README.md

metadata

title: TENSOR Runtime Lab
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: true
license: mit
short_description: Transformer-Native Computational Paradigm Research Demo

🧠 TENSOR Runtime Lab

Temporal Engine for Neural Search & Optimization Runtime

A research demo testing whether a transformer-native computational paradigm can replace traditional algorithm-selection, implementation, and testing workflows.

---

What is TENSOR?

TENSOR is a theoretical and empirical framework proposing that transformer-native computation can serve as a universal computational engine — one where the algorithm layer (ML, classical, numerical, graph, optimization) is abstracted away beneath a unified runtime. The interface is intent. The engine decides, selects, composes, and executes.

This Space is the Phase 1 empirical proof-of-concept, targeting three core hypotheses:

Hypothesis	Question	Demo
H1	Can a transformer replace algorithm-selection + implementation?	Tab 1: Runtime
H2	Is transformer-native computation efficient vs. hand-crafted pipelines?	Tab 2: ICU Benchmark
H3	Can this scale economically and be symbolically verified?	Tab 3: Latent Inspector

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Architecture

User Intent + Raw Data
        ↓
TENSOR Runtime  (claude-sonnet-4)
        ↓
Latent Computational Operations
  ├── Algorithm search over hypothesis space
  ├── Implementation synthesis
  └── Confidence quantification
        ↓
Symbolic Verification Layer  (Wolfram-style)
  ├── Physiological constraint checks
  ├── Trend plausibility audits
  └── Shock index + composite signals
        ↓
Explainable Output + Evidence Log

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Primary Benchmark: ICU Deterioration Forecasting

Chosen because it simultaneously requires:

• Temporal reasoning over multivariate vital-sign sequences
• Anomaly detection under physiological noise
• High-recall classification (missing a deterioration event = patient harm)
• Interpretable decisions (clinical trust requirement)
• Verification (predictions must be auditable against known physiology)

TENSOR is evaluated against a hand-crafted XGBoost baseline trained with feature engineering, cross-validation, and manual hyperparameter tuning.

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Setup

HuggingFace Space (recommended)

1. Fork or clone this Space
2. Add your ANTHROPIC_API_KEY in Settings → Secrets
3. The Space runs automatically — no other configuration needed

Local development

git clone https://huggingface.co/spaces/ashutoshzade/tensor-runtime-lab
cd tensor-runtime-lab
pip install -r requirements.txt
export ANTHROPIC_API_KEY=sk-ant-...
python app.py

Demo mode: If no API key is set, the benchmark and runtime tabs fall back to a deterministic rule-based proxy so the UI remains functional for inspection.

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Research Roadmap

Phase 1 (this paper — June 2026)
  Proof-of-concept: TENSOR selects + implements single algorithms from intent
  Benchmark: ICU deterioration vs. XGBoost baseline
  Verification: Wolfram symbolic constraint layer

Phase 2 (follow-on)
  Algorithm composition: TENSOR orchestrates multi-step pipelines
  Attention-head extraction: true mechanistic interpretability
  Hardware cost modelling: FLOPs per task vs. engineering hours at scale

Phase 3 (long-term vision)
  TENSOR as universal computational engine
  Algorithm abstraction layer eliminated entirely
  Tensor operations become the computation — not the interface to it

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Citation

@misc{tensor2026,
  title  = {TENSOR: Temporal Engine for Neural Search \& Optimization Runtime —
             Towards a Transformer-Native Computational Paradigm},
  author = {Zade, Ashutosh},
  year   = {2026},
  url    = {https://huggingface.co/spaces/ashutoshzade/tensor-runtime-lab}
}

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Files

File	Purpose
app.py	Gradio UI — three research tabs + About
benchmark.py	H2 experiment: TENSOR vs. XGBoost on synthetic ICU data
latent_inspector.py	Attention heat map + Wolfram verification layer
requirements.txt	Python dependencies

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Paper submission: June 2nd, 2026 · Research by ashutoshzade