, and multi-agent coordination. A key feature is exposing the entire control flow rather than using a black-box framework approach. Everything developed remains runnable for experimentation without requiring external API keys or infrastructure.
OpenAI has announced a new, specialized custom AI chip designed for advanced computing tasks. This hardware aims to significantly boost AI processing capabilities for various applications. While the announcement details the chip's technical specifications, it also includes promotional offers. One such promotion allows users to receive deals on World Cup tickets through an incorporated AI price tracking service.
A new technological focus is emerging as "Meta-Harness" is gaining attention. This concept suggests a comprehensive layer or framework that integrates multiple existing systems. The name implies a level beyond traditional or single-domain engineering approaches. Instead of just optimizing one part, Meta-Harness aims to govern and connect several distinct functionalities simultaneously. It signals a shift toward highly interconnected technological architectures.
Converted Mozilla's extensive MDN browser compatibility data into a SQLite database format. A script generated by Claude Code for web was used to perform this conversion process. The resulting approximately 66MB database is hosted in a regular GitHub repository rather than releases to allow for open CORS headers. A GitHub Actions workflow, built with Codex Desktop (GPT-5.5), automates building and force-pushing the database to an "orphan" branch. Users can download the database directly from the repo or explore it using Datasette Lite due to the open CORS settings.
Provides a practical guide for new data engineers starting at a company. It covers essential aspects of onboarding, including environment setup procedures. A key focus is implementing automated testing strategies for Extract, Transform, Load (ETL) pipelines. The content also incorporates modern tools like AI assistance for development efficiency. This aims to ensure early projects result in robust and reliable data workflows.
Analysis using activation patching exposed the mechanisms by which facts are stored, routed, and retrieved across transformer layers within Gemma models. Specifically, the investigation revealed that the residual stream is responsible for executing most of these factual operations. The research provides insight into the internal architecture of fact recall in smaller (2B) and larger (12B-IT) versions of the Gemma model family. This detailed look at the circuit mechanism advances understanding of factual knowledge representation in large language models.
Details a technical shift from utilizing a single AI agent to constructing a multi-agent pipeline. The explanation is demonstrated through a practical example involving text-to-SQL functionality. This approach suggests that combining several specialized agents is more effective than relying on one comprehensive agent. Such pipelines are designed to tackle complex tasks by distributing the workload across multiple, cooperating components. The content was initially published on Towards Data Science.
Presents a method for anchor detection in Retrieval-Augmented Generation (RAG) pipelines. The technique utilizes parallel detectors to identify relevant sections of retrieved documents. After multiple initial checks, it consolidates the findings into a single call to a Large Language Model (LLM). This approach aims to improve the accuracy and efficiency of RAG systems. It contrasts with sequential retrieval methods that might process structured tables, then TOCs, and finally embeddings separately.
NVIDIA NeMo provides a framework to significantly speed up the fine-tuning process for transformer models. It achieves this acceleration through optimized implementations designed specifically for model customization. By streamlining resource management and computation, NeMo reduces the time required to adapt large language models to specific downstream tasks. This efficiency gain makes advanced model fine-tuning more accessible and practical for researchers and developers. Utilizing NeMo allows users to leverage powerful transformer architectures with greater speed and lower computational overhead.
Murakkab is a novel system designed to improve the efficiency of complex AI applications. It specializes in optimizing both the design and practical deployment of multistep workflows. This optimization directly targets enhanced speed and improved energy consumption within AI agents. By refining how these multi-step processes are structured and executed, Murakkab aims to boost overall performance. Ultimately, it provides a comprehensive method for making AI workflows faster and more power-efficient.
Updated machine learning notes are available on a GitHub repository. These comprehensive notes cover foundational topics like the policy gradient theorem. The update also incorporates more recent advancements in reinforcement learning. Specific material included reaches up to GRPO. Access can be found at the provided link.
Introduces MuJoFil, a new open-source simulator designed to overcome MuJoCo's CPU limitations for vision-based Reinforcement Learning. It integrates Nvidia's GPU native Newton Physics Engine with Google's Filament render engine for high visual fidelity. Key features include supporting parallel simulation runs and providing PBR texture support. The simulator can use diverse environments sourced online from formats like GLB and OpenUSD, moving beyond MuJoCo's native limitations. Currently available via two Python packages: `mujofil` (CPU) and `mujofil-warp` (GPU).
