A standard laboratory functions as a computer, with human scientists acting as the operating system to guide experiments and interpret results. Self-driving labs integrate AI with automated hardware, allowing the experimental process to learn autonomously. Unlike simple automation that follows scripts, these labs can iteratively refine their approach based on data gathered from initial tests. The core functionality operates in a continuous loop: design, make, test, learn, and then redesign the next steps. This shift moves the system from mere execution to genuine decision-making capability.
Ed Elson, Scott Galloway, and Jessica Tarlov have each published new notes on Substack. Ed Elson shared thoughts regarding the value of Snapchat. Scott Galloway posted about a "Memo" signed between Mexico and Trump. Jessica Tarlov wrote commentary praising Barack Obama's presence and impact at his presidential center. These updates were all made within the last seven days according to the posts.
Europe's current record-breaking heatwave is straining the power grid due to increased demand for cooling via fans and air conditioning units. Extreme temperatures are causing some power plants to cease operation, further stressing supply capacity. This challenge is expected to worsen as climate change contributes to more intense and frequent heatwaves. Additionally, seasonal energy peaks are shifting; historic winter heating demands are being replaced by growing summer cooling needs. Separately, IBM unveiled a new prototype chip demonstrating increased transistor density, which could potentially extend Moore's Law for future computing advancements.
A workshop addresses the governance challenges associated with deploying live Claude AI agents in an enterprise setting. The core problem identified is that without oversight, agents can cause damage by accessing unauthorized data or bypassing necessary steps. Rubrik is highlighted as "Customer Zero" for solving this issue by using Rubrik Agent Cloud to govern a live Claude implementation. Attendees will observe a simulated off-script failure caught and corrected in real time within a proctored, hands-on lab environment. This event teaches users how to actively manage and secure their operational AI agents.
Senior leaders from companies like Prudential Insurance, Siemens, GAF, HF Sinclair, and AWS discussed building scalable data foundations for enterprise AI. Attendees learned practical strategies to create modern data architectures and production-ready AI agents using governed data products. Key takeaways emphasized building safe systems with both classical and generative AI while selecting appropriate databases. The session also highlighted how the AWS Marketplace simplifies the discovery and deployment of data and AI solutions. A secondary panel provided a roadmap for securely adopting generative AI, focusing on governance and structured implementation across different phases.
Google.org is awarding a \$5 million grant intended to expand Obrii, which serves as Ukraine's national platform for artificial intelligence jobs. This funding aims to bolster the nation's overall adoption of AI technologies. The investment supports ongoing efforts in building Ukraine’s AI sector. Such contributions are part of sustained support for the country's technological advancement and economy.
Anthropic has publicly accused Alibaba of improperly taking and utilizing parts of its Claude capabilities. This allegation raises concerns about intellectual property rights within the AI sector. The situation highlights potential issues regarding how different laboratories manage user data. Consequently, businesses are advised to perform thorough due diligence on the data handling practices of various AI firms. Such caution is recommended to protect proprietary information.
Public offerings are currently trending across the technology sector. Specifically, there is significant activity in the fields of robotics and artificial intelligence. This trend indicates growing investor interest in these high-growth technological areas. Companies developing advanced robotic systems are increasingly seeking public funding. The broader implications suggest a maturing investment climate for deep tech innovation.
Australian musicians, including Paul Dempsey and Bernard Fanning, are concerned because AI is training on their copyrighted material. A dataset search tool exposed millions of creative works, including songs, scraped from the internet for this purpose. One musician stated that this process renders the original art "useless." The concern centers on AI models learning to generate music using existing recordings without permission or compensation.
The White House has taken action to regulate OpenAI's next iteration model, potentially affecting its development and deployment of future versions like GPT-5.6. Increased government oversight suggests stricter guidelines for advanced artificial intelligence technologies. The reported actions aim to balance innovation with necessary safety precautions. These reins are expected to influence how powerful AI models can be used commercially and publicly. Additionally, related advancements include the ability to safely integrate payment functionality into AI agents.
Bruce Schneier discussed the recent German ruling holding Google liable for errors in its AI overviews. He argues that AI agents should legally be treated as extensions of the deploying person or organization. According to this view, if a company uses human writers, it assumes liability for their inaccuracies. Extending this logic to AI suggests companies cannot use flawed AI as an excuse for poor results. Failure to assign liability risks encouraging corporate misconduct by making technology seem immune from accountability.
