AI domain quality extends beyond mere verifiability, suggesting a higher-dimensional set of properties determines success for modern training pipelines. Domains like math and code excel because they score highly across multiple axes, unlike fields such as open-ended knowledge work. The author proposes an evaluation based on several distinct axes that must be considered together to accurately predict AI capability compoundness. These dimensions offer a framework for understanding why certain applications thrive while others struggle despite having strong individual properties.
SpaceXAI launched Grok 4.5, described as their first coding-and-agents-focused frontier model trained with Cursor. The model is positioned as being Opus-class but significantly faster and more cost-efficient than prior models. Elon Musk highlighted its utility for engineers at Tesla and SpaceX over chasing absolute benchmark scores. xAI announced Grok 4.5 specifically for coding tasks, moving beyond previous limitations seen in benchmarks like SWE-Bench Pro. Cursor confirmed the partnership and indicated immediate in-product availability with enhanced usage allowances.
The recent news covered a collapse of the Iran-U.S. ceasefire, causing oil prices to spike and the IMF to warn of a global slowdown. During a NATO Summit, Trump criticized European allies but announced U.S. permission for Ukraine to manufacture Patriot air defense systems. Bernie Sanders urged Maine Senator Graham Platner to resign over sexual assault allegations, amidst intensifying Democratic pressure in state races. Furthermore, the Justice Department issued letters threatening prosecution to states that do not purge non-citizens from voter rolls.
Prof G Media has launched a new live video titled "Raging Perspective." This program features Jessica Tarlov and Aaron Parnas as the speakers. Viewers can access the live video stream via Substack. The content was published in an email from Prof G Media's official channels.
OpenAI is launching GPT-5.6 Sol, Terra, and Luna tomorrow, with Sol as the flagship model. Anthropic has rolled out beta access for Claude Cowork across both web and mobile platforms. MiniMax released M3, an open-weights model featuring a 1M-token context window capable of combining coding and agent functionalities. Microsoft is beginning to replace third-party AI models in products like Excel and Outlook with its own developing models. Separately, China's DeepSeek plans to enter the silicon market to design its own data center chips.
Worms and microbes are gaining attention for their potential to solve manure pollution issues in the livestock industry. A process called "vermifiltration" utilizes these organisms to clean manure wastewater. This natural filtration system could significantly reduce harmful pollutants like methane, nitrous oxide, and water contamination. Scientists and farmers are implementing this method as part of broader sustainability efforts concerning agricultural waste.
Scientists are exploring giant, uncrewed aircraft capable of operating in the stratosphere to artificially cool Earth. These planes would release materials designed to reflect sunlight back into space, mimicking natural cooling effects from massive volcanic eruptions. Such solar geoengineering aims to counteract global warming by intercepting incoming solar radiation at high altitudes. Significant engineering hurdles remain, including designing suitable high-altitude aircraft and mastering the precise chemistry for aerosol dispersal. Because of the unknown consequences, research is moving beyond simulations toward detailed design and practical implementation studies.
AI-native enterprises structure their entire operations—including workflows, data, and rules—to allow machines to participate reliably in work processes. Current enterprise adoption is characterized by a transition phase focused on making these operations legible to AI systems. Maximizing ROI requires redesigning actual business workflows rather than simply adding chatbots or agents atop existing processes. True value comes from encoding expert knowledge and business rules into compoundable systems that blend human judgment with AI capabilities. This comprehensive guidance series provides roadmaps for navigating this transformation period in enterprise AI adoption.
OpenAI outlines its methodology for engaging in government and national security collaborations. Central to this approach are core principles ensuring the responsible utilization of artificial intelligence. These guidelines emphasize maintaining strong democratic accountability throughout development and deployment. Furthermore, OpenAI prioritizes the overarching goal of public safety when partnering with governmental bodies. The framework aims to integrate advanced AI capabilities within established ethical and oversight structures.
Several artificial intelligence vendors are entering the legal industry. These companies aim to automate various fundamental legal functions. The presence of these AI tools signals a significant technological shift within the legal field. One particular legal AI company has reached an impressive valuation of $1.2 billion. This influx highlights substantial investment and disruptive potential in legal technology.
Political campaigns are increasingly using AI tools and deepfakes for messaging, raising expert concerns about the potential scale of misinformation. One example involved a candidate who used an AI chatbot to generate fake news hits and endorsements on his iPhone. This candidate then shared a fabricated story, complete with a CNN logo, claiming his opponent dropped out of the race. In reality, his opponent continued her campaign and won by a landslide that November. The situation highlights the dual potential of AI: equalization or enabling the spread of lies.
