AI robustness
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Stability Over Retraining: A New Approach to AI Forgetting
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An intriguing experiment suggests that neural networks can recover lost functions without retraining on original data, challenging traditional approaches to catastrophic forgetting. By applying a stability operator to restore the system's recursive dynamics, a network was able to regain much of its original accuracy after being destabilized. This finding implies that maintaining a stable topology could lead to the development of self-healing AI agents, potentially more robust and energy-efficient than current models. This matters because it opens the possibility of creating AI systems that do not require extensive data storage for retraining, enhancing their efficiency and resilience.
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FlakeStorm: Chaos Engineering for AI Agent Testing
Read Full Article: FlakeStorm: Chaos Engineering for AI Agent Testing![[P] FlakeStorm: Chaos Engineering for AI Agent Testing (Apache 2.0, Rust-accelerated)](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8197-300x171.png)
FlakeStorm is an open-source testing engine designed to enhance AI agent testing by incorporating chaos engineering principles. It addresses the limitations of current testing methods, which often overlook non-deterministic behaviors and system-level failures, by introducing chaos injection as a primary testing strategy. The engine generates semantic mutations across various categories such as paraphrasing, noise, tone shifts, and adversarial inputs to test AI agents' robustness under adversarial and edge case conditions. FlakeStorm's architecture complements existing testing tools, offering a comprehensive approach to AI agent reliability and security, and is built with Python for compatibility, with optional Rust extensions for performance improvements. This matters because it provides a more thorough testing framework for AI agents, ensuring they perform reliably even under unpredictable conditions.
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Building a Self-Testing Agentic AI System
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An advanced red-team evaluation harness is developed using Strands Agents to test the resilience of tool-using AI systems against prompt-injection and tool-misuse attacks. The system orchestrates multiple agents to generate adversarial prompts, execute them against a guarded target agent, and evaluate responses using structured criteria. This approach ensures a comprehensive and repeatable safety evaluation by capturing tool usage, detecting secret leaks, and scoring refusal quality. By integrating these evaluations into a structured report, the framework highlights systemic weaknesses and guides design improvements, demonstrating the potential of agentic AI systems to maintain safety and robustness under adversarial conditions. This matters because it provides a systematic method for ensuring AI systems remain secure and reliable as they evolve.
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Aligning AI Vision with Human Perception
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Visual artificial intelligence (AI) is widely used in applications like photo sorting and autonomous driving, but it often perceives the world differently from humans. While AI can identify specific objects, it may struggle with recognizing broader similarities, such as the shared characteristics between cars and airplanes. A new study published in Nature explores these differences by using cognitive science tasks to compare human and AI visual perception. The research introduces a method to better align AI systems with human understanding, enhancing their robustness and generalization abilities, ultimately aiming to create more intuitive and trustworthy AI systems. Understanding and improving AI's perception can lead to more reliable technology that aligns with human expectations.