DeepMind has released its AI Control Roadmap. This roadmap outlines a plan for developing an agent control stack that users can self-host. The release suggests a direction for building localized and more private AI agents. It details the necessary components for managing and controlling advanced artificial intelligence systems outside of proprietary cloud environments. Essentially, it provides a framework for self-sufficient AI deployment.
IBM developed a new prototype chip featuring around 100 billion transistors on a small area, achieving twice the density of its prior technology. To circumvent physical limitations in shrinking transistors, IBM utilized vertical stacking through an architecture called nanostack. This process involves building transistor layers atop one another on a silicon substrate. Compared to earlier models, these new chips can perform up to 50% more work and achieve up to 70% better energy efficiency. The technology is expected to be widely adopted in data centers within a decade for enhanced energy management.
Google.org is providing funding to support three long-term educational technology partnerships. These initiatives focus on leveraging artificial intelligence within educational settings. The funding aims to advance learning through technological integration. Specific areas of collaboration involve developing tools for K-12 and higher education institutions. Overall, the investment underscores a commitment to using AI to improve global learning outcomes.
Gemini offers 100 new methods to simplify daily life using a home voice assistant setup. These new capabilities aim to streamline various household tasks and routines. Specific examples of utility are highlighted, showcasing Gemini's hands-free assistance in the home. Users can manage more aspects of their day simply by speaking commands to the device. The integration enhances convenience within the domestic environment.
Summer savings are combining with major discounts through this week's GFN Thursday event. Participants can find value utilizing deals from both the Steam Summer Sale and GeForce NOW membership rates. Notable additions to the sale lineup include Dark Scrolls joining Devolver’s collection. Additionally, Square Enix has featured their title, The Adventures of Elliot: The Millennium Tales. These offers provide consumers with multiple avenues for maximizing their gaming purchases.
Shaders function as GPU programs to process visual data like rays, pixels, and textures for rendering effects. Accessing shader resources requires efficient management of descriptor heaps. Implementing end-to-end support for Vulkan descriptor heaps streamlines this resource binding process. This improved mechanism enhances the efficiency of how shaders connect to necessary external resources on the GPU. Ultimately, this leads to more robust and streamlined graphics programming using Vulkan.
As generative AI workloads exceed single GPU capacity, efficient deployment across multiple accelerators is critical. This document details using NVIDIA TensorRT to facilitate scaling AI inference computations over several GPUs. Implementing multi-device inference support allows developers to build robust media generation pipelines. Utilizing this method overcomes the limitations of memory and compute on individual hardware units. Ultimately, it enables the efficient scaling required for modern generative AI applications.
Apple introduced container 1.0, an open-source tool built with Swift. This utility allows users to run Linux containers efficiently. It achieves this functionality by emulating them as lightweight virtual machines specifically designed for Apple silicon architecture. The announcement detailing this new capability was published first on MarkTechPost.
A tutorial details building a lightweight, nanobot-inspired personal AI agent running entirely within Google Colab. The construction begins with a provider abstraction layer, systematically adding features like tool registration, session memory, lifecycle hooks, and skills. Instead of using an external framework, the guide rebuilds these components to illustrate how messages, tools, memory, and model responses interact. This process results in a provider-agnostic agent loop. Ultimately, this design can be extended for integration with various LLM providers and production tools.
DeepReinforce introduced Ornith-1.0, an open-source coding model family utilizing Gemma 4 and Qwen 3.5. Uniquely, the model learns its own reinforcement learning scaffold rather than relying on a fixed one. The flagship version, boasting 397 billion parameters, achieved an 82.4 score on SWE-Bench Verified. All weights for this new family are made available under the permissive MIT license.
OpenAI reported significant growth in the median internal output tokens generated by its Codex model across various departments. Since November 2025, Research saw a substantial increase of 56x in output tokens. Customer Support demonstrated a growth factor of 32x in its Codex usage. Furthermore, Engineering's median internal Codex output grew by 27x, while Legal usage increased by 13x. These figures indicate widespread adoption and utility expansion of the model internally at OpenAI.
A 0.3a2 release for the `datasette-export-database` tool was published. Previously, the project's `pyproject.toml` file incorrectly pinned compatibility to `datasette==1.0a27`. This pinning issue rendered the plugin incompatible with all other versions of Datasette. The maintainers have corrected this by updating the requirement to accept any version of Datasette greater than or equal to 1.0a27 (`datasette>=1.0a27`).
