AutoNexus Cognitive Engine (Holo-Synaptic Architecture)

Overview

AutoNexus is an experimental autonomous logic engine designed to integrate multi-stage cognitive reasoning loops with sparse execution layers.

Notice Regarding Benchmark Evaluations: All quantitative metrics previously reported in early development iterations (such as simulated pass rates on SWE-bench Verified, GPQA Diamond, and GSM8K) were synthetic theoretical estimates generated during internal design testing.

True empirical verification on the full 500-instance SWE-bench Verified test set requires full containerized Docker test harnesses with regression and pass validation (FAIL_TO_PASS and PASS_TO_PASS) per instance, which is computationally bound by hardware and execution infrastructure.

Users and researchers must execute their own localized evaluations within isolated test environments to establish formal empirical baseline figures.

Architectural Specification

• Engine Core: Multi-stage iterative reflection loop with state memory
• Quantization Compatibility: Support for low-bit tensor representations (INT4/Sparse formats)
• Local API Endpoint: Standard FastAPI + Gradio interface on port 7860
• Integration Framework: PreTrainedModel and PretrainedConfig abstraction for Hugging Face Transformers

Local Execution

To launch the interactive control interface locally:

python d:\kimi3\app.py

Endpoint available at http://localhost:7860.