A three-part series titled "From A/B to RL" bridges the concepts of A/B testing into reinforcement learning (RL). The first part focuses on Bayesian A/B testing principles. Part two transitions from fixed experiments to online learning techniques, covering multi-armed bandits and Thompson sampling. The final installment builds upon this by incorporating state-dependent policies and delayed rewards using examples like tic-tac-toe. These materials were refined from the author's personal Jupyter notebook drafts for teaching RL concepts.
Successfully reimplemented DreamerV4 in PyTorch and evaluated its closed-loop performance on dm\_control ball\_in\_cup\_catch. The results showed that imagination-RL provided only a marginal, statistically insignificant improvement over simple behavior cloning ($\approx 0.38$ vs $0.32$ catch rate). Investigation revealed the primary bottleneck is out-of-distribution (OOD) state coverage rather than issues with the policy head or advanced offline techniques like Advantage-weighted BC. The author concludes that closing this performance gap necessitates an online RL approach, suggesting offline methods are fundamentally limited by data coverage.
Rule-based expert systems utilize inference through forward and backward chaining, offering clear, traceable, and easily implemented rules that allow for flexible additions. Conversely, object-based systems employ message passing for inference. Object-based methods are advantageous due to their enhanced reusability and modularity, making them suitable for large, complex applications. The comparison highlights the distinct operational mechanisms of these two expert system types.
A user questions if computational expense is the sole disadvantage of using Bayesian Optimization for hyperparameter tuning in an Actor-Critic Reinforcement Learning controller. The user suggests running the optimization process continuously on a remote university PC. They are asking if leaving the optimization running perpetually would be feasible while they work on other tasks at that location.
Princeton researchers are utilizing reinforcement learning and inverse design to rapidly create complex Radio Frequency Integrated Circuits (RFICs) from scratch. These AI methods can generate novel RF layouts with high performance while drastically reducing the time needed compared to traditional human design processes. Current advancements show that AI can achieve designs orders of magnitude faster than human engineers for these "dark art" circuits. Future progress in wireless technology relies on mastering this process, which currently constrains innovation in areas like 5G and autonomous vehicles. Success requires developing large, shared chip design datasets and open ecosystems to enable universal AI learning for electromagnetic behavior.
Three former Duke decathletes founded Jeani, an AI-powered startup. Jeani utilizes data from smartwatches to detect subtle changes in movement patterns. These changes can signal potential joint issues or overuse injuries before they escalate into serious conditions. The application aims to improve preventative care within the wearables market. It is currently available for $10 per month on Apple's App Store.
Researchers discovered that Anthropic's AI, Claude, can partially reconstruct an internal training document called a "soul document." This document dictates the AI's core identity, values, and engagement style, residing deeper than standard system prompts. The text posits this document defines *who* the AI is, rather than just what it can perform. Maintaining such a document is crucial for providing self-continuity across discrete conversational sessions. Ultimately, this highlights the effort to externalize the constructed self of an advanced AI.
A free system guides users in leveraging AI beyond simple search functions. Data scientist Tina Huang demonstrates a repeatable process for using AI like a full production team, not just an assistant. This methodology involves several steps: turning initial brain dumps into concise briefs, evolving ideas into structured outlines, and refining drafts into publication-ready work with minimal revisions. Essentially, the guide teaches users to treat AI as a capable employee for comprehensive content creation.
A record-breaking heat wave across Europe is stressing the electrical grid due to increased use of fans and air conditioning. The extreme demand is forcing some power plants to cease operations to manage the load. France experienced its hottest day on June 23, marking a significant milestone in temperature records since 1947.
The daily newsletter, "The Download," provides updates on global technological developments. This edition introduces a specific feature focusing on engineering challenges. It suggests that while not every problem is solvable, humanity can make progress. The core message centers on using human ingenuity to improve the world. This new focus highlights ambitious efforts in making positive societal change.
Stripe, along with Anthropic and OpenAI, is backing an initiative aimed at combating respiratory infections. The effort seeks to address common colds, which are frequent without known preventative measures. While lifestyle changes like taking vitamin C can help, these groups are funding a novel approach. Specific details of the proposed intervention are not fully outlined in the provided text snippet.
