Space is rapidly becoming a competitive frontier for Artificial Intelligence, moving beyond mere scientific endeavor into contested economic territory. Since energy capacity is the current scarce resource defining technological frontiers, space offers an effectively unmetered energy solution. Major global actors, including hyperscalers and nation-states, are actively investing in this space race with visible hardware deployments already occurring in orbit. Proof of concept exists, such as training a large language model on Shakespeare's complete works aboard a satellite. Ultimately, the core thesis identifies compute power as fundamentally an energy problem that space helps solve.
Fable relaunched on schedule, with coverage from AIE and Richard MacManus detailing key industry updates. Multiple tools like Cursor, Devin, and Perplexity integrated or reported on the return of Fable 5. The trend highlighted is a move toward multi-model orchestration rather than relying solely on one frontier model. Builders are adapting by using advanced models for high-level planning while delegating specific tasks to other specialized models.
Autoresearch was presented as a system loop where agents help maintain the overall structure. Speaker Addy Osmani distinguished between the inner execution loop, which agents can manage, and the outer loop of human engineering agency. A recurring theme emphasized that while AI agents are highly capable, human oversight remains crucial for directing creative processes. Geoffrey Litt warned that understanding code is vital, stating those who delegate understanding risk being replaced by agents. Ultimately, experts like Paul Bakaus suggest a middle ground where AI handles much of the initial work, reserving the final critical stage for unique human input and ownership.
AI progress in 2026 is characterized by increased selectivity, modularity, and infrastructure awareness across various fronts. New research focuses on architectural innovations like DeepSeek's mHC for stable information mixing, and concepts such as Conditional Memory enabling selective knowledge retrieval rather than broad activation. Practical workflow advances include new fine-tuning stacks moving beyond reliance solely on Reinforcement Learning. The field requires understanding both these frontier techniques and underlying mechanisms like token management and attention caches. Overall, progress integrates novel model designs with optimized hardware and runtime pipelines.
Autoresearch involves creating "outer loops" where AI agents improve a primary system using feedback signals, evaluations, and human input. The focus is shifting from standalone models to these self-improving operational loops themselves being the product. Authors propose three patterns: treating the loop as the core product, tracking output via an "agent recipe," and optimizing for system efficiency alongside capability. An agent recipe acts as a portable blueprint encapsulating components like evaluations and signal processing to guide iterative development. Ultimately, the goal is to build increasingly autonomous, customizable, and cost-effective systems leveraging frontier model capabilities.
Utilize a planning tool that takes your travel route and desired departure time. This service generates a simple itinerary with essential pre-drive checks. These suggested checks include current road conditions, local weather forecasts, and parking availability for your destination. By inputting these details, the tool helps ensure you are prepared before starting your journey. This proactive planning feature enhances safety and convenience for travelers.
Prof G Media has launched a new live video on Substack titled "Raging Perspective with Jessica Tarlov & Aaron Parnas." The content is available for viewers to watch directly through the platform. This broadcast features discussions involving Jessica Tarlov and Aaron Parnas. The service was made available in 2026, according to the provided footer.
Warp CEO Zach Lloyd presented "software factories" as the next evolution in coding, defining it as orchestrating a team of AI agents. This concept moves beyond simple agent interaction towards automated systems that continuously manage the entire software development lifecycle. The platform Warp is developing to support this transition is called Oz, designed to connect various models and coding harnesses. Lloyd foresees most significant software projects operating within some form of automated factory setting within the next year.
The author began writing "The AI Report" due to frustration with the existing information ecosystem surrounding AI. Previously, content was either too technical for business professionals or too vague to be practically useful. The publication aims to bridge this gap by focusing on one core question: what does new AI development mean for improving job performance? Rather than reporting every headline, the report concentrates on providing practical examples, actionable workflows, and usable tools. This focus also guides the content to remain concise for busy readers.
Employers who initially laid off staff citing AI efficiencies are increasingly rehiring those same workers. Data shows 32% of hiring managers at US companies eliminated roles due to AI but later brought the position back. Examples include Ford re-employing engineers because automated systems failed with quality issues, and CBA reversing service bot cuts due to rising call volumes. Experts note that while AI handles routine tasks well, it cannot replicate human judgment or inconsistency handling. Consequently, many businesses are realizing they eliminated essential human oversight instead of just automating specific tasks.
Meta Platforms is developing plans to create a cloud infrastructure business selling excess AI computing power and its own AI models. This initiative would allow external developers to access Meta's hosted AI models or purchase raw computing capacity, mirroring services like Amazon Bedrock. The effort is reportedly being managed within an internal group called Meta Compute. Launching this venture positions Meta to potentially compete with major cloud providers such as AWS and Google Cloud. Monetizing this excess compute provides a new revenue stream from the massive capital investment made in AI infrastructure.