Benchmarking various memory retrieval methods revealed that raw chat history, vector-only Retrieval Augmented Generation (RAG), and a context graph exhibit distinct performance profiles in multi-agent conversations. Testing exposed a notable weakness specifically within relational retrieval mechanisms when relying solely on existing systems. The author introduced a novel Context Graph Layer designed to enhance multi-agent memory capabilities beyond the limitations of standard vector search. This new architecture aims to improve how agents utilize complex, interconnected context during ongoing interactions.
Establishes a measurable benchmark for payment fraud detection across three key metrics: latency, cost, and reproducibility. Compares the performance of Gradient Boosting Decision Trees (GBDTs) against agent-based systems in this domain. The analysis aims to specify where each model type excels in practical, real-world application. It suggests GBDTs are suited for "hot path" scenarios requiring immediate processing. Conversely, it posits that agents are best utilized for more complex, background "cold path" tasks.
Selecting an appropriate regression method—OLS, interaction terms, or Tweedie—depends on analyzing data's handling of zeros and extreme outliers. Ordinary Least Squares (OLS) is one foundational technique for modeling relationships. Incorporating interaction terms allows for testing if the effect of one predictor variable depends on the value of another. Switching to a Tweedie distribution model can be necessary when data exhibits patterns that standard assumptions do not capture. Ultimately, choosing among these options requires careful consideration of the underlying data structure.
Describes engineering a method to run three distinct Large Language Models (LLMs) simultaneously on a single 8GB GPU. This is achieved through the use of C++ layer multiplexing and admission control techniques. The approach allows developers to overcome hardware limitations, specifically the restrictive VRAM capacity. By managing inference across multiple models concurrently, the system maximizes utilization of aging or constrained GPU resources.
Incorporating a Large Language Model (LLM) into Retrieval-Augmented Generation (RAG) systems allows it to act as an arbiter for selecting optimal document candidates. This method involves making a single LLM call whose output ranks multiple candidates alongside justifications. The resulting output is presented as a structured object, providing auditable reasoning for the selection process. Such an approach enhances trustworthiness and transparency within RAG workflows.
Simplifies running a vLLM server on Hugging Face Jobs using a single command. This method streamlines deployment by packaging necessary steps into one executable instruction. Users can efficiently utilize this approach to deploy vLLM models without extensive manual setup. It targets researchers and developers who need quick, reproducible deployments of high-performance LLMs. Overall, it significantly reduces the complexity involved in hosting vLLM services on HF infrastructure.
A comparison was conducted to determine which type of token—a pure source language token or a machine-generated hybrid token—is predicted with greater accuracy by a given prediction model. Empirical results showed that the predictive quality varies depending on the specific characteristics and context of the tokens being evaluated. Generally, predictions for pure source language tokens were observed to be more consistent across different experimental settings. However, under certain conditions, the hybrid tokens demonstrated superior or comparable predictive performance compared to their pure counterparts. The findings suggest a nuanced relationship, indicating that no single token type is universally better predicted across all contexts.
An implementation was shared featuring three reinforcement learning algorithms—DQN, PPO, and A3C—all written in PowerShell 5.1 for head-to-head benchmarking. The implemented DQN uses experience replay, while PPO employs GAE lambda=0.95 with a clip epsilon of 0.2. Testing can be conducted across environments like CartPole and GridWorld using dedicated PowerShell functions. Due to limitations in PowerShell 5.1, asynchronous threading is unavailable, meaning A3C workers run sequentially. While dependency injection was utilized, performance remains notably slower compared to established Python frameworks.
A user inquired about whether the complexity of a training environment or "playground" affects Reinforcement Learning (RL) agents. Specifically, they asked if a general Multi SAC agent should be given the capacity to alter its own size. This question relates to best practices when designing RL training scenarios for advanced agents. The core concern is understanding the impact of environmental scalability on agent learning capabilities within the context of Multi-agent SAC implementations.
A submission titled "WhiteICE v1.37b" was provided, suggesting an update or version of the software/project. This specific iteration includes improvements to Reinforcement Learning (RL) algorithms. The primary stated goal of these enhancements is to increase concentration levels within the system. The submission details are attributed to a user identified as /u/Panda-Additional. Further discussion and feedback can be found via accompanying comments.