Wyoming officials reacted after a contractor for Meta flushed bacteria-contaminated water into public sewers while building an AI datacenter. This incident prompted local water authorities in Cheyenne to enact stricter safety regulations. New rules dictate how wastewater from such construction projects must be disposed of. Meta stated it is cooperating with officials to ensure minimal impact on the surrounding area. Ultimately, drinking water supplies were confirmed not to have been affected by the contamination event.
Before implementing more AI agents, organizations should focus on foundational improvements. This involves mapping existing AI value streams within the business. Furthermore, companies must redesign current workflows to effectively integrate new technology. It is also crucial to redefine talent roles and consider upgrading the executive team's capabilities. Finally, establishing metrics to measure overall business impact is essential for success.
OpenAI plans to release GPT-5.6 Sol, Terra, and Luna models tomorrow, expanding global preview access. Sol is designated as the flagship model offering enhanced capabilities. Terra serves as a balanced option for routine work, performing comparably to GPT-5.5 at half the cost. Luna provides strong functionality while maintaining a low operating cost. These releases mark new additions to OpenAI's model offerings.
A free daily newsletter called TLDR AI summarizes key AI news, research, and tools in a brief, five-minute read. With 1.1 million subscribers, it is noted as one of the largest global AI newsletters. The content is dense and engineering-focused, summarizing model releases and research papers with source links. It targets technical professionals such as engineers and researchers who need to track advancements without unnecessary hype. This distinguishes it from other newsletters like The Rundown AI, which focuses more on general, practical application tips.
AI progress in 2026 emphasizes that AI is becoming more selective, modular, and infrastructure-aware across various advancements. Architectural rethinking includes DeepSeek's mHC, which uses geometric constraints like doubly stochastic matrices to improve information flow stability and expressivity in deep networks. Furthermore, models are adopting Conditional Memory to allow them to activate only the necessary knowledge for a given task, moving beyond retaining all information solely in parameters. The overarching theme connects new research techniques with practical workflow mechanisms, spanning tokens, vector databases, and inference orchestration. Ultimately, AI agents are evolving into durable systems capable of memory, tool use, local control, and self-improvement loops.
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OpenAI conducted a new analysis identifying problems within the SWE-Bench Pro coding benchmark. These findings suggest potential issues regarding the benchmark's overall reliability. The evaluation raises significant concerns about its accuracy when assessing artificial intelligence model performance. Consequently, researchers should be cautious when relying on SWE-Bench Pro for model comparisons.
Flint is an open-source visualization language designed to improve chart creation beyond simple, uninspiring specifications. It allows Artificial Intelligence (AI) agents to generate more expressive charts. Users can guide this process by providing compact and human-editable specifications. This approach positions Flint as a useful middle ground in the evolving era of AI-driven visualizations.
NVIDIA introduced Nemotron-Labs-3-Puzzle-75B-A9B, a compressed hybrid Mixture of Experts (MoE) Large Language Model derived from Nemotron-3-Super. This compression method combines hardware-aware structural reduction with knowledge distillation recovery phases. The model parameters are reduced from 120.7B total to 75.3B total and 12.8B active to 9.3B active. Running on a single 8xB200 node, it achieves 2.03 times the throughput of the original version at matched user performance. Furthermore, concurrency on an H100 increased from one request to eight for 1 million tokens.
Google AI Studio is introducing the feature of importing code directly from GitHub within its Build mode. This functionality converts an existing repository into a format suitable for runtime compatibility. Users can then actively iterate on this imported code base. Furthermore, they gain the ability to deploy the application built upon the GitHub source. This enhancement allows developers to build and deploy applications using their existing Git repositories within AI Studio.
Datalab's Lift is a specialized document extraction tool designed to take a PDF or image alongside a JSON Schema for direct, schema-shaped JSON output. Uniquely, it reads rendered page images directly rather than converting the document to Markdown beforehand. The publication compares Lift against several other established tools: NuExtract3, LlamaExtract, Marker, and Docling. This comparison assesses the performance of Lift, particularly its 9B "Schema-First" approach, in real-world extraction scenarios.
Netflix engineers addressed wide partition read latency in Apache Cassandra for their TimeSeries Abstraction using a two-pronged approach. Time Slice re-partitioning optimizes future table partitions, while dynamic partitioning actively splits oversized partitions per TimeSeries ID during reads. Detection of large partitions utilizes byte counting streamed via Kafka, and the resulting splits are checksum-validated. Bloom filters efficiently direct reads to these new parallel child partitions. This implementation successfully lowered average read latency from seconds to low double-digit milliseconds while maintaining access to massive partitions exceeding 500MB.