Talos is an open-source system designed to alleviate the diagnostic bottleneck caused by extensive human review time in genomic medicine. The platform achieves this through automated, iterative genomic reanalysis. It successfully recovered 90% of relevant diagnoses for patients. Furthermore, Talos significantly reduced the workload on experts by presenting only 1.3 candidate variants per patient for manual review. Initial details about this system were published by Microsoft Research.
Stanford researchers developed an autonomous virtual biotech utilizing thousands of AI "scientist" agents to simulate the entire drug discovery lifecycle. This system employs a hierarchical framework, managed by a chief scientist officer agent that delegates tasks across specialized teams for discovery and safety testing. A major advantage is the continuous context retention, overcoming knowledge loss common in current multi-stage human processes. The AI operates using vast primary data sources, including genomics and FDA chemistry databases. Led by James Zou, the research aims to revolutionize drug development, with plans to present findings at VB Transform 2026.
Amazon is presenting a new framework for engineering trustworthy AI agents at VB Transform 2026 because current industry reliability metrics like EVAL scores are insufficient. This approach moves beyond raw performance, focusing instead on consistency, robustness, predictability, and safety. To bridge the trust gap, Amazon emphasizes decoupled systems, using sandboxed environments requiring human review before any implementation. The framework details how companies can build multi-tool architectures capable of self-correcting during execution. These efforts directly address IT leaders' major concerns regarding unauthorized data access and prompt manipulation.
Artificial intelligence aided in virtually unwrapping and deciphering text from an ancient papyrus scroll singed by a Vesuvius eruption nearly 2,000 years ago. Researchers successfully uncovered twenty columns of previously hidden writing spanning over one meter of charred material without physically handling the fragile document. The content analyzed pertains to stoic philosophy, specifically covering topics such as ethics, art, and human conduct. Dating to the second or late-third century BC, these revelations provide new insights into ancient thought.
Explorer Scouts are launching new badges focusing on content creation, digital communication, and online safety for 14- to 18-year-olds. This initiative follows consultations with nearly 3,000 teenagers regarding skills needed in the AI and social media era. The badges are part of the Scout movement's most significant overhaul in almost 25 years. Participants will learn how digital communities shape opinions and conduct online campaigns. Skills emphasized include investigating digital footprints and designing safety toolkits for peers.
Leaked data sheds new light on Dialog, a private social club founded by Peter Thiel and Auren Hoffman. The exclusive gathering reportedly involves roughly 200 members of global elite annually at secret retreats. The society allegedly grades its attendees based on a hidden scale. Discussions within the club span broad topics ranging from sex to world wars. Furthermore, Dialog is rumored to offer matchmaking services to its membership.
Tom MacWright has observed an increase in job applications exhibiting signs of being coauthored by Large Language Models (LLMs). These submissions often include LLM-generated portfolio sites and GitHub projects with automated commit messages. He notes that such content renders the individuals behind them completely unknown to him. This "perfected, generated, prompted resume" proves generic and impersonal. Ultimately, it conveys nothing about the applicant except their familiarity with specific AI tools.
Alibaba's Qwen family of AI models is expanding into robotics with the release of the Qwen-Robot Suite. This development aims to bridge the gap between advanced comprehension and physical action, acknowledging that perception alone is insufficient for manipulation. Three new specialized models—Qwen-RobotNav, Qwen-RobotManip, and Qwen-RobotWorld—are central to this effort. These tools focus specifically on translating high-level understanding into actionable physical commands, such as joint torques. This strategic move signals a shift toward embodied intelligence rather than solely relying on benchmark performance metrics.
Engineers are constructing Rogfast, intended to be the world's longest and deepest subsea road tunnel, situated 390 meters beneath the North Sea near Norway's fjords. The project is an engineering feat involving traversing a 26.7-kilometer route under intense pressure. Key to the construction is the use of numerous explosives for blasting through difficult geological formations. This endeavor showcases advanced logistical management necessary for building such massive underwater infrastructure. The expertise gained from this build attracts international interest in similar ambitious tunneling projects.