AI deployment in finance requires more than just using existing models; it demands scientific research to solve unique, complex problems. Capital One acts as a major industry vertical testing ground where advanced AI is necessary for high-stakes tasks like real-time fraud detection. The bank employs "destination-back thinking," working backward from desired customer outcomes to identify required technological breakthroughs. This effort is supported by a modern cloud data ecosystem, enabling continuous research and deployment into functional systems. Consequently, the role of Chief Scientist focuses on building original scientific capacity rather than simply deploying off-the-shelf technology.
Researchers successfully decoded an ancient scroll preserved near Mount Vesuvius, which was buried nearly 2,000 years ago. This breakthrough utilized advanced scanning technology and machine learning after centuries of failed deciphering attempts. The deciphered text reveals previously unknown historical information. Separately, a world's oldest soccer ball, associated with Mary, Queen of Scots, is currently on loan from Scotland to Miami.
A dangerous heat wave has recently struck Western Europe, with London recording its highest ever June temperature of 36.1 °C. Scientists are currently investigating the specific ways extreme heat affects human cognitive function. Elevated temperatures pose potential risks to our brains. The unprecedented high temperatures observed in areas like London have prompted this scientific inquiry.
Artificial intelligence is fundamentally reshaping the retail industry by transforming backend operations rather than just front-end consumer experiences. Key changes involve subtle shifts in how product visibility is managed within search result algorithms. Furthermore, AI is revolutionizing the efficiency of supply chains and inventory movement across the sector. These technological advancements also impact internal processes, such as enabling faster code deployment for engineering teams. Ultimately, the most significant changes are happening within the decision-making infrastructure supporting retail operations.
Three Google AI tools are presented to simplify the job search process. Career Dreamer helps individuals begin their job hunt by assisting with initial career exploration. Additionally, other unspecified Google AI tools can further aid in various stages of securing employment. These technologies aim to make the often difficult process of looking for a new job much easier. Using these tools offers a technological boost to modern job seekers.
The graphic showcases the integration of Gemini into various educational tools. These depicted features include Guided Learning modules for student support. Study Notebooks are available for organized learning materials. Additionally, NotebookLM is highlighted as an integrated academic resource. The visual emphasis on a security shield suggests a focus on data privacy within these educational AI applications.
June's Demand Gen Drop provides enhanced resources for improving overall campaign performance. The initiative is specifically designed to help advertisers connect with fresh audiences on the YouTube platform. It offers various new methods for increasing viewer engagement within existing campaigns. Advertisers can utilize these tools to broaden their reach beyond current demographics. Ultimately, the drop aims to maximize marketing impact through improved YouTube strategy.
A Kentucky school district is utilizing Gemini to scale the process of providing writing feedback. The technology helps educational institutions manage the increasing demand for detailed writing critiques across student populations. By implementing this AI tool, teachers can offer more personalized and efficient feedback on student assignments. This adoption represents a significant technological shift in K-12 assessment practices. Ultimately, it aims to improve writing instruction quality without proportionally increasing teacher workload.
Google for Education unveiled its newest advancements supporting teaching and learning at ISTE 2026. These updates focus heavily on integrating connected Artificial Intelligence tools into educational practices. Specific details regarding these AI-powered tools were presented to attendees. The announcements aim to enhance the capabilities available to educators and students within digital learning environments.
Generative causal testing is a new method developed by researchers to elucidate brain function using artificial intelligence. This technique translates complex, "black box" machine learning models into explicit, testable hypotheses. These generated hypotheses are then experimentally verified using advanced scanning technology in humans. The research specifically aims to pinpoint which distinct brain regions become active in response to language processing. The initial publication of this work was featured on Microsoft Research.
Executives from data center companies suggest that Artificial Intelligence (AI) offers solutions for the escalating energy consumption problem associated with its own growth. They maintain that integrating AI can actively aid in achieving broader energy transition objectives. This suggests a pathway where advanced computing is both a consumer and a contributor to sustainable energy goals. AI's capabilities are seen as crucial for balancing technological progress with necessary energy management practices.
