Autonomous AI agents require an identity before they need a gateway to ensure security. Enterprise AI shifts the security focus from mere authentication to continuous runtime trust throughout an agent's execution. This runtime trust involves continuously validating agent behavior through intent validation, behavioral monitoring, and policy enforcement. Risks like goal drift and excessive tool invocation require runtime controls to manage autonomous decision-making. Organizations must integrate runtime governance into existing security processes to safely deploy autonomous AI systems.
AI's worst disasters may occur not through dramatic takeovers, but through gradual, unseen decisions where AI influences critical systems. International agreement should focus on establishing minimum safeguards, requiring human authority for consequential actions, and sharing failures across areas like weapons and infrastructure. A fragmented approach is insufficient due to the interconnected nature of AI, corporate agendas, and national goals. Experts advocate for a broad, systemic global strategy rather than fragmented incrementalism to manage these escalating risks. Addressing AI requires integrating scientific expertise with humanities perspectives to establish effective governance.
The Bank of England governor, Andrew Bailey, warned that advanced AI models pose a risk of destabilizing the interconnected global financial system through cyber-disruption. He expressed concern that many jurisdictions lack the necessary protocols to manage the development and deployment of frontier AI models. Bailey also highlighted the potential for AI to alter the speed and economics of cyber-risk, which could undermine market confidence. Furthermore, he worried that high valuations in AI markets combined with leverage could amplify future market corrections. Consequently, he urged authorities to prioritize safe and responsible global deployment of AI models.
Local opposition to data center development is uniting the entire political spectrum against big tech billionaires. This backlash highlights how a few individuals control decisions impacting communities, leading to a nationwide resistance movement. The issue has spurred political action, with local bans and state-level moratoriums being implemented across the country. This movement is fostering solidarity across party lines, uniting people based on shared concerns about environmental impact, pollution, and corporate power. Ultimately, the data center fight represents an opportunity to redirect partisan anger against oligarchic forces and promote a broader societal shift.
Small businesses have learned what to do and avoid with AI by observing the successes and failures of large corporations. Big companies have implemented AI in areas like software development, customer service, and security, providing models for smaller firms. Small businesses are focusing on practical applications, such as using AI for analysis and recommendations, rather than relying on unproven agentic systems. Corporations' experience with AI's costs and implementation has taught small businesses caution and to trust their own judgment. Ultimately, small businesses are utilizing AI for productivity without resorting to practices seen in corporate mismanagement.
EnvHarness is a programmable layer that adapts static agent benchmark environments to policy training. It wraps existing, frozen environments with plug-in components that interact strictly through the standard reset() and step() interface. This allows LLM designers to automatically create these wrappers using EnvRigger to diagnose and fix policy flaws. This approach enables agents to learn more effectively from interactive environments, yielding performance gains across five benchmarks. EnvHarness ships as open-source Python and requires resettable environments as a prerequisite.
Effective agent instructions require a structured approach involving flow charting, utilizing AI developer tools, and designing for failure. Instructions should clearly define how the agent interacts with tools and knowledge bases, providing specific guidance on expected outputs. Clarity, specificity, and maintainability are crucial, achieved by avoiding fluff, minimizing complex logic, and using concrete examples. Finally, provide concrete examples, define output formatting, and start small through iterative testing to ensure the agent performs robustly.
Context engineering is becoming more important than prompt engineering for large language models like Claude, focusing on the collection of sources an LLM uses to answer. Data scientists should structure this context by creating specific files, such as "CLAUDE.md," to provide clear instructions and avoid contradictory information. Effective context engineering involves modularizing skills into specific routines and utilizing artifacts like HTML files for richer data exploration. The core principle is that model failures often result from insufficient or incorrect context, making good context essential for unlocking the models' full potential. Ultimately, successful interaction requires trusting the model to navigate, maintaining modular context, and leveraging self-updating memory.
Julia originated from an MIT research project in 2009, aiming to create an easy-to-use, high-performance language for scientific computing. The language's key feature is just-in-time compilation, which makes it fast and flexible for scientific and engineering applications. This research evolved into the JuliaHub company, which now has over one million users across various fields. Julia is utilized in diverse areas, from drug design and jet engine development to advanced AI systems like Dyad 3.0.
A live 16-agent MARL league is presented, featuring partial observability, one life, and a shrinking zone mechanic. Episodes are approximately 150 seconds long, allowing for continuous league play with an Elo rating system. Policies are arbitrary code, leading to a dynamic environment where trained policies outperform initial heuristics. The league is open to entry, with hosted evaluations requiring no local GPU. A tournament season begins Monday with small cash prizes available.