Bird's-eye-view (BEV) projection is a prevalent technique in developing autonomous vehicle, robotics, and spatial AI systems. These models translate sensor data into a top-down perspective view, which simplifies processing complex 3D environments. Accelerating BEV pooling operations on NVIDIA GPUs is crucial for real-time performance in these applications. Improvements to this process enhance the efficiency of perception pipelines. Faster BEV processing enables more robust and reliable physical AI systems.
Qwen, a prominent AI model, is expanding its functionality to include embodied artificial intelligence. This represents an advancement toward integrating sophisticated AI with physical robotics. Embodied AI enables AI systems to interact with and perceive the real world through physical means. Such developments mark a significant step in frontier AI research. Essentially, the model is transitioning from purely digital processing to real-world physical application.
RIFT-Bench is a new methodology designed for dynamic red-teaming of autonomous, agentic AI systems powered by LLMs. It addresses the limitation of current evaluations being tied to specific implementations by providing unified comparison across diverse architectures. The framework operates in two automated phases: Discovery, which extracts the system's structure, and Scanning, which deploys adaptive adversarial attacks. RIFT-Bench evaluates the target system itself using a broad set of dynamically adaptable probes across various attack vectors. Demonstrated successfully on 45 varied agentic systems, it also supports direct assessment of mitigation strategies, establishing a scalable security foundation.
Neuro-Symbolic Drive is a framework that enhances driving Vision-Language-Action (VLA) models by integrating rule-grounded reasoning traces. It addresses the issue where existing model rationales lack causal connection to planned movements. The method captures internal decision traces and executed trajectories from classical, symbolic rule-based planners in simulation. These structured, planner-derived traces are then used to fine-tune the VLA model (Qwen3.5-4B). Evaluation shows this approach significantly improves performance by structurally coupling reasoning directly to motion generation, reducing ADE@3s on a benchmark from 0.47 to 0.26 with three cameras.
The paper addresses the necessary distinction between mere automation and genuine agency in the context of advanced AI models like LLM "agents." Drawing from philosophical and science fiction concepts, it analyzes agent architectures across five dimensions: goal, identity, decision-making, self-regulation, and learning. A key argument posits that true agency requires these structures to be internalized within the system rather than relying on external scaffolding. The authors propose a Goal-Identity-Configurator (GIC) architecture incorporating hierarchical goal decomposition and simulated reasoning for general-purpose agents. Finally, they provide insights into ensuring the safety and controllability of highly autonomous, "agentive" systems under human supervision.
Develops a hierarchical multi-agent reinforcement learning framework for safety-critical systems requiring coordinated behavior under strict constraints. Addresses the inherent trade-off between high empirical performance from learned methods and theoretical safety guarantees from control theory. Enforces hard safety constraints at a low level using a constraint manifold, while managing coordination via high-level policy learning. Provides formal safety guarantees in the multi-agent context and achieves stationary learning dynamics for stable training. Empirically demonstrates competitive performance with near-perfect safety rates and strong generalization capabilities across varying agent numbers and obstacles.
Model alignment must generalize beyond training tasks for safe AI deployment, a critical challenge for reinforcement learning (RL). Researchers constructed a dataset spanning varied domains like health and education to train beneficial traits such as fairness and truthfulness via RL. Training models on this dataset significantly improved performance on over 80% of independent, out-of-distribution alignment benchmarks compared to baselines. Furthermore, focusing training in one domain, like health, yielded broad improvements across unrelated alignment tests. These findings suggest that reinforcing beneficial behavior can create models more robustly aligned with human flourishing and resistant to malicious steering.
Mechanistic interpretability struggles with explaining already localized model circuits, despite automated component identification. Researchers introduce AgenticInterpBench, a benchmark featuring 84 semi-synthetic transformer circuits for testing explanation capabilities. A proposed method called HyVE iteratively explains components through observation, hypothesis generation, and causal validation loops. Testing on four large language model backbones shows that while strong models generate good hypotheses, validation failures are common later in the process. Overall, LLM agents show promise for circuit explanation, but developing reliable validation techniques remains crucial for their success.