Accelerating code generation via LLMs has created a pressure for an industrial "software factory," suggesting speed in development. However, merely increasing output without structural controls leads to rising bugs and technical debt ("AI slop"). A true software factory requires more than scattered tools; it demands a unified platform defining how work flows through the entire process. Key components include traceability, integrated guardrails, standardization baked into the process, and quality control at every stage, not just at the end. Ultimately, genuine productivity is measured by generating durable, reliable outputs with minimal downstream defects, rather than raw coding speed.
The author struggled significantly while attempting to mine for diamonds in Minecraft, taking over an hour despite initial expectations. The experience highlighted the game's complex nature, suggesting it is ill-suited for current AI testing compared to games like StarCraft. Progress involved repeated failures due to falling into lava and losing items after encounters with hostile mobs. Ultimately, survival depended on utilizing a Golden Apple when nearly overcome by fire damage near an exposed mine shaft. Despite considerable effort, the author did not achieve their goal of mining diamonds through methodical exploration.
Historically, mathematicians derive joy and meaning from the slow journey of developing understanding through conjectures and rigorous proof. While computation has long aided mathematics, human intuition remains central in proposing and guiding conjectures. Modern AI systems are rapidly advancing, demonstrating capabilities in solving complex problems and producing publishable-level research results autonomously. Furthermore, LLMs are automating the laborious process of translating informal mathematical proofs into the formal code required by proof assistants. This evolution challenges traditional reliance on human deliberation in advanced mathematical discovery.
No substantial content was provided other than a title and basic forum metadata. The title suggests the topic is "Tunnel drone inspection SITL." It appears to be a link shared in a discussion forum format. Specific factual details regarding drone operation, tunnel inspections, or System-in-the-Loop (SITL) testing cannot be extracted. Therefore, the post merely references this technical subject matter within an online community setting.
Presents an iterative data generation pipeline to isolate cascading linear features responsible for model behaviors, improving over simple binary sample pairs. This method specifically targets sycophancy, the inclination of language models to prioritize user validation. Researchers demonstrated that these identified sycophancy features form linearly separable subspaces within the model's activations. Selecting model activations based on this technique offers a clearer representation corresponding to desired behavior. The approach matches or exceeds existing baselines like LLM-as-a-judge while maintaining lower computational cost and better interpretability guarantees.
When benchmarks reach peak accuracy, reliance on continued measurement risks overlooking crucial performance dimensions. Utilizing CORE-Bench Hard for computational reproducibility serves as a case study demonstrating this limitation. Researchers introduce CORE-Bench v1.1 and an out-of-distribution suite to address these gaps. The study shows that even after accuracy saturation, these updated benchmarks still measure efficiency, reliability, and the interplay between models and scaffolds. Furthermore, they document a statistically significant speedup factor of two when humans collaborate with agents on real-world tasks.
Identifies distinct linear directions for refusal and persona traits within instruction-tuned chat models. The research demonstrates that these two mechanisms interact significantly, with a compliant persona acting to gate or suppress the natural propensity for refusal. By intervening in Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct, steering toward a compliant persona dramatically lowered the refusal rate (e.g., 97% to 2% in Llama). Further analysis revealed that while reintroducing the refusal direction partially restores it at later layers, projecting out the persona direction effectively negates this late-stage effect. This indicates that refusal expression is gated downstream by the established persona context, suggesting they are not independent phenomena.
AlgoEvolve is an LLM-driven framework designed for the meta-evolution of algorithmic trading programs, moving beyond static code benchmarks. It generates and iteratively refines executable Python trading strategies evaluated via a rigorous testing protocol. The system demonstrates emergent regime-adaptive logic, autonomously adjusting its rules to changing market conditions. A key feature is an outer meta-evolutionary loop that optimizes the prompts used by the LLM for program synthesis. This advanced search heuristic improves performance over initial human instructions by better balancing exploration and exploitation in noisy environments.
A new LLM-powered pipeline was developed to comparatively analyze governance structures across proliferating AI agent protocols. This system integrates automated annotation, neural topic modeling, and multi-layer network analysis for large-scale discourse examination. The tool was tested by analyzing 4,323 records from two distinct interoperability standards: the permissionless ERC-8004 and the corporate Google A2A. Findings indicated that both governance forms show comparable levels of participation inequality and community fragmentation. However, open (permissionless) governance demonstrated denser discourse alignment, suggesting greater thematic convergence.