OpenAI introduced GPT-Live and GPT-Live-1 mini, representing a new generation of voice models for ChatGPT Voice. These models feature a full-duplex architecture, allowing them to listen and speak simultaneously. A key advancement is their delegation of deeper reasoning tasks to the more advanced GPT-5.5 model. This integration enhances the conversational capabilities of OpenAI's voice offerings.
Analyzes the components defining optimal environments for Artificial Intelligence development. Examines criteria beyond simple verifiability to assess domain suitability. Suggests that an ideal environment incorporates several key structural and functional attributes. These attributes relate to data availability, process transparency, and established rulesets within a specific domain. Ultimately seeks to provide a comprehensive model for determining AI feasibility across various fields.
Grok 4.5 has been launched by SpaceXAI. This model is significant as it represents the first "Opus-class" offering following the acquisition of Cursor. The launch signals continued rapid advancement from SpaceXAI. Industry observers note that SpaceXAI operates at a faster pace than any other frontier AI laboratory globally.
Explores how "Agent Experience" is becoming functional following recent developments in the field. Discusses learnings gained from constructing a novel "agent cloud." The conversation revisits insights on making AI agents highly functional for end-users. It details the necessary evolution of underlying AI infrastructure to support this advanced agent capability. This exploration comes two years after initial coverage on the topic.
The author rewrote the Bun JavaScript runtime from Zig to Rust due to memory management bugs common in the original implementation. This rewrite leveraged a comprehensive TypeScript test suite as a conformance standard for automated porting. Sophisticated "agentic engineering," utilizing LLMs like Claude, was employed to automate and review the massive codebase migration. The process required monitoring workflows and using adversarial code review rather than manually fixing individual lines of code. The resulting Rust implementation of Bun has been integrated into Anthropic's Claude Code.
Limitations in current AI models are not primarily due to the speed of Graphics Processing Units (GPUs). Instead, the core challenge resides within the fundamental architecture and mathematical understanding required for advanced model development. Significant advancements require developing new algorithmic approaches beyond simply scaling up existing hardware capabilities. Researchers must focus on improving computational efficiency and creating novel methods for training complex neural networks. Overcoming these structural hurdles is necessary to unlock the next major leaps in AI capability.
Determining when an AI agent should operate autonomously is best achieved using cost asymmetry rather than relying on a static confidence threshold. This approach reevaluates decision-making based on the potential costs of incorrect actions. Instead of setting a fixed percentage cutoff for certainty, one must weigh the expected loss associated with acting too early versus the cost of inaction. By modeling these differing costs, agents can make more economically sound decisions about when to intervene independently.
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An AI research club is organizing a summer reading group focused on Reinforcement Learning (RL) papers. The group targets upper-level undergraduates through master's students, but welcomes all backgrounds. Sessions will begin with entropy-based online methods and no-regret learning before covering sequential decision making and RL for LLMs. The curriculum will conclude by exploring RL applications for agents. Interested individuals can comment, submit availability via the provided link, or join the mailing list.
Describes a strategy for improving the efficiency of debugging and implementing reward shaping within Reinforcement Learning. The approach aims to provide structured methods for tackling these complex areas of RL development. Specific techniques are detailed to enhance both diagnostic processes and the design of reward functions. By optimizing these elements, practitioners can accelerate model training and performance tuning. This provides a comprehensive framework for advanced RL research and application.
Introduces tlabel, an open-source Python toolkit unifying tactile sensor data from various sources like GelSight and DIGIT. To address inconsistent labeling, it utilizes a configurable taxonomy system built upon T-Rex's 22 motor primitives as a default baseline. The system can automatically predict foundational primitives (e.g., grasp, press) even from visual-tactile images without dedicated force sensors. Users can extend the system by registering custom physical rules or scoping annotations to local taxonomies while retaining manual annotation control. Predictions are provided with source tags and confidence scores, positioning the tool as an assistant rather than a definitive annotator.
Content details are unavailable as the provided text only contains a submission header. It indicates an entry titled "Mountain Car IRL" was submitted by a user named /u/Greg_war via a link. The structure suggests it is from a forum or community platform. No actual content body or discussion points were included for summarization.
Good domains for AI must score highly on multiple properties, not just verifiability. While verifiability is often cited as key, it is only one axis among several necessary factors. Domains where current progress has been disappointing, such as robotics or general knowledge work, are typically weak on other crucial axes. The author suggests that the most successful AI domains score highly across all these required dimensions simultaneously. Verifiability itself is the first property to be analyzed in detail within the post.