AI agent security requires a layered approach where controls are not applied prematurely. Gateway controls should be the fifth layer, not the first, as they lack the necessary context about agent identity and delegation. A dependency-gated deployment model requires establishing agent inventory, distinct identities, task-scoped credentials, and attributable telemetry before enforcing runtime actions. The process emphasizes monotonic delegation, ensuring that authority is never increased during task execution. Organizations must start by inventorying agents and testing attribution before automating enforcement.
OpenClaw 2.0 was released, featuring a guided setup that reuses existing AI access and models. The Control UI was rebuilt, resulting in a 575 ms startup time and significantly reduced JavaScript requests. Sessions and transcripts are now moved to SQLite, and shared cloud sessions allow for multiplayer interaction. The system includes security considerations, such as a focus on model choice as a mitigation for prompt injection. The update also introduces new features like docked panels and a command for side conversations.
Young people are focusing on the present due to the perceived unreachability of traditional long-term financial goals. This has led to a form of "production nihilism," an indifference to traditional work and income structures. As the traditional path to wealth breaks down, Gen Z is experimenting with diverse income streams like OnlyFans and AI training. This shift reflects a move away from the "good life" fantasy and an active reshaping of values within the economy. Ultimately, this is a reaction to economic uncertainty where the source of income no longer dictates future outcomes.
AI scribes used in doctor consultations have been shown to make errors in recording drug names and diagnoses, posing a risk to patient safety. An NHS watchdog, Healthwatch England, has highlighted specific instances where AI inaccuracies led to potential medical mistakes. Patients have reported errors in consultation transcripts that were missed by GPs, which could remain in medical records if not corrected. Concerns were raised because the Medicines and Healthcare products Regulatory Agency has not classified AI scribes as medical devices, limiting oversight. Experts suggest that while AI offers time-saving potential, the risk of errors necessitates clear procedures for reporting and correcting mistakes.
Guardian journalist Michael Safi investigates the dangers and promises of artificial intelligence. The episode explores the concerns surrounding AI that Eliezer Yudkowsky has raised for decades. The discussion addresses whether it is too late to address these AI risks now that people are listening. The podcast is part of a series being released before the launch of season two. The episode was first broadcast on March 21, 2024.
Agentic AI introduces monitoring challenges because traditional observability signals break down when models execute loops and call tools. Existing monitoring stacks fail because they assume stateless inference, which is untrue for agent systems that involve sequential decision-making. Five key assumptions about model behavior break down when agents act, leading to silent failures that are invisible to standard alerts. To properly monitor agents, teams must instrument the full trajectory, focusing on run-to-run consistency and compounding failures rather than single-step accuracy. The decision to instrument depends on whether the agent takes external actions, making trajectory tracking necessary for systems that write data.
Noisy text in Retrieval-Augmented Generation (RAG) arises from user typos, transcription errors, and Optical Character Recognition (OCR) mistakes, which cause tokens in queries and documents to not literally match. Classical spell-checking tools like Levenshtein distance fail because they assume a single word change, which is insufficient when typos affect word boundaries or involve character substitutions across phrases. Embeddings and LLMs handle this noise naturally by learning contextual similarity, allowing them to tolerate typos and OCR errors effectively. While Levenshtein struggles with the asymmetry between character-level noise and phrase-level context, embeddings maintain a high degree of retrieval accuracy. Therefore, modern RAG systems rely on the contextual understanding of embeddings to bridge the gap left by classical, literal-matching spell-checking methods.
AI agents are enabling the creation of adaptive computer worms. This research focuses on the use of AI to enhance the capabilities of these malicious programs. The work explores how AI can facilitate the evolution and adaptation of worms. It details the mechanisms by which AI agents drive this adaptive behavior in malware. The findings demonstrate a new method for developing more sophisticated and evasive worms.
The author is interested in theoretical research and practical aspects of Autonomous Mobile Robot (AMR) fleet management and optimization. They find the challenge of creating a realistic simulator for warehouse possibilities and optimization methods interesting. The author is seeking recommendations for studying current frameworks and methodologies used in real-life AMR operations. They are asking for resources on where to learn more about these topics. Ultimately, the post is a request for guidance on the study and practical application of AMR fleet management.
The provided text is a link to a GitHub repository titled "Chrome dino evolutionary robotics." It points to research submitted by the user /u/SquashTemporary9387. The link includes options for viewing the repository and comments. The content itself is a reference to a specific piece of research.
AI agents and Large Language Models (LLMs) are being explored for integration with robotics. This combination aims to enable robots to perform more complex, human-like tasks. LLMs provide the reasoning and natural language understanding capabilities necessary for advanced robotic control. AI agents can use these capabilities to plan, adapt, and execute multi-step robotic actions. This synergy promises to lead to more intelligent and versatile robotic systems.