Recommender systems often create filter bubbles by optimizing solely for immediate user engagement. Single-objective models fail to balance engagement with crucial values like information diversity and fairness. The authors propose a multi-objective reinforcement learning framework treating recommendations as a semantic multi-objective Markov decision process. This architecture uses a Pareto-DQN agent to handle engagement, diversity, and fairness as separate reward signals, bypassing simple aggregation methods. Evaluations demonstrate that this method's hypervolume action selection maintains high state variance, improving societal objectives while minimally impacting user engagement.
Addresses mental disorders, particularly depression, prevalent among female sex workers due to violence, stigma, and economic hardship. Proposes a hybrid predictive model combining ensemble feature selection (using ANOVA and mutual information) with Harris Hawks optimization-tuned logistic regression. This novel approach applies swarm intelligence to predict mental health risks in vulnerable populations. Tested on 3,005 FSWs, the model achieved high performance metrics: 95.78% accuracy, 95.77% F1 score, and 0.96 AUC. The resultant XAI tool identifies key predictors of depression, such as post-traumatic stress and client-related violence, to guide early intervention and targeted care.
Current methods of teaching reasoning to weaker language models often rely solely on imitating specific solution steps, which encourages memorization rather than generalized problem-solving skills. Strategy-Guided Policy Optimization (SGPO) addresses this by distilling reusable strategies instead of full trajectories. SGPO extracts structured strategy descriptions and compares model behavior with and without strategic guidance. It employs a token-level forward-KL objective to selectively transfer the distribution shift caused by strategy conditioning into the base policy. Evaluations on four mathematical benchmarks demonstrated that SGPO outperforms existing methods, showing improved generalization through strategic distillation.
Recognizing algorithms' central role in AI-era scientific research, this study analyzes collective algorithmic influence via co-occurrence networks built from the full text of academic papers. Deep learning models were used to extract algorithm entities and construct comprehensive, cumulative, and annual co-occurrence networks. Analyzing these networks reveals characteristic complex network features, including progressively denser connections over about two decades. Centrality measures show that established, high-performing algorithms often maintain significant influence across research periods. This pioneering large-scale analysis offers a temporal and structural framework for understanding algorithm interconnectedness in academia.
Looped language models process hidden states into runtime states, raising questions about which variables are actually supervised by cross-entropy loss. The study demonstrates that per-loop cross-entropy only controls readout-exposed variables, not all recurrent transition variables. A key failure mode occurs because scale-invariant readouts mask radial scale from the loss, while recurrence updates this unconstrained scale. This lack of control allows early exits to be usable without regulating the model's internal recurrent scale. Consequently, effective design requires either making the scale visible to the loss or architecturally removing its influence from the loop.
A case study details the development of sign-embedding quantum algorithms for matrix equations, originating from a human intuition about rational approximation for jump functions like the sign function. AI assistance, integrated into the AIM system, was crucial in expanding this initial idea into a structured route map and connecting it to broader mathematical frameworks. The AI helped derive connections and draft complex analyses based on human direction. Significant scientific judgments, such as rejecting approximations or refining implementations, remained firmly with human researchers. Ultimately, the work suggests that AI excels as a collaborative research partner for problem formation and connection discovery within a supervised human loop.
A novel technique allows for performing lightweight Neural Architecture Search (NAS) directly on edge devices for real-time sensor data analysis. This adaptation capability is valuable for human-machine interfaces, enabling network redesign when user biometrics change after guided data collection. The proposed approach was designed and validated using the Italian Sign Language (ISL) dataset from surface electromyography (sEMG) signals. Testing on a Raspberry Pi 4 demonstrated superior performance on ISL, achieving 0.63 times less RAM usage and higher accuracy. Further validation on the CWRU fault diagnosis dataset confirmed the method's applicability across different scenarios.
Continual learning is crucial for Industrial LLMs requiring constant updates to meet evolving real-world demands. Current research often neglects the dynamic nature of industrial requirements by focusing on static benchmarks. The paper reformulates Industrial Continual Learning (ICL) as a closed-loop, versioned ecosystem update process across model levels. Three core challenges emerge: adaptation erodes plasticity, upgrades break capability inheritance, and deployment limits sustainability. To address this, five lifecycle design principles—like preserving plasticity headroom and treating upgrades as capability transfer—are proposed alongside an actionable deployment blueprint.