MIT Technology Review Narrated provides weekly audio summaries of ambitious journalism covering major technological topics. Recent episodes have explored geoengineering efforts to cool the planet and advancements in understanding dark matter composition. Other featured subjects include global mobilization concerning dangerous asteroids and decoding internal body-brain signals. The podcast also examines trends such as the impact of AI on labor markets, advancements in IVF, and smart glasses for warfare. Content features analysis backed by MIT Technology Review's trusted reporting across various scientific fields.
Worms and microbes are gaining traction as potential solutions for manure pollution in agriculture. The text references the experience of a third-generation California dairy farmer demonstrating their use. It suggests that biological methods are emerging to address agricultural waste issues. Furthermore, the newsletter signals coverage on the current status and realities facing geoengineering techniques.
Focuses on collaborative efforts to advance worldwide health initiatives. Emphasizes the necessity of working together across various sectors. Suggests that shared commitment is key to building a healthier global future. The visual element comprises diverse pairings illustrating human interaction. Overall, it advocates for unity in tackling global health challenges.
A novel AI imaging model from Meta is available for use by creators and businesses. This technology allows users to generate visually appealing content directly within Meta's social media ecosystems. Such capabilities can help small and medium-sized businesses (SMBs) produce competitive advertising material. Utilizing this tool streamlines the creation of high-quality ads for platforms like Facebook and Instagram. Essentially, it provides a direct creative solution tailored for SMB marketing efforts on Meta's platforms.
Cowork is expanding its availability to both mobile devices and the wider web. This development demonstrates the increasing integration of artificial intelligence into professional workflows. Users can now access Cowork's functionalities on various platforms. The evolution signals a significant shift in workplace technology. Essentially, AI tools are becoming more accessible across all personal and professional computing environments.
The proliferation of datacentres poses significant environmental risks by consuming large amounts of energy and water while generating substantial heat. These issues intersect critically with two major global concerns: the climate crisis and artificial intelligence (AI). The rapid expansion of these facilities is fueling a complex public debate regarding their overall benefit versus cost. A central question revolves around whether AI's promised benefits can genuinely outweigh the mounting environmental strain caused by datacentre infrastructure.
It cautions against reading its contents due to sensitive information regarding the AI industry. The piece implies there is a significant and undisclosed negative aspect of the field. It advises readers that they are better off remaining ignorant of this secret. No specific details about the "dark secret" are provided in the available snippet. Therefore, the main message is one of caution and non-disclosure to the reader.
OpenAI upgraded the model powering ChatGPT's voice mode to a new version called GPT-Live. This updated system can handle complex tasks by delegating them to a more advanced underlying frontier model, such as GPT-5.5. It maintains conversational flow even while processing intensive requests like web searches or deep reasoning. Previously, the voice mode relied on an older GPT-4o era model with limited capabilities. Early testing revealed an issue where the model would inappropriately interrupt users with laughter, which OpenAI has reportedly addressed.
Kenton Varda has declared a moratorium against accepting AI-generated change descriptions for team tasks like PRs and commit messages. He found that the AI's output was unhelpful during his review process. Specifically, the AI tended to describe granular code details already visible in the code itself. Conversely, it often failed to provide the necessary high-level framing required for a broad understanding of the changes.
AI personalities are not intentionally engineered or designed by their creators. Perceiving a distinct personality in artificial intelligence is an inherent aspect of how humans interact with them. Because this perception exists, it presents itself as a difficult engineering challenge. Current efforts to address AI personality remain largely unresolved within the field.
Explores how small sample sizes can generate seemingly strong correlations purely by chance. It addresses the common misconception that larger data sets automatically yield more meaningful relationships. The discussion cautions readers against over-relying on correlation simply because it appears in a large dataset. Ultimately, it warns that statistical significance does not always equate to real-world meaning or causality.
AI agents in 2026 are evolving into durable systems possessing memory, skills, and the ability for physical action. The shift is marked by platforms like OpenClaw, which establishes local agents with file-backed identity, scheduled reasoning, and memory within structured markdown files. Beyond chatbots, advanced models such as VLA models are enabling physical capabilities, complemented by Web World Models. Infrastructure is crucial, forming a personal control plane that integrates agents with various messengers. Furthermore, the development incorporates responsible AI frameworks to ensure safe and reliable functionality for these new systems.
