展示 HN：Omni_genesis – 一个为执行而构建的132模态AGI核心
我是一名构建者，而不是研究者。我厌倦了看着单体变换器在非文本数据上窒息，并在实时感知流的重压下崩溃，因此我跳过了白皮书的循环，构建了OMNI_GENESIS（v0.7.0）——一个旨在实现高速度AGI执行和前额叶模拟的模块化框架。
<p>代码库：<a href="https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-Implementation-v1" rel="nofollow">https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-...</a>
<p>该架构处理132种异步模态——从生物信号到通过Scapy获取的网络熵——这些模态被压缩成一个1344维的因果流形。通过在解耦包（请参见/packages和/TCS）之间利用分层的“三脑”管道，该系统在标准模型失效的情况下保持认知稳定。
<p>技术规格：
* 模块化架构：在专用目录中分布式逻辑，将感知皮层与因果推理引擎分开。
* 分层处理：集成了snnTorch（脉冲神经网络）用于反射性时间触发和torch_geometric（图神经网络）用于情节记忆保留。
* TCS-25可塑性：实施了修改过的Hebbian逻辑，优先考虑“惊讶”（MSE增量）而非静态权重，实现了在线学习而无需反向传播延迟。
* 性能：使用Polars进行了优化，实现了对全132模态缓冲区的亚毫秒熵检查。
<p>当前状态：
感知皮层和HTSP（分层时间）单元已完全运行。该系统以高精度处理512到4096的潜在空间扩展。这是一个以实现为先的项目；代码库就是证明。
<p>我发布这个v0.7.0代码库以供架构同行评审和技术验证。我特别希望与任何致力于非变换器AGI范式的人讨论HLS投影数学和模块化推理引擎的协调。
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I am a builder, not a researcher. I got tired of watching monolithic Transformers choke on non-text data and crash under the weight of real-time sensory streams, so I bypassed the whitepaper cycle to build OMNI_GENESIS (v0.7.0)—a modular framework designed for high-velocity AGI execution and frontal lobe emulation.<p>Repository: <a href="https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-Implementation-v1" rel="nofollow">https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-...</a><p>The architecture handles 132 asynchronous modalities—from biological signals to network entropy via Scapy—flattened into a 1344-dimensional causal manifold. By utilizing a tiered "Tri-Brain" pipeline across decoupled packages (see /packages and /TCS), the system maintains cognitive stability where standard models fail.<p>Technical Specifications:
* Modular Architecture: Distributed logic across specialized directories, separating the Sensory Cortex from the Causal Inference engine.
* Tiered Processing: Integration of snnTorch (Spiking Neural Networks) for reflexive temporal triggers and torch_geometric (GNNs) for episodic memory retention.
* TCS-25 Plasticity: Implementation of Hebbian-modified logic prioritizing "Surprisal" (MSE delta) over static weights, enabling online learning without backprop latency.
* Performance: Optimized with Polars for sub-millisecond entropy checks across the full 132-modality buffer.<p>Current Status:
The Sensory Cortex and HTSP (Hierarchical Temporal) units are fully operational. The system handles the 512-to-4096 latent space expansion with high precision. This is an implementation-first project; the codebase is the proof.<p>I am releasing this v0.7.0 codebase for architectural peer-review and technical validation. I am specifically interested in discussing the HLS projection math and the orchestration of modular inference engines with anyone working on non-transformer AGI paradigms.