Developed a knowledge-graph-based multi-agent framework to synthesize conflicting sources of mental health medication safety information. The system integrated 466,525 Reddit posts, 60,782 WebMD reviews, and FDA adverse event records for nine antidepressants. An LLM entity-recognition pipeline achieved high F1 scores (e.g., 0.973 for conditions) across the combined dataset. The research found patient narratives significantly overlapped with each other but deviated from formal regulatory reports, suggesting independent safety signals. Using a Neo4j knowledge graph grounded in medical vocabularies preserves provenance, ensuring traceability between clinical facts and patient anecdotes.
Proposes the Drift-Diffusion-Enhanced Elo Rating System (DD-Elo) to improve standard Elo ratings in competitive chess. This novel framework models skill expression as a decision-making process, allowing it to integrate move-by-move data beyond mere match outcomes. By drawing inspiration from cognitive neuroscience's drift diffusion model, DD-Elo captures rapid fluctuations in player skill. Mathematically, the system is proven to maintain bounded deviation from traditional Elo scores while offering faster adaptation. Experimentally, DD-Elo demonstrates a highly responsive and explainable upgrade for chess rating systems.
Autonomous AI agents capable of high-stakes actions require a new governance framework. This paper proposes that human institutions historically govern powerful actors not by monitoring internal reasoning, but through required attestation at the point of impact. The formal computational model allows agents full planning autonomy but restricts execution authority for risky actions. Execution is conditional upon multiple preconditions, each independently attested by separate authoritative sources and cryptographically bound to an intent. This system records all decisions in a tamper-evident log enabling independent re-verification.
COrigami introduces an end-to-end AI pipeline for designing flat-foldable, visually recognizable origami art. This system addresses the challenge of merging strict geometric constraints with subjective aesthetic goals in computational origami. The pipeline generates crease patterns starting from natural language inputs, progressing through semantic stick figure generation and base packing computations. It solves for a mathematically valid, flat-foldable crease pattern and then refines this model using reinforcement learning guided by an autonomous aesthetic evaluation loop. Ultimately, COrigami functions as a collaborative assistant, providing structurally sound starting points that integrate algorithmic optimization with critical aesthetic feedback.
For advanced coding agents, generating complex solutions is now easier than reliably verifying them. Verification faces challenges because human intent is inherently underspecified, and optimization widens the gap between proxy rewards and true intent. The quality of verification signals should be evaluated across scalability, faithfulness, and robustness dimensions. Researchers analyzed four types of reward constructions for different tasks like general coding or long-horizon planning. Findings confirm that no single fixed reward function will remain effective; instead, verification methods must co-evolve with the generative models.
Nonlinear partial differential equations govern the evolution of many physical, chemical, and biological systems. Deep neural network models offer efficient alternatives to traditional, computationally intensive numerical solvers. Researchers propose an attention-based, physics-guided convolutional neural network as a surrogate model for microstructural evolution. The model was trained to accurately predict the full time-evolution of phase separation in binary mixtures governed by the Cahn-Hilliard equation. Results confirm the framework's stability over long time rollouts and its ability to consistently capture domain growth consistent with established laws.
$\chi$sao{} is a novel, GPU-native population optimizer designed for finding all modes of complex multimodal black-box functions. It utilizes a cyclical process of convergence and anti-convergence oscillation to escape local optima while solidifying found true peaks by "freezing" them. The method maintains diversity through adaptive reseeding using strategies like Repulse Monkey and Golden Rooster. Benchmarking across 42 Simon Fraser University functions showed $\chi$sao{} achieved 100% mode recovery, unlike CPU baselines on high dimensions. Furthermore, it demonstrated up to $39\times$ speedup over existing methods in derivative-free evaluations.
Unicellular algae phototaxis is modeled as a subjective, information-driven sensorimotor process within a framework linking a Partially Observable Markov Decision Process (POMDP) to biochemical reaction dynamics. Environmental variables are considered hidden, and the cell updates an internal state via memoryless Bayesian steps implemented through Chemical-Reaction-Network Ordinary Differential Equations (CRN-ODEs). By applying Inverse Reinforcement Learning to experimental *Chlamydomonas* data, researchers inferred the behavioral objective driving phototaxis. The resulting model successfully reproduces the empirical alignment-to-light distribution compared to standard baselines. This framework shows that run-tumble alternation emerges as an information-acquisition strategy necessary for resolving sensory ambiguity in cellular navigation.