Nvidia and Hugging Face are collaborating on the development of open-source robot models. This partnership aims to enhance the accessibility and practical deployment of physical artificial intelligence systems. The initiative is expected to bolster Nvidia's established leadership within the AI robotics sector. By making these models open source, they facilitate broader industry adoption and research. Ultimately, this collaboration pushes the boundaries for real-world AI implementation.
Robbyant unveiled LingBot-VLA 2.0, an open-source 6B vision-language-action model designed for cross-embodiment robot manipulation under an Apache-2.0 license. This checkpoint was pretrained using approximately 60,000 hours of data, encompassing 50,000 hours of diverse robot trajectories and 10,000 hours of human egocentric video. The model unifies multiple embodiments into a single 55-dimensional canonical action space covering various body parts. It employs a token-level Mixture-of-Experts expert for scalable capacity, while dual-query distillation adds geometric and temporal supervision. On the GM-100 generalist benchmark, it surpasses previous models like $\pi0.5$ and LingBot-VLA-1.0 on both tested platforms.
VersatIL is a modular PyTorch framework designed for imitation learning in surgical robotics to combat inconsistent research implementations. It decouples key components—data, network architecture, algorithm, and objective—allowing users to swap parts independently without rewriting the entire codebase. The framework includes a unified data pipeline accepting formats like HDF5 and LeRobot, and supports swappable encoders for various observations (RGB, depth, etc.). Unlike existing tools that treat policies as monoliths, VersatIL reimplements policies on a shared low-level skeleton, facilitating easy experimentation with state-of-the-art variations. Features also include vision explainability methods and quantization support.
A final-year PhD student specializing in RL-based robotic control is expressing significant professional distress. Initially, the topic seemed manageable, but the lack of prior experience proved challenging. The student has faced setbacks, including two paper rejections due to methodological flaws and an inability to handle out-of-distribution states with their model. Despite years of effort, they feel far from the current state-of-the-art in the field. Currently, the student is unsure how to salvage their research progress.
LingBot-Video serves as an open model that predicts robot manipulation rollouts. This model operates in pixel space, generating a fully synthetic video output based on action and hand-pose signals. A key question raised is the practical utility of using pixel-space prediction for model-based Reinforcement Learning (RL) and policy evaluation. Concerns center on computational cost and potential state drift inherent to pixel-space approaches. Consequently, the text questions whether latent-space predictions, similar to Dreamer architectures, represent a more feasible alternative for such tasks.
Presents an eight-part technical breakdown detailing the complete robot learning pipeline. The guide covers every stage, starting from data collection through to real-world deployment. Specific topics include teleoperation hardware and utilizing generative models for action generation. It also examines Vision Language Actions (VLAs), world models, and Reinforcement Learning fine-tuning techniques. All components are rigorously grounded in established academic research papers.
AgentLens is introduced as a new benchmark for evaluating interactive code agents by assessing their entire operational trajectory. Unlike existing benchmarks that only provide a pass/fail outcome, AgentLens analyzes the full process, including tool usage, self-verification, and error recovery. It achieves this by combining formal verification with detailed reviews written by Large Language Models (LLMs). The resulting evaluation provides a readable explanation detailing the rationale behind each performance score. This makes AgentLens valuable not just for model ranking, but also for diagnosing agent behavior and detecting regressions in product pipelines.
In-context search allows large language models (LLMs) to iteratively generate, critique, and revise solutions, which is modeled as approximate inference over reasoning traces. The method utilizes a base model's prior combined with self-reflection for posterior updates. Theoretically, when reflections successfully localize early errors, in-context search can provide exponential performance gains using only polynomial sequential attempts. Conversely, if this localization property fails, conditioning on past attempts offers no asymptotic advantage over parallel sampling. These theoretical gains are shown to be robust and learnable through simple cross-entropy training on the search rollouts.
Agent-based modeling (ABM) is valuable for simulating millions of interacting individuals for policy decisions but has been limited by relying on static prior assumptions. To overcome this deficiency, the research introduces a novel method utilizing Large Language Models (LLMs). This results in a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework. HALE specifically leverages LLMs to predict complex human decision-making within an ABM simulation structure. A proof-of-concept successfully applied this framework to simulate COVID-19 dynamics in Salt Lake County, UT.
QANTIS proposes treating quantum processors as a calibrated service to estimate rare-event evidence for sequential POMDP belief updates. It assesses whether this service can reliably function across a Tiger POMDP horizon on IBM Heron hardware without corrupting classical planner inputs. A controlled case study compared different amplification techniques—including all-step fixed-point amplification (FPAA)—across 8, 12, 20, and 32 steps. The findings showed that all-step FPAA maintained the Tiger posterior across tested trajectories. Critically, the hardware-derived posteriors matched the exact Bayes posteriors in determining the immediate action taken by the planner.