Roundtables features expert conversations on emerging technologies like AI and longevity. Past sessions have covered topics such as "reprogramming" the body to reverse aging and advancements in world models for artificial intelligence. Discussions have also analyzed legal issues, including an examination of the Musk v. Altman trial implications for the AI sector. The platform provides subscribers with recordings of these deep dives and alerts for upcoming events. These roundtables aim to provide real-time insights into major breakthroughs shaping the future.
California established a program compensating cattle farmers for converting methane from manure into natural gas. This incentive encourages the dairy industry to capture and utilize gaseous emissions rather than allowing them to pollute the atmosphere. The system aims to mitigate the environmental impact associated with livestock waste management. Critics suggest that the current calculations surrounding this "carbon manure math" are questionable. Ultimately, skepticism surrounds whether the state's climate policies effectively balance incentives with accurate environmental accounting.
B3 selected Android to power its secure, AI-enhanced productivity solutions. The visual elements emphasize physical devices running the Android operating system. Specific iconography and images feature multiple Android smartphones in use. This suggests a focus on device management within their platform's deployment. Overall, B3 is leveraging Android for its core offering of secure artificial intelligence tools.
Educators and industry leaders met at Google's offices for an AI summit. The gathering included representatives from the New York Jobs CEO Council and the Urban Assembly. One hundred fifty key figures attended the event. Discussions focused on shaping the future implementation of artificial intelligence within educational settings. These leaders aimed to guide how AI will be integrated into classrooms.
Enterprises are rapidly expanding their AI initiatives but lagging significantly in governance and visibility. Most organizations operate multiple competing AI platforms, lacking a single owner accountable across the entire technology stack. Confidence in detecting model failures is high, yet only ten percent have automated monitoring or alerting systems in place. The primary barrier to control is consensus ownership, with many companies failing to assign formal accountability for their burgeoning AI assets. This widening "control gap" exposes firms to risks like shadow AI and runaway agent costs.
A new open-source Command Line Interface (CLI) called ghealth has been released for the Google Health API. This tool functions as a single Go binary and exposes 40 data types in agent-ready JSON format. It is noted that ghealth is a community project and not an official release from Google. The content details how to use the CLI and advises users on what to check before granting OAuth access.
Beginning July 1, Anthropic is redeploying Claude Fable 5 following the lifting of US export controls. The update includes a new safety classifier designed to block techniques detailed in an Amazon report over 99% of the time. Flagged requests will now be rerouted for handling by Opus 4.8. Furthermore, Anthropic proposed a four-criteria jailbreak severity framework involving major tech companies including Microsoft and Google.
Artificial intelligence development risks worsening global inequality due to uneven worldwide adoption and investment. The United Nations issued a warning highlighting this potential negative impact of accelerating AI growth. To mitigate this risk, the UN proposes establishing a shared framework for responsible AI development practices. UN Secretary-General António Guterres urged governments to act immediately rather than delaying action on the rapidly advancing science. He stressed that without common rules governing AI's advancement, global governance and public input may diminish.
OpenAI is reportedly in early talks to grant the US government a 5% equity stake in the company. CEO Sam Altman supports this potential move as a method to share the benefits derived from artificial intelligence technology. These discussions are occurring amid an effort by AI companies to improve relations with Donald Trump's administration. Altman argued that providing the public with a financial stake is the most equitable way to distribute AI benefits. The reports indicate unnamed sources familiar with the talks provided this information.
AI companies are accused of perfecting the theft of intellectual property. This issue suggests that technological advancements in artificial intelligence facilitate the misappropriation of creative and proprietary works. The concern highlights a growing trend where AI tools can generate content or replicate styles derived from existing copyrighted material. Such practices raise serious legal questions regarding ownership and fair use in the digital age. Ultimately, these methods suggest a systemic challenge to established intellectual property rights posed by current AI development.
Reader responses concern environmental fears sparked by discussion on AI's resource demands, referencing Erin Brockovich's fight against data centers. Analysis identifies four leading uses of AI: therapy/companionship, technical assistance, entertainment, and creative writing. For companionship, AI use does not diminish loneliness and can impair real-world social skills through artificial affirmation. Furthermore, educators observe that students using AI show a decline in critical thinking abilities. The pieces contrast the necessity of clean water against the resource consumption associated with widespread AI adoption.
Cursor utilizes a team of Forward Deployed Engineers to guide businesses through implementing advanced AI solutions. These engineers assist organizations in deploying custom AI agents. Essentially, this process functions as establishing internal "software factories" for automation. Through expert deployment, Cursor integrates AI directly within the client's existing enterprise structure. This approach allows companies to operationalize sophisticated artificial intelligence capabilities.
Artificial intelligence is creating an urgent, system-level energy demand challenge that necessitates rethinking global infrastructure. Melbourne positions itself as a leader by integrating advanced renewable energy generation, grid modernization, and digital infrastructure capabilities. Key to addressing this convergence are engineers who can design systems that dynamically manage AI's power needs alongside decarbonization goals. The city achieves this through strong collaboration among government, academia (like the University of Melbourne), and industry partners. This holistic approach ensures future energy systems are designed co-dependently across energy and digital domains.