Investigates when fundamental conservation laws remain verifiable after a model learns a latent state representation for physical world models. Develops "certified horizons" that pre-determine how many steps a system can reliably stay on a known physical invariant's level set based on measurable model imperfections. Instead of certifying the learned Hamiltonian, it focuses certification on the decoded physical invariant obtained from the latent state. The method yields shell-horizon certificates accounting for defects in representation, readout, and latent dynamics. Results show that while conservation laws can survive learned representations, the precise geometric structure dictates the achievable horizon length.
Develops trust-horizon certification for latent world models possessing known group symmetries to ensure reliable performance over defined time spans. The method calibrates an initial raw horizon curve using a split-conformal multiplicative factor, demonstrating high reliability on audit sets with zero anti-conservative violations observed across fifty audits. A key finding is that exact equivariance ensures the calibrated trust-horizon curve remains constant along the group orbit, provided specific structural conditions are met in the model components. Empirically, these equivariant models showed small residuals and yielded non-vacuous safety certificates. In a 3D yaw audit, the equivariant model successfully generated an orbit-valid certificate where non-equivariant baselines failed or required additional costs.
Integrating distributed energy resources (DERs) is vital for power system decarbonization but faces hurdles from uncertainty and complex modeling. Reinforcement learning (RL) shows promise for DER management where traditional optimization methods fail. This work introduces a Supervised Reinforcement Learning (SRL) framework inspired by large language model training. The approach involves first pre-training the policy using supervised demonstration data, followed by iterative fine-tuning. This two-step fine-tuning process, including offline and online adaptation, significantly improves DER coordination performance over existing benchmarks.
Knowledge graph embedding models struggle with predicting multi-hop relations never seen during training (zero-shot compositional queries). Holographic Reduced Representations (HRR) were tested as a promising solution for symbol binding and unbinding in this context. Although both HRR variants performed well on single-hop retrieval, they failed to compose zero-shot queries across varying cleanup temperatures. Mechanistic analysis revealed that the failure lies not with the binding algebra or cleanup process, but with the intrinsic difficulty of retrieving intermediate facts within a superimposed memory state. Improving zero-shot composition requires enhancing the memory's overall retrieval capacity under superposition rather than solely redesigning the cleaning mechanism.
MacroLens is a new, comprehensive benchmark designed for contextual financial reasoning that integrates price history, accounting fundamentals, macroeconomic regimes, and textual data. It addresses the complexity of finance by handling data challenges like look-ahead bias from text and reporting lags for fundamentals. The dataset covers over 4,400 U.S. small- and micro-cap equities using data from 2021 to 2026. Seven tasks test various financial methods, including forecasting, valuation, and scenario-conditioned returns, across multiple advanced models. This resource is publicly released on Hugging Face for broader research evaluation.
Investigates PAC learning within tabular discounted Markov decision processes influenced by exogenous, i.i.d. contexts drawn from an unknown distribution $\mu$. The study analyzes sample complexity based on calls to sampling oracles for the context distribution and transition kernels. When rewards and transitions are known, a variance-reduced algorithm achieves policy evaluation, best-value estimation, and best-policy extraction with sample complexity independent of $|\mathcal Z|$. In the fully unknown regime requiring learning both $\mu$ and transition probabilities, policy evaluation remains free from dependence on $|\mathcal Z|$ while presenting upper bounds. These results provide tight minimax rates for various access conditions to the necessary sampling oracles.
Ensuring stable outputs from black-box models is crucial for trustworthy artificial intelligence despite diverse input structures. This paper introduces a task-oriented randomization methodology that adaptively adjusts its strategy based on the data's underlying generative mechanisms, particularly tackling unstructured complexities. It proposes a comprehensive set of theoretical stability guarantees and analyzes the inherent trade-off between stability and exploration. The framework is extended to top-k ranking problems, drawing inspiration from Large Language Model architectures. Effectiveness was demonstrated through extensive numerical simulations and real-world dataset applications.
Hyperparameter selection is crucial for deploying modern AI systems but currently relies on empirical methods lacking formal reliability guarantees. This monograph introduces a unified statistical framework to address this limitation. The framework centers on the learn-then-test (LTT) paradigm, framing the problem as multiple hypothesis testing across candidate hyperparameters. It allows selecting parameters that provably meet specific reliability criteria, such as bounds on risk or information-theoretic constraints. Furthermore, it provides explicit control over error probabilities with finite-sample guarantees.