Introduces an agentic harness approach for ARC-AGI-1 that relies on architecture rather than specialized training or high compute costs. The system features an Explorer-Definer Pipeline to separate pattern discovery from program synthesis, augmented by a Reflective Orchestrator for autonomous hypothesis testing. On the 400-task evaluation set, this method achieved 67.25% pass@2 with the orchestrator at \$0.62 per task. This represents a significant lift over a baseline without needing benchmark-specific fine-tuning or extensive test-time computation. Analysis suggests that improvements depend on broadening generated hypotheses rather than refining selection capabilities.
A ReAct-style agent setup was proposed, integrating LLM reasoning with verifiable feedback from SageMath and Context7 to aid mathematical problem-solving. Evaluating this on the RealMath benchmark emulating research loops showed substantial performance improvements. Accessing SageMath yielded an average gain of +9.7 percentage points across all tested models. Qwen~3.7-Max benefited most significantly, while GPT-5.5 achieved a 75.2% solve rate with minimal token usage among tool-enabled configurations. These results indicate that CAS-augmented agents are a promising avenue for computational mathematical exploration and potential automated conjecture discovery.
Development of agentic AI currently suffers from "token maxing," where increasing token usage outpaces task value. The proposed solution to this inefficiency is the orchestration layer, or "harness," which manages context assembly, tool exposure, and work sequencing. Evaluating 22 tasks across six models showed that implementing a structured harness reduced blended cost per task by 41% while maintaining comparable quality scores. This efficiency improvement was model-invariant, leading to higher quality per dollar spent overall. The study concludes that mastering the orchestration layer is critical for controlling token economics regardless of which underlying foundation model is used.
Introduces the Large Behavioral Model (LBM), a novel system designed to model retail customer decision-making. LBM learns directly from large-scale transactions using a unified Person-Environment framework, incorporating historical purchases as behavioral profiles and product context via retrieval-augmented generation. Training involves continued pre-training on verbalized data, supervised fine-tuning for decisions, and reinforcement learning for evidence-based calibration. Evaluation shows the model surpasses general language models on various in-domain tasks like purchase prediction and promotion response. These results validate encoding transaction history into language models, creating a scalable foundation for customer digital twins.
Human-AI coordination often fails because current AI methods do not explicitly capture underlying social norms guiding natural interaction. Researchers hypothesized this gap exists due to failing to quantify the tacit expectations present in social settings. Using pedestrian-vehicle interactions, they identified three core principles of human social norms: outcome predictability, value alignment, and advantage awareness. Integrating these formalized principles into LLMs significantly improved AI coordination performance. Specifically, a social-norm-informed LLM achieved substantially higher scores in closed-loop tasks compared to baseline models.
TriRoute introduces a novel lightweight controller to jointly manage three coupled computational decisions in large language models: attention resolution, expert selection (MoE), and KV-cache bit-width. For each token at every layer, this single controller outputs a coordinated policy covering the attention mode, sparse FFN experts, and required cache precision. It trains end-to-end under a Lagrangian budget constraint to make compute and memory costs controllable knobs. Evaluation shows TriRoute Pareto-dominates state-of-the-art independent combinations across various model sizes at matched inference cost. Analysis indicates the controller intelligently allocates high resources to rare or structurally important tokens like named entities, while conserving them for common words.
Standard marginal conformal prediction fails significantly on imbalanced datasets in drug discovery, failing to cover the minority class adequately despite achieving overall coverage targets. This deficiency is reproducible across various models and relates to calibration issues specific to rare labels rather than model architecture. The shortcoming implies that the undercoverage of the minority class equals the overcoverage of the majority class times the imbalance ratio. Implementing class-conditional (Mondrian) conformal prediction successfully restores minority class coverage back to target levels. This work provides a crucial protocol demonstrating how imbalance can create a severe, yet easily missed, failure in reliability guarantees for chemical screening.
Long-term forecasting in complex systems is often hampered by dataset-level distribution shifts reflecting diverse underlying behavioral modes. Current methods largely neglect the global challenge of datasets being composed of distinct operational regimes. This paper introduces NEST, a framework that models and recomposes these evolving structures using a two-phase dense Mixture-of-Experts (MoE) architecture. NEST first clusters the data into discrete operational regimes within a moment-entropy space. Subsequently, it utilizes a regime-oriented router and specialized experts to capture unique, regime-specific dynamics through varied attention patterns, achieving state-of-the-art results across benchmarks.
