Science – Page 23 – Investment Policy

AI’s New Shield: Securing Autonomous Cyber Defense

12.03.2026 by Ray Dalio

The system employs a multi-agent architecture-built around a central Multi-Agentic SOAR and phase-scoped servers for monitoring, analysis, administration, and reporting-to automate security operations, leveraging LLM-driven reasoning and a persistent organizational memory layer for coordinated incident response across all stages of the lifecycle.

As artificial intelligence takes on a larger role in cybersecurity, a new framework is needed to address the unique risks posed by autonomous, multi-agent systems.

Decoding Equivalence: A New Algebraic Approach to Linear Codes

11.03.2026 by Ray Dalio

This review explores how algebraic modeling, specifically leveraging Plücker coordinates and invariant theory, advances our understanding of linear code equivalence and its implications for cryptographic security.

Pinpointing Quarkonium Splitting with Unprecedented Precision

11.03.2026 by Ray Dalio

$The hyperfine splitting of the P-wave bottomonium state for [latex]n=2[/latex] is experimentally determined, with results aligning with theoretical predictions based on a [latex]m_{b,\rm PV} = 4.836 \text{ GeV}[/latex] value for the [latex]b[/latex] quark mass, as established by Ayala et al. (2020).$

New calculations achieve next-to-next-to-next-to-next-to-leading order (N4LO) accuracy in determining the hyperfine splitting of heavy quarkonium states.

Playing It Safe: Robust Multi-Agent Learning in Competitive Environments

11.03.2026 by Ray Dalio

Algorithms employing varying levels of risk aversion demonstrate a predictable convergence toward either mutually beneficial cooperation-achieved by non-risk-averse and low-risk-averse agents-or self-protective, though less rewarding, isolation, as evidenced by the consistent selection of stag-stag or hare-hare outcomes during training.

A new algorithm tackles the challenge of training teams of AI agents to cooperate and compete reliably, even when facing unpredictable opponents.

Securing Cloud Workloads: The Lockbox Approach

11.03.2026 by Ray Dalio

Lockbox presents a detailed architecture designed to compartmentalize sensitive data, enabling a robust system for secure storage and access.

A new architecture minimizes risk by ensuring sensitive data remains encrypted even during processing in the cloud.

Beyond Bits: Achieving High-Fidelity Communication with Analog Coding

11.03.2026 by Ray Dalio

The system articulates a foundational approach to image transmission, integrating source coding and channel coding within a unified analog framework-a configuration destined to degrade over time, yet designed to maximize information fidelity within the constraints of a decaying signal.

Researchers have developed a new framework to bring the benefits of analog joint source-channel coding to existing digital WiFi infrastructure.

Secure Search for Many: A New Approach to Data Privacy

11.03.2026 by Ray Dalio

The MASSE scheme establishes a high-level system workflow, integrating multiple components to achieve a cohesive and functional architecture.

Researchers have developed a novel searchable encryption scheme that allows multiple users to securely access data based on their individual permissions.

Quantified Boolean Formulas: Taming Complexity with Limited Existential Variables

11.03.2026 by Ray Dalio

New research establishes a definitive lower bound for solving a fundamental class of logical problems, even with simplified constraints, and offers algorithmic improvements for a common subproblem.

Decoding Futures Risk: A New Measure for Slippage

11.03.2026 by Ray Dalio

$A concentration-adjusted slippage analysis across 184 tokens reveals a pronounced liquidity risk within the most extreme 5% of observations, demarcated by a [latex]\mathrm{SaR}^{\mathrm{adj}}(0.95)=3.47\%[/latex] threshold, suggesting heightened vulnerability in those specific assets.$

A novel framework, Slippage-at-Risk (SaR), offers a proactive way to assess execution risk in the fast-moving world of perpetual futures trading.

Codecs Under Attack: Balancing Speech Clarity and Security

11.03.2026 by Ray Dalio

Neural automatic speech recognition systems benefit from a novel inference-time transformation leveraging audio codecs, which introduce a discrete bottleneck that, while ignored by standard adversarial optimization techniques like PGD, is addressed by BPDA+EOT through the approximation of codec gradients, ultimately enhancing robustness.

New research reveals a critical trade-off in neural audio codecs: deeper compression can improve speech recognition, but also opens the door to adversarial manipulation.