rmems/SEMM-Latent-Telemetry

 

SEMM-Latent-Telemetry

Bare-metal hardware telemetry and SNN latent space routing data for neuromorphic quantization research. This dataset documents the discovery of Semantic Attractor Clustering — that a Spiking Neural Network physically routes different semantic concepts (abstract language vs code syntax vs math logic) into distinct, repeatable biological pathways when L2 Normalization is applied to LLM embeddings.

Hub ID: `rmems/SEMM-Latent-Telemetry` Names: SEMM = Spiking Experts Mixture Mechanism · SAAQ = Spiking Adaptive Activity Quantization · Metis = research idea that SAAQ can deepen understanding of SNN–ANN hybrid setups

Dataset Details

• —Curated by: Raul Montoya Cardenas
• —Language(s): English, Code
• —License: Apache 2.0 / MIT (dual-licensed)
• —Repository: huggingface.co/datasets/rmems/SEMM-Latent-Telemetry
• —Primary routing model (first-day discovery): allenai/OLMoE-1B-7B-0125-Instruct · GGUF: allenai/OLMoE-1B-7B-0125-Instruct-GGUF
• —SR campaign models: see Models Tested (8 GGUF lineups + OLMoE baseline)
• —Implementation: corinth-canal (SNN quantization pipeline)
• —Analysis: Surrogate_Viz.jl (symbolic regression)
• —Hardware baseline (planned home): `rmems/gaming-telemetry` (RE4 path-tracing seed; multi-title corpus)

Dataset Description

Origins of Metis

Before this was a formal dataset, it was an attempt to solve a bare-metal problem. I had been experimenting with mining telemetry, HFT bots, and sync node data to train a spiking neural network (SNN), but the data kept returning dead zeros in value after being used for training.

The breakthrough came entirely by accident. I was running heavy mods — DLSS 4.0 and path tracing — on Cyberpunk 2077 and the Resident Evil 4 Remake. My workstation PC was screaming, pushing harder and louder than it ever did during crypto mining. That sparked the realization: What if I used raw gaming telemetry data for neuromorphic spike data conversion? Dynamic GPU/CPU stress traces gave usable temporal structure where mining/sync logs had returned dead zeros. (Early notes sometimes called this an “artificial heartbeat”; that framing is not a current research track — the live line is SAAQ / SEMM routing and adaptation laws.)

When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, Metis was born: the research idea that SAAQ (Spiking Adaptive Activity Quantization) could open a clearer window onto SNN–ANN hybrid setups — using real hardware pressure, MoE latent drive, and spiking routing as the instrument. This Hub dataset is the telemetry backbone for that program, aimed at understanding SEMM (Spiking Experts Mixture Mechanism).

Relationship to Spikenaut

Spikenaut is my pure SNN model, built from scratch as a native spiking neural network. Metis is the research program and teacher-side probe: SAAQ applied through the OLMoE Mixture-of-Experts model to discover routing laws and adaptation equations. Those discoveries are meant to transfer into SEMM and into Spikenaut's native training and evolution. Metis proves the math; Spikenaut implements it natively.

The Science: Semantic Attractor Clustering

This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the routing encoder. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics.

The Discovery: Physical Neighborhood Mapping

The primary breakthrough documented in this dataset is the organic, physical separation of semantic concepts into distinct routing bands. By applying L2 Normalization to the embeddings, the network bounds semantic pressure, forcing tokens to follow the biological path of least resistance.

Telemetry visualizations prove that the Spike-based routing physically routes different cognitive tasks into isolated biological neighborhoods:

When fed abstract English logic, the network distributes energy across multiple nodes, establishing a dominant attractor basin at the 2000-index walker route, with secondary echoes in Walkers 700 and 1450.

When fed rigid mathematical statements or raw Rust syntax, the network completely abandons the 2000-route. The tokens experience mathematical pushback in abstract centers and organically collapse into the exact same 600-800 frequency band. This demonstrates that the network physically maps highly structured logic tasks to adjacent biological neighborhoods to conserve energy.

Experiment Progression

The dataset documents the chronological progression from synthetic baselines to actual semantic routing:

Symbolic Regression Benchmark

The sr_benchmark/ directory contains the outputs of a multi-model symbolic regression campaign run via Surrogate_Viz.jl. The goal: discover compact mathematical equations that predict SAAQ delta-q targets from latent telemetry features.

Setup

• —Target variable: saaq_delta_q_target
• —Features: avg_pop_firing_rate_hz, membrane_dv_dt, routing_entropy, saaq_delta_q_prev
• —Operators: +, -, *, /, exp, sqrt, square
• —Config: maxsize=15, parsimony=0.01, 30 iterations per run
• —Rule: SaaqV1_5SqrtRate

Models Tested (SR benchmark)

Slug names match sr_benchmark/{slug}/ and the model column in the sr_pareto Hub config. Base = upstream weights on Hugging Face; GGUF used = local quant file lineage (community or official GGUF repo when known).

First-day Semantic Attractor Clustering maps (English / Rust / math) were produced with OLMoE only. The multi-model table is the symbolic regression campaign over SAAQ features (often RE4-backed drive + prompt embeddings), not a re-run of every routing map on every model.