Policy Gradient for Parallel State Entropy maximization (PGPSE) expands state-space coverage using independently trained policies, but the team-entropy score fails to identify non-redundant exploration. Marginal Coverage Credit (MCC-PGPSE) is introduced to estimate policy-specific credit by combining leave-one-policy-out coverage and state-owner specialization. This credit is used to redistribute non-negative auxiliary intrinsic rewards, designed to discourage redundant visitation and promote complementary coverage. Evaluation showed that MCC-PGPSE improved normalized team state entropy and state support over the baseline. The results suggest contribution-conditioned reward allocation effectively improves complementary coverage among parallel policies.
Post-training quantization creates a validation-deployment gap because source-precision certification does not guarantee behavioral equivalence in deployed models. This gap allows for quantization-triggered backdoor attacks, as malicious payloads can remain undetected during initial checks. Research embeds latent malicious payloads that activate specific adversarial behaviors only upon INT8 or 4-bit compression. Evaluation on machine translation and content analysis showed significant performance degradation and ideological shifts upon compression. Cross-quantizer transferability demonstrates that attack persistence depends on the specific quantization scheme and architecture, not just the bit-width.
Softmax attention memory is reduced in models like GDN and KDA by using recurrent states, which are often stored in FP32 and cause memory and latency issues. DAMP studies post-training quantization of these recurrent states, finding that uniform quantization does not yield good accuracy trade-offs. The method uses quantization-error energy and decay to identify high-risk channels, storing them at higher precision while using INT8 for the rest. Evaluation on large models showed DAMP reduced recurrent-state storage by 69.1% and improved speed and reduced full-model TPOT. Ultimately, DAMP achieves accuracy close to the FP32 baseline while significantly compressing recurrent-state memory.
Block-sparse featurizers (BSF) are similar to sparse autoencoders but use small subspaces instead of single directions, targeting features on low-dimensional manifolds. The work analyzes BSF's strengths and weaknesses, noting it still exhibits issues like feature splitting and composition. The authors propose architectural changes, such as a Tournament Top-K selection rule, to mitigate feature splitting. Furthermore, they extend the block paradigm to the crosscoder. This research focuses on improving the performance of block-sparse feature representations.
Continual learning and model merging are challenged by catastrophic forgetting and weight-disentanglement error, which the paper frames as task interference. The work formalizes this interference using a layer-wise Frobenius inner product to expose the role of the optimizer. A theoretical bound isolates the spectral norm of parameter updates as an optimizer-controllable factor of interference. The Muon optimizer is shown to regulate this spectral norm, tightening the interference bound for both learning paradigms. Experimental results demonstrate that replacing AdamW with Muon yields significant accuracy improvements across various benchmarks.
Dandelion proposes a spherical version of Flower as a warp-based neural PDE solver for simulating planetary dynamics. It addresses issues with standard Euclidean machine learning architectures when applied to spherical geometry, such as distorted convolutions and positional encodings. Dandelion achieves spatial mixing through spherical coordinate changes or warps, avoiding traditional convolutions. The work introduces a new benchmark suite of natively-spherical PDE datasets for comparison. Dandelion performs excellently on these datasets, often outperforming existing spherical architectures.
A new end-to-end self-explainable multi-label graph neural network (SEMGNN) is proposed for correlated evidence attribution. This network simultaneously classifies multi-labeled nodes and identifies edges that significantly contribute to each target node's predicted labels. SEMGNN jointly learns a predictor and a sparse edge-mask explainer within a unified training objective. It leverages label-label correlations to achieve more faithful and compact explanations for node labels. Experiments demonstrate that SEMGNN achieves competitive or improved predictive performance while offering superior label-conditioned explanations on various datasets.
The framework, CARSANN, adapts nearest neighbor classification by considering local geometric complexity rather than assuming uniform neighborhood support. It estimates intrinsic dimensionality and local mean curvature to guide neighborhood size adjustments. Regions with high curvature receive greater radius shrinkage, while flat regions maintain broader spatial support. Experiments on real-world datasets show CARSANN consistently outperforms standard $k$-NN and other adaptive methods. This demonstrates that local manifold curvature provides a valuable geometric control for neighborhood adaptation.
Unsupervised representational alignment recovers stimulus-by-stimulus correspondence from geometry alone, but the automorphism group limits what can be identified before data exists. A diagnostic test based on the cheapest non-identity relabelling reveals this degeneracy, as dense sampling creates near-duplicates. This diagnostic can move representational models away from degenerate points, reducing catastrophic alignment failures significantly. The same risk exists when a regular design aligns with the candidate geometry's isometry group. This check can be performed efficiently before actual data collection.