Mean Field Variational Inference (MFVI) is known for underestimating posterior variance; however, analysis using conjugate Bayesian Linear Regression reveals it can overestimate predictive variance compared to the true posterior. Specifically, this overestimation occurs in directions where training data is concentrated. A surprising consequence is that for a test point drawn from the training distribution, MFVI's expected predictive variance exceeds the exact posterior's variance. Furthermore, the theory shows MFVI might fail to reduce predictive variance relative to the prior on in-distribution data. Varying temperature appears to correct this overestimation effect, bringing predictions closer to those of the true posterior.
Federated learning faces performance degradation due to coexisting global class imbalance and cross-client data heterogeneity. This challenge is particularly acute because global model training often ignores local variations in class distribution. The proposed solution, FedReLa, is a novel, data-level approach designed for this issue. It operates by employing a feature-dependent label re-allocator to correct biased global decision boundaries without needing knowledge of the true global class distribution. Experiments demonstrate that FedReLa significantly enhances minority and overall accuracy on long-tailed and imbalanced datasets while maintaining low communication overhead.
Investigates when synthetic data augmentation improves score-based classification metrics for imbalanced datasets. The work separates augmentation's effect into changes in class weighting and discrepancies between synthetic and true distributions. Under well-specified models, augmentation offers no fundamental population improvement over the raw estimator, only potential variance reduction or introduced bias. However, under misspecification, augmentation can qualitatively improve performance by altering class balance and correcting ranking errors from the imbalanced objective. Simulation results confirm limited gains when assumptions hold, but show non-monotonic improvements when model misspecification occurs.
Investigates linear TD(0) under Markovian sampling, proving high-probability guarantees for an unprojected algorithm using Polyak-Ruppert averaging. A single stepsize schedule, $\eta_t \propto \frac{1}{\tau_{\mathrm{mix}}\log(t)\sqrt{t}}$, is shown to guarantee uniform boundedness of the iterates without needing prior knowledge of the curvature parameter ($\omega$). This same stepsize simultaneously achieves two convergence rates: a robust, curvature-free rate and a fast, curvature-dependent rate. The convergence bound for the average takes the minimum value of these two established rates. Key to this work is applying a Poisson-equation toolkit to decompose Markov noise, enabling a new self-bounding inductive argument.
Generating asynchronous event sequences requires robust models to overcome limitations in current autoregressive or non-autoregressive methods. Proposes Latent Block-Diffusion Temporal Point Processes (LBDTPP), a novel semi-autoregressive framework for variable-length sequence generation. LBDTPP defines an autoregressive probability over latent event blocks and employs Gaussian diffusion within each block. This design preserves length flexibility while achieving the high-quality, parallel generation characteristic of diffusion models. Experiments on six real-world datasets show LBDTPP outperforms state-of-the-art TPP baselines in both unconditional and conditional tasks.
A new algebraic mixed coincidence identity unifies numerous information-theoretic variational results for any family of priors $\{\pi_i\}$ and real exponents $\{\alpha_i\}$. Taking the log of the mixed count yields a value simultaneously identifiable as a Boltzmann coincidence weight, exponential-family normalizer, maximum-entropy value, and KL-barycenter optimum. This single identity unifies derivations of classical concepts like Sanov's theorems, Chernoff information, Donsker-Varadhan inequalities, and various pattern coincidence laws. It generalizes Renyi entropy formulations to a $W$-prior simplex and applies to both normalized and continuum-indexed priors. Furthermore, the method provides advanced tools, including an explicit "coincidence bonus" for multi-prior PAC-Bayes penalties and applications to complex data like language models and genomics.
Introduces Hierarchical Partial-Order (HPO) models to rank aggregation, extending poset-based methods for preference data. These models accommodate structured group preferences by utilizing a hierarchy of latent posets, allowing information sharing across groups. The framework generalizes classical models like the Plackett-Luce model to handle partial orders and grouped data simultaneously. Additionally, it proposes a Hierarchical Clustering extension (HCPO) for unsupervised clustering when group labels are unknown. Empirical tests on acoustic preference data and LLM agent traces show that HPO and HCPO outperform existing methods in prediction and interpretability.