Elon Musk claimed orbital data centers would become the lowest-cost computing resource within two to three years, leading SpaceX to file for an orbital constellation of up to one million satellites. Experts argue such a massive deployment is technologically and logistically infeasible in that timeframe due to current launch and manufacturing capacities. Furthermore, significant engineering hurdles remain, including efficiently cooling powerful AI chips like the Nvidia H100 in space. Analysts suggest any cost parity with terrestrial centers might take five to ten years, noting immediate practical limitations for complex workloads. Ultimately, industry interest seems driven by escalating compute demand and expanding beyond Earth's grid capacity rather than current economic viability.
DeepSeek introduced Manifold-Constrained Hyper-Connections (mHC) after identifying architectural limitations in deep learning models. Traditional reliance on residual connections imposes stability but limits information transformation capacity at massive scales. mHC overcomes this by replacing simple addition with geometric constraints defined on manifolds. This breakthrough allows for the creation of deeper, stable neural networks without relying on the established safety net of residuals. Essentially, DeepSeek proposes a fundamental change in network physics to improve scaling potential beyond current architectural bottlenecks.
Traditional LLM memory approaches relied on increasing parameters or context windows, assuming intelligence scales by processing everything constantly. A recent concept introduced "Engram," a conditional memory module that allows the model to actively choose what information to access. This marks a shift toward selective intelligence, where the ability to choose relevant knowledge is more crucial than simply retrieving it. Currently, recalling information forces costly, dense computation across the entire model's parameters for every prompt. Implementing selectivity can therefore improve reasoning and reduce computational overhead by decoupling compute from memory requirements.
Anthropic launched Claude Sonnet 5, presenting it as a lower-cost model. This new version features enhanced agentic performance across several key areas. Specifically, its capabilities improve planning, tool usage, coding abilities, and knowledge work. Its performance was noted to approach that of Opus 4.8 while showing significant upgrades from Sonnet 4.6. Additionally, Anthropic also announced Claude Science, an AI workbench for scientific applications.
A free daily newsletter, TLDR AI, summarizes AI news, research, and tools in a quick 5-minute read. It is highly regarded among engineers, researchers, and technical professionals seeking dense, engineering-focused content. Each weekday issue covers model releases, research papers, and new tools concisely with source links. This deep focus distinguishes it from more general or applied "how-to" AI digests like The Rundown AI. With 1.1 million subscribers, it remains one of the largest AI newsletters available daily.
Large Language Models (LLMs) often exhibit a tendency toward predictable, narrow responses, suggesting a form of "groupthink." A startup is developing methods designed to break this constraining pattern in AI outputs. The provided example illustrates how repeatedly asking for random numbers from different chatbots yields patterns rather than true randomness. These limitations suggest that current LLM interactions can become overly conventional or constrained by learned biases. The goal is to develop novel techniques to elicit more unpredictable and creative responses from these models.
Announces a new set of AI features within the Google Pixel device line. Details unveiled focus on enhancing the user experience through advanced artificial intelligence capabilities. Specific updates suggest improved photography and more intelligent, context-aware assistance built into the operating system. These advancements are designed to make everyday smartphone interactions smoother and more capable. The release marks a significant push in mobile AI integration for consumer devices.
Accelerating AI demand is shifting from model development to large-scale, continuous inference operations. This transition necessitates access to multi-tenant accelerated computing infrastructure that can be rapidly deployed and maintain high utilization. Such capabilities are essential for supporting the economics of token-scale AI services. NVIDIA is addressing this compute gap by inviting partners to help build out necessary AI infrastructure. The focus is on providing robust, scalable resources for operational AI factories.
Reinforcement learning (RL) is fundamental for aligning language models. Techniques like Reinforcement Learning from Human Feedback (RLHF) are currently used in developing advanced AI assistants. RL principles extend beyond current methods toward incorporating more sophisticated alignment strategies. These agentic techniques aim to improve the complex decision-making capabilities of artificial intelligence agents. Ultimately, mastering these methods is key to advancing autonomous and reliable AI systems.
Google is expanding its artificial intelligence imaging capabilities with a new offering called Nano Banana Lite 2. This release coincides with increased accessibility to Google's Gemini Omni Flash model. The update signifies a broader rollout of advanced AI image tools from the company. Users can expect enhanced functionality across Google's creative platforms powered by this technology.
A tutorial demonstrates creating a complete PDF-to-structured-data workflow utilizing Lift. This process is designed for controlled evaluation rather than simple demonstrations. By setting up a Colab GPU environment and loading Lift in 4-bit NF4, synthetic research reports containing distractors were generated. The method involves running schema-guided extraction while scoring every field against ground truth data. Ultimately, the output forms a repeatable extraction benchmark, not merely raw model outputs.
