TTA establishes AI security standards group to address emerging risks - telecompaper.com
TTA establishes AI security standards group to address emerging risks telecompaper.com
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Seeking arXiv cs.AI endorsement — neuroscience-inspired memory architecture for AI agents
Hi everyone, I’m an independent researcher (Zensation AI) seeking endorsement for my first arXiv submission in cs.AI. Paper: “ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems” Summary: ZenBrain is the first AI memory system grounded in cognitive neuroscience. It implements 7 memory layers (working, short-term, episodic, semantic, procedural, core, cross-context) with 12 algorithms including Hebbian learning, FSRS spaced repetition, sleep-time consolidation (Stickgold & Walker 2013), and Bayesian confidence propagation. Prior art: Published as defensive publication on TDCommons (dpubs_series/9683) and archived on Zenodo (DOI: 10.5281/zenodo.19353663). Open-source npm packages with 9,000+ tests. Why this matters: Recent surveys (arxiv:2603.07670) identi
LLMs as Idiomatic Decompilers: Recovering High-Level Code from x86-64 Assembly for Dart
arXiv:2604.02278v1 Announce Type: new Abstract: Translating machine code into human-readable high-level languages is an open research problem in reverse engineering. Despite recent advancements in LLM-based decompilation to C, modern languages like Dart and Swift are unexplored. In this paper, we study the use of small specialized LLMs as an idiomatic decompiler for such languages. Additionally, we investigate the augmentation of training data using synthetic same-language examples, and compare it against adding human-written examples using related-language (Swift -> Dart). We apply CODEBLEU to evaluate the decompiled code readability and compile@k to measure the syntax correctness. Our experimental results show that on a 73-function Dart test dataset (representing diverse complexity level
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Seeking arXiv cs.AI endorsement — neuroscience-inspired memory architecture for AI agents
Hi everyone, I’m an independent researcher (Zensation AI) seeking endorsement for my first arXiv submission in cs.AI. Paper: “ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems” Summary: ZenBrain is the first AI memory system grounded in cognitive neuroscience. It implements 7 memory layers (working, short-term, episodic, semantic, procedural, core, cross-context) with 12 algorithms including Hebbian learning, FSRS spaced repetition, sleep-time consolidation (Stickgold & Walker 2013), and Bayesian confidence propagation. Prior art: Published as defensive publication on TDCommons (dpubs_series/9683) and archived on Zenodo (DOI: 10.5281/zenodo.19353663). Open-source npm packages with 9,000+ tests. Why this matters: Recent surveys (arxiv:2603.07670) identi
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