Files Per Model

Each model directory contains Pareto front CSVs ({condition}_pareto.csv) and run manifests ({condition}_manifest.json). The Pareto fronts map complexity vs loss for discovered equations. See sr_benchmark/sr_pareto_all.parquet (Hub config sr_pareto) for the consolidated table, and bundle.json for campaign metadata.

Key Result

The SAAQ 1.5 delta-Q adaptation law was discovered from this benchmark:

$$\Delta q{\text{target}} \approx 0.0573 \cdot \sqrt{\bar{f}{\text{pop}}} + 0.496 \cdot \Delta q_{\text{prev}}$$

See SAAQ 1.5/ for the full derivation and interpretation.

Usage

There is no anonymous default config. Always pass a config name.

Quick Start (Python)

from datasets import load_dataset

# Bare-metal GPU/CPU telemetry (explicit config required)
hw = load_dataset("rmems/SEMM-Latent-Telemetry", "hardware_re4", split="train")
print(hw.features)
print(hw[0])

# Symbolic regression Pareto table
sr = load_dataset("rmems/SEMM-Latent-Telemetry", "sr_pareto", split="train")
print(sr.features)
print(sr[0])

Rebuild the consolidated SR table after adding runs:

python scripts/build_sr_pareto_all.py
python scripts/validate_dataset.py

Data Schema (hardware_re4 — full parquet)

Captured at ~5ms intervals via NVML on Ship of Theseus. The Hub interface matches all on-disk columns (not a reduced neuromorphic subset).

Note: RE4_path_tracing_telemetry.csv is a legacy 5-column export (gpu_temp_c, gpu_power_w, …). It is not the same schema as the parquet batches. Prefer hardware_re4 for Hub loads.

Data Schema (sr_pareto)

Built by scripts/build_sr_pareto_all.py from per-model *_pareto.csv files.

Neuromorphic Mapping

This data behaves as "sensorimotor" stimulus for neural networks:

• —Excitatory Inputs: High surges in pcie_rx_kbps indicate asset floods (e.g., BVH structure updates for path tracing), mimicking sensory signals
• —Action Potentials: encoder_util_perc, decoder_util_perc, and power_usage_mw transients represent internal activity and firing rates
• —Inhibitory Inputs: Non-zero throttle_reasons_bitmask signals act as inhibitory governors, dynamically suppressing activity
• —State/Momentum: Slow-moving temperatures (cpu_tctl_c, temperature_c) and memory capacity

Dataset Structure

├── origin_hardware_baselines/
│   └── resident_evil_4/
│       ├── system_telemetry_v1_batch_*.parquet   (48 files)
│       ├── RE4_path_tracing_telemetry.csv
│       └── README.md
├── first-day-testing-real-weights/
│   ├── first-test-falied/        # Routing collapse visualization
│   ├── second-test/              # English logic routing (2000-route)
│   ├── third-test/               # Rust syntax routing (600-800 band)
│   └── fourth-test/              # Math logic routing (600-800 band)
├── experiments/                  # Smoke test visualizations
├── sr_benchmark/                 # Multi-model symbolic regression benchmark
│   ├── sr_pareto_all.parquet     # Hub config: sr_pareto (consolidated)
│   ├── bundle.json               # Campaign metadata (prefer consolidated table)
│   ├── dashboards/               # Pareto plots (optional visuals)
│   └── {model}/                  # Per-model Pareto fronts + manifests
├── scripts/
│   ├── build_sr_pareto_all.py    # Build consolidated SR table
│   └── validate_dataset.py       # Local interface checks (CI later)
├── SAAQ 1.0/                     # Foundational L2-normalized voltage bound
├── SAAQ 1.5/                     # Delta-Q adaptation law (discovered via SR)
├── CONTEXT.md                    # Domain glossary + multi-config decisions
├── Research notes/               # Informal research notes
└── model_metadata_manifest.json  # Structured project metadata

Hub configs today: hardware_re4, sr_pareto. Gated: latent_routing (needs English + Rust + math feature CSVs).

Hardware Environment

Bias, Risks, and Limitations

• —This dataset is generated from a single hardware configuration (RTX 5080 + Ryzen 9 9950X). Routing patterns may differ on other GPU architectures.
• —The telemetry captures are from gaming workloads (Resident Evil 4 Remake with path tracing). Other GPU stress patterns may produce different power/thermal signatures.
• —The SNN routing visualizations represent a specific quantization approach (SAAQ) applied to a specific model (OLMoE). Results may not generalize to other SNN architectures or MoE models.
• —The dataset is small (48 parquet batches) and is intended for research and equation discovery, not large-scale training.

Citation

BibTeX:

@dataset{montoya_2026,
  author = {Raul Montoya Cardenas},
  title = {SEMM-Latent-Telemetry: Spiking Experts Mixture Mechanism Routing Data},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/rmems/SEMM-Latent-Telemetry}}
}

APA:

Montoya Cardenas, R. (2026). SEMM-Latent-Telemetry: Spiking Experts Mixture Mechanism routing data [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/SEMM-Latent-Telemetry

Glossary

License

This dataset is dual-licensed under Apache 2.0 and MIT.