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license: mit
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# Fused Patent + arXiv Technical Clustering Dataset (Deterministic, Quality-Gated)

## Overview

This dataset is the output of a zero-touch technical clustering pipeline built over a fused corpus of patent text and arXiv-style research text.

The pipeline is fully deterministic from ingest through release and is designed to run end-to-end without manual curation or mid-run intervention. All artifacts, cluster assignments, and release decisions are derived from the same run.

This is not a curated dataset. It is a large-scale fused technical corpus that has been **deterministically analyzed and quality-gated** to isolate the portion that behaves like a semantic clustering dataset.

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## Key Stats

* **Total labeled rows:** 9,063,272
* **Raw clusters (pre-filter):** 422
* **Release clusters (post-filter):** 147
* **Retained rows:** 3,881,329
* **Retention rate:** 42.82%
* **Shards:** 91 (labels / embeddings / chunks)
* **Size:** ~20+ GB compressed

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## Pipeline Summary

The dataset was produced by a staged, resumable pipeline with Postgres acting as a control plane.

### Core stages

* Ingest and normalize fused patent + arXiv text
* Chunk-level embedding
* Embedding clustering
* Shard-level processing with persistent state
* Reducer-tree merge into global clusters
* Global assignment + BM25 artifact generation
* Deterministic inspection and release gating

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## System Design

The pipeline is built to operate under real constraints (long runtimes, memory pressure, interruptions), not ideal notebook conditions.

### Control plane (Postgres)

* Task leasing and discovery
* Heartbeats and worker liveness
* Stage state tracking (not-ready / running / done / failed)
* Reducer-tree coordination and staged unblocking

### Failure-aware execution

* Distinguishes between:

  * true OOM
  * bad allocation
  * killed process
  * general memory pressure
* Descending batch ladder (deterministic step-down on failure)
* Proactive downshifting based on resource pressure
* Resumable state across interruptions

### Reducer-tree merge

* Progressive level-by-level reduction
* Final stage unblocked only after upstream completion
* Prevents global merge bottlenecks
* Avoids downstream fan-out gaps

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## Deterministic Quality Gating

The raw clustering output was **not** treated as valid by default.

A full deterministic inspection pass across all 422 clusters produced:

* **147 coherent clusters**
* **107 mixed clusters**
* **168 metadata-heavy clusters**

### Filtering decision

For the release dataset:

* **Kept:** coherent clusters only
* **Dropped:** mixed + metadata-heavy clusters

This was done without:

* re-embedding
* hand labeling
* manual cluster editing
* modifying the original run

All decisions are reproducible from pipeline outputs.

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## Metadata Leakage

A large portion of clusters were dominated by ingestion or wrapper fields such as:

* `source_file`
* `record_hash`
* `raw_meta_json`
* `authors_parsed`
* `published_date`
* similar structural tokens

These are not errors in the source data, but they degrade semantic clustering if left unfiltered.

Explicit detection and removal of these clusters is a core part of the release process.

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## Dataset Structure

The release package includes filtered artifacts aligned to the retained clusters:

* `labels/` — cluster assignments
* `chunks/` — source text chunks
* `embeddings/` — embedding vectors
* `microclusters/` — original microcluster outputs (for provenance)
* `global/` — cluster summaries, BM25 artifacts, reference data

All components are consistent with the same filtered subset.

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## What This Dataset Is

* A **deterministically derived** technical clustering dataset
* A **fused patent + research corpus** with broad technical coverage
* A **quality-gated subset** of a larger clustering run
* A **reproducible artifact** tied to a single pipeline execution

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## What This Dataset Is Not

* Not manually curated
* Not hand-labeled
* Not cleaned via ad-hoc scripts
* Not a “perfect” semantic dataset
* Not independent from its pipeline (the pipeline defines it)

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## Example Cluster Themes

Cluster naming was derived deterministically from top terms. Example themes include:

* wireless communication systems
* semiconductor substrates and layers
* chemical compounds and formulations
* neural networks and data processing
* vehicle control systems
* signal processing and circuits

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## Intended Use

* Retrieval / RAG experiments
* Technical topic clustering
* Cross-domain similarity analysis
* Large-scale embedding evaluation
* Downstream filtering / refinement pipelines

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## Notes

This dataset represents the **release-grade subset** of the full run. The original unfiltered output (422 clusters) is intentionally not presented as the primary artifact.