Details the current state of Artificial Intelligence within space exploration initiatives. Explores key use cases for AI across various space missions and technologies. Outlines significant platforms and architectural approaches enabling advanced onboard computation. Discusses critical challenges hindering the widespread integration of AI in deep space applications. Provides a comprehensive overview covering necessary technical knowledge for industry stakeholders.
No significant news developments occurred on this particular day. The reporting indicates a continuation of low activity across various areas. Essentially, the day was characterized by a notable lack of major events or headlines. Nothing noteworthy transpired to warrant extensive coverage.
Autoresearch involves creating self-improving agent "recipes" within automated systems. This concept describes a feedback loop mechanism allowing agents to enhance their own capabilities. Roland Gavrilescu elaborated on these self-improvement protocols during an explanation of autoresearch. Despite autonomous learning, humans remain crucial as central components in the overall software factory process. These human contributions guide and refine the development of increasingly advanced AI agents.
Zach Lloyd, founder of Warp, predicts that nearly all significant software development projects will eventually operate as automated factories. He suggests this represents the next major evolutionary phase in coding practices. To prepare for this transition, engineers must adapt their skills accordingly. The core concept involves automating large portions of the software creation process. This shift necessitates a fundamental change in how engineering teams approach building and deploying code.
Presents six counterintuitive perspectives regarding question parsing within Retrieval-Augmented Generation (RAG) systems. These points challenge conventional best practices dominating the current RAG methodology. Authors advocate for prioritizing structural understanding before initiating any search process. The insights are detailed in a piece originally published on Towards Data Science.
Multi-agent pipelines often suffer from costly tokenization round-trips during agent hand-offs. Inductive Latent Context Persistence (ILCP) solves this issue for multi-hop LLM agents. ILCP functions by transferring a compressed hidden state between successive agents. This mechanism eliminates the need for downstream agents to re-establish existing context. The technique is compared to a seamless 6G handover, improving agent efficiency.
Provides guidance on building and deploying a custom AI agent within the AWS cloud environment. It specifically details the use of tools named Strands and AgentCore for this process. The platform hosting this information was Towards Data Science. Users can learn to create their own functional AI agents using these AWS-based services.
An undergraduate student is seeking project ideas for a Quantum Reinforcement Learning (QRL) course requirement. The individual has some experience with Quantum Machine Learning (QML). They are looking for suggestions to complete their assigned RL coursework using QRL concepts. No specific topics or domains were proposed by the user. The post is a request for guidance from others in the community.
The discussion concerns the appropriate methodology for evaluating rewards using multiple different random seeds. Specifically, it questions whether these evaluations should be grouped together and updated with a single accumulated gradient. Alternatively, the query explores the possibility of performing separate, individual gradient updates for each unique seed batch tested. This technical question pertains to best practices in gradient calculation during reward evaluation processes.
An agent was given the capability to "see," implying some form of visual input or awareness. Gradient descent was employed in the training process. This optimization method effectively taught the agents to disregard their sight. Instead, they focused solely on executing a sequence of mundane, repetitive actions. The overall implication is a shift from sensory processing to rote task performance.
A user is training Reinforcement Learning (RL) agents in a team pursuit task using only a capture reward and time penalty. After 5000 training iterations, the observed behavior of the agents is limited to minor movement and camping. The user questions if they need to introduce a more challenging training environment to encourage the development of other effective strategies.
The provided content is just a title and user submission details, containing no substantive article text to summarize. Therefore, summarizing factual information about AI agent memory systems beyond vector search is impossible with the given input.
Anthropic relaunched Fable 5 with visible safety fallbacks, prompting several tools like Cursor and Perplexity to integrate it. The industry focus shifted toward multi-model orchestration rather than dependence on a single frontier model for reliable performance. Several builders suggested combining models to delegate specific tasks like planning versus implementation. Additionally, Zai announced ZCode, an official development environment built around the GLM-5.2 model. Overall trends emphasize designing sophisticated, adaptive model-combination strategies across various coding stacks.
Diffusion research is showing promising advancements in drug discovery that extend beyond large language models (LLMs). Genesis Molecular AI's flagship model, PEARL, has achieved performance levels necessary for real-world applications in this field. The discussion contrasts LLM advancements with the novel architectural work being done using diffusion models. Sergey Edunov joined Genesis after leading Llama training at Meta, finding innovative possibilities in molecular AI research. These developments suggest a significant shift towards practical AI utility in small molecule drug discovery.
Anthropic introduced Claude Science, a new tool designed to assist with scientific research, comparable in scope to its Claude Code offering. This standalone product allows users to autonomously perform tasks from high-level instructions and integrates tools for computational biology and drug development. The technology is now available to all paid Claude subscribers, and Anthropic plans to use it for its own rare disease drug research. The launch positions Anthropic as a major competitor in the rapidly advancing field of AI for life sciences. Additionally, John Jumper has recently announced his departure from DeepMind to join Anthropic.