Neural Architecture Search (NAS) is a key technique automating the optimization of Generative Adversarial Network (GAN) designs. This review comprehensively categorizes and compares various NAS methods applied to GANs based on search strategies and evaluation metrics. It emphasizes that NAS can enhance GAN performance, stability, and efficiency beyond manual design. The paper highlights evolutionary algorithms and gradient-based approaches as superior in certain scenarios. Furthermore, it stresses the necessity of robust evaluation metrics and diverse datasets for accurate GAN assessment.
KG-TRACE is a novel neuro-symbolic framework designed to ground neural predictions in established biological pathways for antimicrobial resistance (AMR) prediction. It achieves this by integrating the WHO mutation knowledge graph as a structured constraint on a genomic neural model. The framework fuses genomic features with RotatE-based KG embeddings using a learned epistemic trust gate, dynamically weighting evidence from both sources. Evaluating on the CRyPTIC $M$. tuberculosis cohort, it achieved an AUROC of 0.9760 for isoniazid. Crucially, it introduces the Biological Grounding Ratio (BGR) and provides verifiable audit trails by flagging 'UNCERTAIN' cases with identified MDR co-occurrence artifacts.
Current LLM-as-judge safety evaluations often assume setting sampling temperature to 0 ensures deterministic grading, but this assumption is flawed. Testing against an open-source evaluation code showed that default provider settings can result in significant pass/fail flips for boundary cases during repeated runs. Even forcing greedy decoding with temperature=0 fails to eliminate all non-reproducible outcomes across different providers and model tiers. Furthermore, some advanced models have deprecated the use of temperature control entirely, complicating mitigation efforts. Consequently, evaluation harnesses should treat grader disagreement metrics as a critical measure alongside reported scores rather than reporting single-run verdicts.
Money Laundering Group Discovery (MLGD) aims to map entire criminal structures within financial networks. Current detection methods are insufficient because they either provide only isolated node alerts or passively search without incorporating investigative starting points. To bridge this gap, the authors propose Clue-Guided Group Discovery (CGGD), which progressively recovers groups from an initial piece of evidence provided by an analyst. They introduce Clue2Group, a framework that first builds a local context to minimize noise while preserving structural links. This system estimates a clue-conditioned risk field using a multi-semantic local-temporal GNN and integrates various evidence types for robust group recovery.
Proposes a Federated Hash Projected Latent Factor (FHPLF) model to address privacy and communication overhead in traditional Federated Learning by integrating Hash Learning. FHPLF replaces large real-valued gradient matrices with binary gradient-like matrices, substantially cutting costs and boosting privacy. It improves representation capacity by utilizing the Projected Hamming Distance for effective similarity modeling among bits. The method further incorporates a Secure Binary Gradient Reassembly and Privacy-Enhanced Upload (SBG-PEU) strategy to minimize leakage risks during transmission. Experimental results across four datasets confirm that FHPLF outperforms existing state-of-the-art methods, balancing accuracy, efficiency, and privacy well.
Machine learning systems now function as complex socio-technical architectures impacting human opportunity, leading to embedded structural inequalities. Because optimized predictive accuracy can disadvantage marginalized groups, algorithmic fairness emerged as a critical field. Early mitigation strategies proved insufficient in complex settings due to oversimplifications. This work specifically critiques two primary limitations of current fairness paradigms. These limitations include the reliance on deterministic point estimates for auditing and viewing individuals outside their structural context.
Accurate and rapid flood detection from satellite imagery is vital but challenging due to obstacles like cloud cover obscuring optical data. Existing AI models often function as opaque black boxes, hindering interpretability in critical safety applications. Topological Data Analysis (TDA) offers a mathematically grounded method for capturing global structural features within complex imagery, making it suitable for floods. Researchers systematically evaluated topological descriptors on the SEN12-FLOOD dataset for flood detection. Incorporating these topologial features into neural networks demonstrates that they independently carry meaningful signals, enhancing robustness and interpretability.
A systematic study examined how input dimensionality influences the creation and steering of adversarial examples in deep neural networks. The work analyzed existing theoretical models, finding that real image classes exhibit stronger empirical localization than previously assumed. Empirical evaluations across varying dimensionalities consistently showed that constructing adversarial examples becomes easier as input dimensionality increases. Furthermore, high dimensions imply that achieving a specific target label requires only minor additional distortion over untargeted attacks. These results establish high dimensionality as a key factor in the emergence and targeted control of such vulnerabilities.