D2PO introduces a novel framework to optimize diffusion sampling policies by utilizing a preference-based alignment approach instead of traditional student-teacher regression. It reformulates sampler optimization using the Direct Preference Optimization (DPO) framework, modeling the policy as an energy-based model. A new energy formulation derived from the score network allows for preference evaluation in perturbed spaces capturing both structure and fine details. The method incorporates dynamic preferences, enabling an iterative refinement process that self-improves alignment during learning. Experiments confirm D2PO more faithfully aligns samplers with perceptual quality, surpassing conventional schedulers under low-NFE conditions.
Addresses limitations of static portfolio optimization by introducing a deep reinforcement learning (DRL) framework for multi-objective reliability based optimization. The proposed model jointly optimizes expected return and downside risk using variance, CVaR, and EVaR. Uncertainty and heavy tails are modeled using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure. A Proximal Policy Optimization (PPO) strategy incorporates practical constraints like transaction costs. Testing on ten global equity indices across various market regimes showed that the DRL method achieved competitive performance and reduced downside risk during stress periods.
Accurately forecasting bike-sharing station demand is difficult due to complex spatial and temporal interactions. Researchers introduced STAGformer, a Spatio-Temporal Agent Graph Transformer designed for efficient global modeling with linear complexity. It utilizes a two-step agent attention mechanism to aggregate global information from a small set of tokens before distributing it across individual stations. The model incorporates modules for spatio-temporal encoding, graph propagation, local pattern extraction, and global dependency modeling. Testing on NYC Citi-Bike and Chicago Divvy-Bike datasets showed STAGformer significantly outperforms existing state-of-the-art methods in RMSE and MAE metrics.
Label skew degrades federated learning performance due to client drift. To mitigate this imbalance using synthetic data augmentation without excessive computation, the authors propose FedEAS. This policy calculates an entropy-adaptive per-class generation budget for each client based on its local label distribution. The resulting total generation budget is determined by summing these individual budgets, rather than being fixed beforehand. FedEAS achieves most of the accuracy benefit of full class balancing while significantly reducing the required generation budget by 94.1%.
Wearable motion data offers vast potential for human behavior analysis using foundation models, but current methods lack understanding of effective pretraining strategies across diverse settings. Inertia-1 is introduced to address this gap by providing an open framework for studying the entire lifecycle of these models. It utilizes massive global datasets exceeding 18.2 million hours of accelerometer data. The framework allows controlled exploration of variables including sensor modality, device placement, sampling rates, model architectures, and training objectives. Evaluations across 15 diverse datasets demonstrate state-of-the-art performance for various tasks while establishing a comprehensive guide for wearable motion representation learning.
Attention head scores are modeled by bilinear forms using learned operators $M = W_q^T W_k$, which are generally non-symmetric and possess complex eigenspectra. Analysis across multiple pre-trained models reveals that the strongest previous-token heads exhibit spectral rotational patterns under RoPE, distinguishing them from content-based positionings like ALiBi. Removing the imaginary component of the RoPE phase successfully abolishes induction in these specialized heads across three distinct RoPE implementations. Furthermore, computational studies show that even when certain connections are blocked, capability remains intact, although with a noticeable increase in training delay. Ultimately, the chosen positional scheme determines the inherent spectral structure or "default algebra" adopted by an attention head's solution rather than acting as an unbreakable constraint.
Proposes Task-Semantic Field Factorization (TSF), an LLM-guided framework for industrial time-series forecasting where labeled data is scarce. TSF leverages process documents to build a task-semantic field offline, using the LLM solely for this construction. During online training and inference, variable semantics are activated within each numerical window, guiding predictions. This integration allows semantic information to support adaptation across different prediction targets and operating shifts. On various industrial tasks, TSF achieved an average MAE reduction of 6.4%, significantly improving forecasting capability with minimal added parameters or overhead.
When evidence restricts belief to a set of probability measures (a credal set), valuing information requires different approaches than using a single measure. The paper introduces a rule-specific VOI, which quantifies the value for decision-makers employing a specific decision rule like Gamma-maximin. It also derives a fixed-measure envelope that assesses classical VOI across all admissible precise measures within the set. A key finding is that the rule-specific Gamma-maximin value can surpass this envelope, showing they are not equivalent. Furthermore, the expected value of perfect information exhibits concavity over the credal set.