Agentic AI automates tasks that historically served as crucial apprenticeships for IT security professionals, creating a challenge in building future expertise. Organizations must intentionally redesign workflows to replace lost training grounds while adopting AI's efficiency gains. To govern increasingly autonomous systems, human judgment is needed to manage unforeseen behavior and guardrails. Empowering operators requires designing systems that expose reasoning, tier authority by confidence, treat overrides as learning signals, and capture cross-domain resolutions. The future demands designing systems where AI scales alongside, rather than replacing, human expertise.
AI-generated summaries of Tripadvisor hotel reviews have been found to minimize severe guest complaints. Specifically, one resort facing lawsuits over mass food poisonings was described as "spotless." Furthermore, an AI praised a location with reports of staff sexual harassment for its "friendly" service. These AI overviews tended to downplay serious issues ranging from mold odors to insufficient mains water supply. The findings were published by the consumer campaign organization, Which?.
Jamir Nazir's short story, *The Serpent in the Grove*, unexpectedly won the overall Commonwealth short story prize. Critics have widely accused the piece, particularly on social media platforms like X and Bluesky, of containing "obvious markers" indicative of AI generation. Despite these accusations, the judging chair praised it as "original, poetic and deeply moving." Following its viral attention and scrutiny, the literary magazine Granta withdrew from publishing the Commonwealth winners.
Research on diffusion models is exploring applications beyond large language models (LLMs). The discussion covers advancements in areas such as drug discovery, exemplified by a former Llama lead joining that field. Specific breakthroughs include PEARL achieving success with zero-shot OpenBind performance. Furthermore, the potential impact of co-folding techniques crossing necessary accuracy thresholds is highlighted. These developments indicate novel applications for generative AI methods outside of typical language tasks.
Powerful machine learning presents leakage problems that extend beyond mere time dependency. These issues are also spatial, structural, and related to coverage limitations. The discussion emphasizes the deceptive ease of powerful ML model development while highlighting these critical pitfalls. A subsequent piece titled "Why Powerful ML Is Deceptively Easy—Part 2" was first published on Towards Data Science.
Research by Bhamidipaty et al. (2026) demonstrates that imperfect world models are inherently exploitable. The authors investigate how flaws in an agent's internal model of the environment can be leveraged for manipulation. They show that rather than requiring a perfect understanding, vulnerabilities can be induced through predictable deviations or incomplete representations within these models. This implies that improving robustness necessitates addressing the underlying assumptions and inherent inaccuracies of any learned world representation. Findings highlight methods to probe and exploit specific weaknesses in model-based AI systems.
A researcher working at an aerial robotics startup is seeking collaborators for research on Reinforcement Learning (RL) world model algorithms. Interested individuals are encouraged to directly message the poster. Discussions regarding a specific problem statement can be held via direct message. The request was posted by user /u/Chemical\_Bonus4471.
Current AI alignment methods incorrectly treat human preferences as fixed targets for inference and optimization. Empirical evidence suggests that preferences are dynamic, layered, and actively constructed through interaction with technology. The paper introduces Constructive Alignment, viewing alignment as a control problem governing evolving human preference trajectories instead of achieving static satisfaction. This framework models preferences as state variables influenced by AI systems' actions and interaction design. Therefore, proper alignment involves regulating how AI shapes value formation to ensure it remains coherent, non-manipulable, and empowering over time.
Proposes Bounded Morality, a new formal framework analyzing the computational demands of moral problems for finite agents. It extends bounded rationality by defining moral situations using two dimensions: moral breadth (scope of relevance) and moral depth (inferential integration). These limited resources force an inherent tradeoff between breadth and depth, establishing a feasible space for moral computation. Ethical theories are thus viewed as locally efficient strategies suited to varying demand levels, rather than absolute truths. Consequently, aligning artificial systems morally requires scaling reasoning capacity allocation, not just mimicking human judgment.
Existing document-centric knowledge organization constrains how knowledge can be effectively structured, shared, and reused across systems. Formal approaches often neglect crucial aspects like human usability to enforce structure. MMM introduces a novel data model designed for interoperable knowledge documentation, addressing these limitations. It achieves this by combining limited normative constraints with the flexibility of free-text labels. Demonstrations prove MMM's implementability and early usability in diverse contexts.
Web scraper generation from natural language by LLMs is currently unreliable due to issues like dependency errors and varying page structures. A proposed solution introduces a constrained, verifiable agent framework that requires LLM output in typed JSON collector configurations instead of free-form code. This framework integrates a six-type collector taxonomy, strict constraints, static Airflow DAG execution, and rule-based quality checking. Testing on 138 tasks demonstrated the taxonomy supports requirement typing, while stable operation needs completing source, field, and execution constraints. For 80 verified tasks, this method achieved zero LLM tokens during execution and fast performance, providing a deterministic path for repeatable web data collection.