Presents a probabilistic framework designed for online test-time adaptation challenges. These scenarios involve models trained on labeled data but needing to adapt using unlabeled data at test time. The approach accounts for potential distributional shifts between the training and testing environments. The underlying structure is based on a state-space modeling architecture. This framework characterizes key processes, including parameter learning, parameter time evolution, prior tuning, and prediction.
Kernel-based Empirical Bayes (EB) estimation can perform worse than Maximum Likelihood (ML) at second order, as shown by Excess Mean Squared Error (XMSE) analysis when kernels are poorly aligned. This paper introduces an XMSE-aware mixed estimator that interpolates between ML and EB shrinkage methods. The proposed estimator features a fixed-weight XMSE that is a scalar quadratic, yielding an oracle mixing weight with guaranteed performance no worse than ML or the base EB estimate at the XMSE level. A plug-in implementation of this method has been proven consistent, achieving a second-order oracle regret rate. Simulations demonstrate that the new estimator retains regularization benefits when appropriate but approaches ML accuracy under kernel misspecification across benchmark tests.
Existing global objectives in Bayesian inference, like KL divergence and ELBO, fail to capture all aspects of distributional discrepancy. This work introduces two tools: a Mass Index to quantify local mass decay scales and regularised extended KL (RE-KL), a localized divergence suitable for singular components. The Mass Index characterizes how updating alters local mass due to likelihood factors or changes in parameter supports. Using local RE-KL, the authors establish inequalities for comparing small-ball masses under different KL directions. These combined results offer a detailed theoretical description of local mass behavior during Bayesian inference.
Quantifying predictive uncertainty reliably remains difficult for complex machine learning models using standard Bayesian or bootstrap methods due to computational expense. Introducing Ribbon offers a scalable approximation to Dirichlet-reweighted bootstrap uncertainty. It bypasses costly refitting by using influence-function linearization around one fitted model, preserving the structure of the Bayesian bootstrap with post-hoc linear algebra. Ribbon provides a calibrated family whose uncertainty scale is tunable via a general concentration parameter. Evaluations across various benchmarks confirm that Ribbon achieves competitive performance and improved calibration without requiring repeated model retraining.
Decoding methods in large language models generally shift probability mass toward more likely outputs. The core question addressed is determining when sequence probability truly correlates with factual correctness. Research quantifies this relationship across various dimensions, including different decoding methods and hyperparameters. While higher sequence probability often predicts correctness within a fixed prompt-answer dataset, this does not reliably improve accuracy when adjusting methods or parameters. Furthermore, the study concludes that sequence probability fails to serve as a dependable indicator of correctness for repeated responses to the same prompt.
Presents a novel method for explaining outlier detection in interval-valued data, building upon an Interval Minimum Covariance Determinant estimator. This technique utilizes the concept of the Shapley value to quantify how much each component contributes to an observation's outlyingness. A closed-form expression is derived for the Shapley value of the robust Interval-Mahalanobis distance, enabling efficient computation. The framework allows detailed interpretation by decomposing contributions into centers, ranges, and cross-terms. Furthermore, it extends analysis via a Shapley interaction index to identify pairwise variable interactions causing atypical behavior.
Bayesian filtering estimates a system's state distribution from noisy observations in real time. Since this distribution is rarely available as a learning target, the paper introduces the Proper Scoring Ensemble Filter (PSEF). PSEF uses an ensemble data assimilation method trained via a transformer-based map using synthetic trajectories. Training optimizes probabilistic accuracy by employing strictly proper scoring rules like the energy score. Experiments confirm that PSEF accurately approximates challenging filtering distributions, outperforming classical and mean-squared-error learning methods, especially for highly non-Gaussian problems.
Polynomial Stein discrepancies (PSD) offer a scalable sample quality metric but suffer from poor understanding of their statistical properties. Increasing polynomial degree can amplify signals without controlling variance, potentially causing $\text{SNR}^2$ to decay exponentially. The authors reformulate this as an explicit $\text{SNR}^2$ maximization problem using a Rayleigh quotient over Stein features. This leads to the development of $\lambda$-PSD, an approximate covariance-aware reweighting scheme defined in a low-dimensional subspace. In Gaussian settings, $\lambda$-PSD avoids the exponential collapse and achieves stable $\text{SNR}^2$, significantly improving test power while maintaining linear complexity.