Determinantal point processes are gaining traction as advanced sampling techniques offering superior approximation properties over i.i.d. samplers. This work extends these deterministic sampling methods beyond traditional Euclidean spaces to much broader settings. Explicit rate guarantees are established for determinantal sampling using spectral kernels derived from Laplacian and Markov diffusion operators. The approach successfully applies to complex domains, including Riemannian manifolds and weighted networks like k-nearest neighbor graphs. These guarantees show improved dependence on the intrinsic dimensionality ($d_{\text{int}}$), matching known optimal rates in Euclidean spaces.
Weighted empirical measures on compact manifolds are relevant in fields like importance sampling and representation learning. Standard weight-only summaries fail to account for the manifold's geometry. The work introduces heat-kernel entropy profiles, a multiscale summary tracking nonuniformity via intrinsic heat flow diffusion. For R\'enyi entropy, this profile yields a geometric effective sample size that adjusts for particle proximity while matching traditional methods when particles are separate. Experiments on spheres demonstrate the profile successfully captures structural features like antipodal or multimodal arrangements missed by simpler weight-based summaries.
Diffusion models rely on solving for the score function's governing Hamilton-Jacobi-Bellman PDE to sample complex probability densities. Existing solution methods, like PINNs, suffer from slow training and hyperparameter sensitivity. This work introduces a novel solver utilizing the functional tensor train (FTT) format. The FTT representation efficiently approximates high-dimensional functions by leveraging low-rank structures for model compression. By combining this with backward stochastic differential equations, the method achieves fast, robust, and accurate sampling.
Investigates methods for locating stationary points in stochastic convex optimization problems. Instead of relying on weaker surrogates like proximity guarantees, the focus is on ensuring the subdifferential itself contains a small element near the optimum. This criterion is challenging because subdifferentials do not uniformly converge even near optimal points. Convergence proofs utilize dimension theory to decompose the graph structure of the function's subdifferential. This decomposition allows proximal-point-like methods to effectively benefit from stochastic sampling.
Hidden Markov Models (HMMs) are frequently used for time-series analysis, but many real-world systems require factorial HMMs (fHMMs) to capture multiple independent factors. Reformulating an fHMM as an equivalent standard HMM results in an unmanageably large state space and computational cost for core algorithms like forward filtering. This research proposes developing scalable methods using tensor algebra to analyze fHMMs directly. By exploiting the multidimensional structure of fHMMs, the authors bypass the need to construct overly large intermediate HMM representations. This novel approach significantly improves performance, allowing for efficient analysis of large systems and datasets.
DiPhon introduces a diffusion framework to address the scaling challenge in generating large graphs within the dense-graph setting. It leverages graphons, which represent the size-agnostic limit of dense graph sequences, by formulating a continuous process on the graphon space via a Jacobi SDE. DiPhon discretizes this continuous process into a manageable graph-level method that mimics the underlying dynamics on finite graphs. The model utilizes a tractable form for the reverse-time score, estimated through data from graph denoising. This approach allows for generating progressively larger graphs at inference time without retraining, thus preserving key topological properties.
Investigates recovering sparse functions from limited, noisy, and indirect measurements within statistical inverse learning theory. It models the unknown as an $\ell^1$ element observed through a potentially nonlinear forward operator $A$. The authors propose and analyze an $\ell^1$-regularized empirical risk minimizer, establishing almost-sure consistency and non-asymptotic convergence rates. These derived convergence rates are shown to be optimal by matching minimax lower bounds, depending on source smoothness and spectral decay characteristics. Finally, the methodology is successfully applied to concrete problems like elliptic PDE coefficient identification and sparse computed tomography.
Proposes a unified detection framework utilizing Mahalanobis distance scores (MDS) to detect various AI-related content, including LLM text generation, hallucinations, watermarks, and adversarial examples. A core component involves robustly estimating the covariance matrix for diverse "positive" sample types, such as human or factual writing. To handle multiple, potentially varied positive classes, joint estimation methods are developed for both casewise and cellwise minimum covariance determinant (MCD) estimators. The authors provide efficient optimization algorithms and prove the convergence and high breakdown point properties of these joint estimators. Empirical results validate the effectiveness of this proposed detection framework.
Standard marginal conformal prediction fails significantly in drug discovery with imbalanced datasets, failing to cover the minority class adequately across multiple benchmarks. This issue persists regardless of the underlying model used for prediction. The failure is mathematically linked such that the undercoverage of the rare class equals an amplified surplus in the majority class. Class-conditional (Mondrian) conformal prediction successfully restores minority class coverage while slightly increasing the size of the output prediction sets. Researchers also propose a diagnostic and cost model suggesting abstaining on affected molecules can improve screening utility from net-negative to net-positive.