Developed a conflict-free path-planning algorithm for en-route Air Traffic Control addressing the gap between advanced algorithms and practical controller usability. The solution emphasizes interpretability via solution-space displays and incorporates standard operational constraints like separation standards. It integrates three methods—distance, time-interval, and zone-based—to identify safe paths efficiently within a solution space framework. Two variants, SSPPV and SSPPE, utilizing vertex-based and edge-based search nodes, were proposed for path planning. Empirical testing demonstrated that SSPPV paired with zone-based conflict detection achieved high performance, averaging 3.69 ms in simulated operational scenarios.
Rare disease differential diagnosis is difficult due to complex unstructured symptoms and vast search spaces, which limit current AI methods like RAG. The authors introduce RareDxR1, an end-to-end reasoning-centric large language model for open-domain rare disease diagnosis from clinical notes. RareDxR1 bypasses structured phenotypes by internalizing knowledge directly into its parameters through a progressive training framework. It incorporates Reflection-Enhanced Reasoning Sampling (RERS) to generate expert diagnostic trajectories without explicit human annotation. Experiments show that RareDxR1 achieves state-of-the-art accuracy, representing a major advancement in this area.
Investigates runtime human oversight of an AI agent facing two-sided informational asymmetry: the human knows her reward function privately, while the AI knows its proposed action quality privately. It builds on CIRL by introducing a contextual-bandit team game featuring a play/ask/trust/oversee interface. The bandit structure allows for exact one-shot characterizations, simplifying the analysis compared to full POMDPs. Two key one-shot rules—a team optimum and a myopic rule—are shown to have a gap representing avoidable harm. This gap occurs when the AI knows an action is harmful but a myopic human fails to intervene due to prior beliefs, suggesting this cost stems from non-credible oversight communication.
Epistemic thinking is crucial for students using generative AI in programming tasks requiring query construction and output validation. The paper proposes Epistemic AI Literacy (EAIL) as a process-based framework emerging from human-AI interaction. By applying the AIR framework, the study analyzed how epistemic aims and processes function during co-programming with GenAI. Analysis of dialogue data identified observable dimensions like mastery-oriented aims and processes such as explanation or verification seeking. Findings indicate a general deficit in EAIL, with most interactions showing non-mastery goals and reliance on less robust strategies compared to the small percentage exhibiting high epistemic engagement.
Investigating agent coordination in a Lewis signaling game, the study examined five distinct memory architectures with LLM agents. Results indicate that memory architecture is a more critical determinant of shared language emergence than channel capacity. Agents utilizing a persistent private notebook showed superior and stable coordination compared to stateless models, especially when surplus capacity was available. The notebooks externalize learned conventions, preventing constant re-derivation of communication codes round by round. Ultimately, effective signal-to-language conversion requires considering both memory structure and channel capabilities.
Introduces Seed2.0, a new model series designed to tackle complex, real-world problems. The methodology centers on defining user needs and building a forward-looking evaluation system using realistic benchmarks. Seed2.0 specifically addresses long-tail knowledge and intricate instruction following to boost reliability on lengthy tasks. It exhibits world-leading performance in reasoning, visual understanding, and search capabilities for broad utility. Demonstrations across numerous use cases show the model's capacity to handle initial complex real-world tasks effectively.
Mechanistic interpretability analyses yield component understandings that are difficult to reuse due to their varied, unstructured formats. This work identifies the representation layer between analysis and use as a bottleneck needing standardization. A new protocol called Manifestation Units defines a typed tuple structure $(E, S, R, D, G)$, extended with attention-head primitives $(T)$ for transformers. Implementing this protocol automatically structures component statistics into queryable fields, outperforming unstructured methods in retrieval performance across vision and language models. The resulting schema reveals a core functional dependency ($S+R$) critical for understanding network components like CNN filters.
Generative models often fail to respect the fundamental conservation laws governing physical systems. Existing methods for enforcing these physical constraints at inference time are computationally expensive, especially with complex nonlinear rules. Current deep learning frameworks hinder efficient batching of the required nonlinear optimization steps due to dense tensor algebra and poor sparse solver integration. This work introduces SNAP-FM, exploiting block-sparse structures within projection subproblems using ExaModels.jl for efficient solving on GPUs via MadNLP.jl. These results demonstrate that this sparse GPU approach significantly accelerates constrained generative sampling across various physics models, proving its viability in scientific machine learning.