A generative continual learning framework based on growing self-organizing maps (GSOMs) is proposed for unsupervised class-incremental learning. The method utilizes learned distributional statistics and encoder-decoder models to enable exemplar-free replay by generating synthetic samples from the GSOMs. Each GSOM unit stores mean, variance, and covariance estimates to facilitate this synthetic sample generation. This approach is fully unsupervised, as it operates without explicit class labels during training. Results demonstrate performance competitive with supervised methods while outperforming memory-free approaches on various benchmarks.
Optimal transport is used in network science to compute both dissimilarity and a transport plan explaining how one graph transforms into another. The review examines how optimal transport compares undirected, unweighted graphs using the Wasserstein, Gromov-Wasserstein, and Bures-Wasserstein distances. The paper details the one-dimensional closed form for the Wasserstein distance and derives bounds for the Bures-Wasserstein distance using Laplacian spectra. These distances are evaluated on synthetic network datasets and real-world time series networks for tasks like clustering and anomaly detection.
A mathematical theory of superposition is developed for neural networks using frame theory and compressed sensing. The model encodes sparse binary feature vectors via an overcomplete dictionary and recovers features using a ReLU function. Recovery theorems are established, including high-probability support recovery for low-coherence dictionaries. A sharp criterion for support recovery is also provided in the worst-case setting. The work specifically applies these concepts to Gaussian random matrices and equiangular tight frames.
The research studies risk-averse decision making where agents act with uncertainty about the system state. Risk is measured using optimized certainty equivalent (OCE) metrics, which encompass criteria like mean-variance risk and CVaR. The optimal policy is characterized as a prediction set-based solution for CVaR, offering an interpretation for conformal prediction sets. For unknown distributions, a data-driven calibration strategy using a synthetic model is developed to control OCE risk. The approach was evaluated on two wireless beamforming settings.
I-FLOP extends the FLOP algorithm to learn order and parents from interventional data, utilizing the interventional BIC score and iterative Cholesky-based updates. The method demonstrates that I-FLOP recovers a Directed Acyclic Graph (DAG) in the same Markov equivalence class as the true data-generating DAG in the sample limit. Performance comparisons show that I-FLOP is favorable in both accuracy and run time when tested on real and simulated interventional data. This approach is compared against existing causal structure learning algorithms. The work was accepted to the 13th International Conference on Probabilistic Graphical Models (PGM).
A framework is developed to localize global discrepancies by assigning conditional or marginal contributions to individual observations across statistical contexts. This approach connects resampling diagnostics and data valuation to projection theory and anomaly detection. The method provides efficient estimators, such as matched-context subtraction, to remove irrelevant fluctuations. Context can either facilitate efficient localization of a global discrepancy or reveal genuinely additional class information beyond the event's own features. The study demonstrates how context's role depends on the underlying shared structure of the data.
The study investigates kernel ridge regression when data is anisotropic, with input covariance decaying according to a power law with exponent $\alpha$. The research derives sharp asymptotic expressions for the kernel spectrum and generalization error in high-dimensional regimes. Weak anisotropy ($0<\alpha<1$) results in variance peaks at integer sample complexities that are damped by $\alpha$. Strong anisotropy ($\alpha > 1$) leads to a constant effective dimension and variance that plateaus or vanishes under certain conditions. The results clarify how input geometry fundamentally shapes kernel features and generalization properties.
The research proposes a multivariate extension of the pseudo-Voigt profile, a mixture of Gaussian and Cauchy distributions, for robust model-based clustering. It enforces shared location and scale parameters between the Gaussian and Cauchy components to ensure cluster coherence. Parameter estimation is achieved using an Expectation Maximization algorithm with latent variables for efficient inference. Performance is evaluated via simulations and applications to real data, showing effectiveness for heavy-tailed distributions. The model's accuracy and outlier detection capabilities are compared against established robust models.
The Empirical Maximum Entropy on the Mean (MEM) method offers a framework for solving inverse problems by incorporating data fidelity and entropy regularization. The study establishes a parametric convergence rate of $O(n^{-1/2})$ in expectation for empirical MEM, which improves upon previous guarantees. This improvement is achieved through a novel stability analysis of the optimization problems using convex analysis and probability. Furthermore, the dual problem of MEM can be reformulated as an expected risk minimization, facilitating the use of scalable stochastic gradient algorithms. Consequently, empirical MEM is presented as a statistically and computationally efficient methodology for data-driven inverse problems.
The analysis of network dynamics, learning models, and neural tangent kernels leads to a semigroup formulation of the time-dynamics. The research provides new and explicit a priori perturbation-bound results for fixed-kernel linearizations arising in the NTK setting. These bounds are expressed as finite-time perturbation estimates for the corresponding semigroup perturbations. The authors also refine these estimates for specific task spaces and extend the comparison to nonautonomous NTK evolutions. Finally, they offer worked examples illustrating these perturbation-bound estimates.