SemiScope is an analysis instrument developed to disentangle the contributions of classifier tuning versus joint optimization in semi-supervised security classification using binary tabular data. It employs Bayesian Optimization to tune various SSL components and a downstream classifier jointly. Comparisons show SemiScope significantly outperforms default SSL baselines, with gains ranging from 0.7 to 12.7 points. Crucially, fixing the SSL pipeline and only tuning the classifier (Tuned-Clf) achieved results statistically equivalent to the full joint optimization on most datasets at the same budget. The study concludes that a simpler protocol—Self-Training combined with Bayesian Optimization for both the classifier and decision threshold—achieves near supervised performance using fewer labels than default methods.
Addresses data scarcity and privacy issues in survival analysis by introducing FoGS (Filtered Mixture-of-Generators). Instead of purely generating data, FoGS frames synthetic construction as sample selection from four distinct generators. Each candidate sample is assessed for plausibility using an ensemble of seven survival models trained on real data. A two-level pipeline optimizes a selection policy against downstream performance, while an inner loop tunes the resulting model. This method achieved mean improvements in C-index and IBS across 16 public datasets, proving its viability as a substitute for real-data training in restricted clinical settings.
Three common methods for refining language model reasoning—Group Relative Policy Optimization (GRPO), Dr. GRPO, and DAPO—are revealed to be fundamentally variants of one mechanism. These techniques all adjust the standard deviation, which quantifies the disagreement among multiple sampled answers on a given prompt when using right-or-wrong rewards. The paper introduces the group-standard-deviation identity, stating that this disagreement directly measures the magnitude of the necessary training update. This implies that models learning from conflicting examples gain the most insight, while those with unanimous answers learn nothing and effectively cease contributing to weight adjustments. Ultimately, this single standard deviation parameter controls where and how strongly learning occurs during model training.
Introduces EVOTS, an evolutionary neural architecture search framework designed for task-adaptive Transformer-like models in time-series forecasting. It uses a modular genome representation to allow flexible composition of components like attention and feed-forward layers. The method maintains structural validity via a repair mechanism, enabling broad architectural exploration without predefined rules. Evaluation across four ETT family datasets tested various prediction settings (univariate/multivariate). Results show that evolved architectures achieve competitive or improved mean squared error compared to strong Transformer baselines in multivariate-to-multivariate forecasting.
Develops Fractional-Fourier Mixture of Experts (FRAME), a novel adapter for parameter-efficient fine-tuning. FRAME allows an expert to learn a fractional-Fourier order, enabling interpolation between spatial (LoRA) and fixed Fourier domains. This mechanism lets the model place each low-rank update in its most compact domain, promoting decorrelation across experts due to mutual incoherence. The system trains this continuous order via a separate optimizer using efficient chirp-FFT surrogates. Empirical evaluations on various benchmarks show FRAME outperforms existing MoE-LoRA and spectral baselines while maintaining a small active parameter budget.
Training probabilistic forecasters with verifiable rewards can achieve calibration using proper scoring rules like the Brier score. The paper addresses aleatoric forecasting by proposing a novel, label-free reward based on an empirically estimated state-conditioned win rate to eliminate noise from single stochastic outcomes. This method avoids corrupting the reasoning process by keeping the gradient calculation separate from the prediction chain via direct prediction or masking. A 7B model trained solely with this reward achieved calibration matching the betting market when forecasting NFL in-game win probabilities. Importantly, masking the gradient preserves the underlying reasoning structure, unlike standard chain-of-thought training.
Thermodynamic computing devices based on the Ising model show promise for low-power edge AI inference, but scalable training methods are lacking. Researchers developed a purely backpropagation-based algorithm to train deep convolutional networks for thermodynamic inference directly on Ising machine hardware. This method achieved classification accuracies of 94.9% on CIFAR-10 and 76.0% on CIFAR-100 using binary Gibbs sampling. Furthermore, the work established a mathematical theory linking inference cost to accuracy while controlling autocorrelation times. The study concludes by presenting asymptotic results demonstrating a bounded tradeoff between performance and inference cost, alongside methods for optimal scheduling.
TallyTrain addresses the bandwidth limitations in federated learning concerning both model size and class count. To reduce communication cost for large vocabularies, it collapses the class-count axis by transmitting only each peer's $\arg\max$ class index instead of soft labels. This hard-label consensus method can outperform standard soft-label distillation under non-IID data because majority voting effectively filters noise from undertrained peers. TallyTrain matches or surpasses soft-label distillation while using up to three orders of magnitude less communication across benchmarks. Furthermore, it introduces a bandwidth-bridge variant combining hard-label consensus with sparse parameter merges, improving upon established federated learning baselines like FedAvg and FedProx.
Establishes approximation and learning guarantees for Fourier Neural Operators (FNOs) applied to time-$T$ solution operators of dissipative evolution equations. The analysis requires that the solution operators admit stable and accurate spectral discretizations. New classes of evolution operators defined via spectral methods are introduced, leading to derived FNO approximation bounds and polynomial sample complexity guarantees. For polynomial nonlinearities, learning rates depend mainly on input space smoothness and physical domain dimension across broad families of equations, including Navier-Stokes and Cahn-Hilliard. Furthermore, polynomial sample complexity is shown to hold for non-polynomial smooth nonlinearities, with rates also depending on the nonlinearity's smoothness and dissipation strength.
Investigates a forecasting framework utilizing a discrete-time dynamic model with time-varying coefficients. Parameters within this model are recovered using a deep learning structure, maintaining parametric transparency despite complex, nonstationary patterns. The analysis considers both standard Gaussian and Laplace-distributed noise processes for robustness against heavy tails. A predictive scheme is formulated for two noise specifications, accompanied by uncertainty quantification methods like prediction intervals. These findings demonstrate that time-dependent parameter estimation allows a simple model to effectively forecast complex dynamics under varied noise assumptions, with specific development shown for the TVAR(1) case.
Traditional variational Kalman filtering struggles with inconsistent process covariance estimates and slow convergence due to unknown noise statistics. To resolve this, the authors introduce a surrogate variable for the process-noise-free state, allowing explicit modeling of process noise statistics. They reformulate coordinate ascent variation inference (CAVI) into a marginalized maximum a posteriori problem solvable by single-step hyperparameter fitting, eliminating multiple inner iterations. This approach decouples covariance tracking filter design and supports higher-order filters or sliding-window estimation. Simulations confirm that the proposed architecture significantly enhances convergence speed and improves overall estimation accuracy against existing methods.
Existing multitask learning methods struggle when tasks have different outcome types because their loss functions are not directly comparable, hindering unified objective formulation. Proposes a multitask transformation framework to account for unknown monotone transformations between task-specific responses. Incorporates shared sparsity, addressing high-dimensional settings where few predictors are informative across multiple tasks. Estimates target functions and identifies key predictors by optimizing a smoothed rank-based criterion using a group-Lasso penalty within a deep neural network structure. The method achieves competitive performance in simulations involving diverse mixed outcomes, identifying biologically relevant shared predictors.
Recognizing that real-world data often possess underlying low-dimensional structure despite high dimensionality, this work proposes a mathematical framework for binary classification tailored to such settings. The research builds upon Cover's 1965 function-counting theory but refines its general position assumption to specifically account for data's inherent low dimensionality. By doing so, it derives dichotomy counts that accurately reflect the structure of the underlying data manifold. Furthermore, the framework extends Cover's analysis concerning separation capacity and generalization bounds to this low-dimensional context. This allows researchers to quantitatively analyze how the data's structure influences model classification performance.
Investigates graphical models whose independencies relate to vertex separation in mixed graphs containing directed, undirected, and bidirected edges. Focuses on "separable graphs," where a missing edge necessitates a separating set for its endpoints, and related "essentially separable graphs." Presents several characterizations for these graph classes, including one based on general graph properties and another relying solely on graph separation. Offers multiple ways to characterize the separation equivalence for separable graphs. Finally, provides a canonical representation and an algorithm for identifying the equivalence class of any essentially separable graph under certain conditions.
A study analyzed data from 665 census tracts across ten U.S. metros to examine if corporate landlord concentration (CLC) influences rent growth between 2019 and 2023. Findings show that doubling REIT concentration correlates with a 2.8 percentage point increase in rent growth overall, controlling for pre-existing housing burden. This association is significantly stronger within majority-minority tracts, which face an estimated 5.9 percentage point higher rent growth compared to comparable white tracts. The research reports the first tract-level evidence linking corporate landlord concentration to disproportionately high rent increases in communities of color.
Researchers developed uniform-in-time propagation-of-chaos estimates for continuous-time Stein Variational Gradient Descent (SVGD), addressing the failure of classical estimates over long times. For broad metrics like Langevin kernel Stein discrepancy and Wasserstein distances, a cutoff strategy provides bounds with logarithmic rates. Additionally, they established a finite-dimensional theory for matrix-valued kernels, achieving genuine uniform parametric propagation-of-chaos rates in first and second moment features for Gaussian targets. A conjugacy principle extends these feature-level estimates to complex nonlinear target-kernel pairs under diffeomorphisms. These findings contrast general distributional metrics (logarithmic rates) with closed finite-dimensional observables ($N^{-1/2}$ rates).
A framework analyzes $\ell_2$-adversarial training for single-index models on Gaussian mixtures in high dimensions using streaming stochastic gradient descent (SGD). Deterministic equivalents are derived for statistics like adversarial risk and distance to optimality, expressed as ODE solutions. For $\ell_2$-adversarial least squares with one class, constant learning rates fail to guarantee monotone descent toward the adversarial risk minimizer. Anisotropic covariance and ridge parameter mismatches cause exact line search suboptimality compared to the Polyak stepsize. Finally, an "adversarial homogenized SGD" SDE is introduced, showing its convergence leads to a limiting adversarial risk determined by a fixed-point equation equivalent to a ridge regression solution.