README.md

---
license: cc-by-4.0
language:
  - en
pretty_name: Speculative Decoding Papers
size_categories:
  - n<1K
task_categories:
  - text-classification
  - text-generation
tags:
  - arxiv
  - semantic-scholar
  - papers
  - research
  - machine-learning
configs:
  - config_name: default
    data_files: data.jsonl
---

# Speculative Decoding Papers — FineSet

A research-paper dataset on **Speculative Decoding Papers**, assembled, deduplicated, and quality-scored by
[FineSet](https://fineset.io) from arXiv and Semantic Scholar.

> **📸 This is a dated snapshot — generated 2026-06-19.**
> It is not auto-updated. Research on **Speculative Decoding Papers** moves fast — new papers land on arXiv every
> week. Want this same dataset **refreshed daily**, on a topic *you* choose? See the bottom. ↓

## Why this dataset

- **Quality-scored:** `quality_score` float (0–1), blends citations with recency + code/venue signals — filter out the noise
- **Papers with code:** 134 flagged via `has_code` — find reproducible work fast
- **Deduplicated:** arXiv + Semantic Scholar cross-referenced, duplicate records merged
- **Clean JSONL:** 485 records, one per line, normalized fields — no encoding garbage

## Dataset details

- **Records:** 485
- **Date range:** 2022–2026
- **Snapshot date:** 2026-06-19 (frozen — see note above)
- **Sources:** arXiv, Semantic Scholar (cross-referenced, duplicates merged)
- **arXiv categories:** cs.LG, cs.CL
- **Quality scoring:** citations + recency + code/venue blend, 0–1 (p50=0.35, p90=0.61)
- **Format:** JSONL, one record per line

## Fields

| Field | Type | Description |
|---|---|---|
| id | string | Deterministic SHA256 record id |
| sources | list | Which sources contributed (`arxiv`, `semantic_scholar`) |
| title | string | Paper title |
| abstract | string | Full abstract |
| authors | list | Author names |
| categories | list | arXiv category codes |
| fields_of_study | list | Semantic Scholar field tags |
| published_date | string | ISO 8601 date |
| url | string | arXiv abstract URL |
| pdf_url | string\|null | Open-access PDF if available |
| arxiv_id | string\|null | arXiv identifier |
| doi | string\|null | DOI if available |
| citation_count | int | Citation count (Semantic Scholar) |
| influential_citation_count | int | Influential citations (Semantic Scholar) |
| has_code | bool | Code repo detected in the arXiv comment |
| code_url | string\|null | GitHub URL if detected |
| venue | string\|null | Publication venue |
| quality_score | float | 0–1, blended (citations + recency + code/venue) |

## Quality score methodology

`quality_score = max(impact, freshness)`, clamped to [0, 1], where:

- **impact** = `max( log10(citations+1)/4 , log10(influential_citations+1)/2 )` —
  realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
- **freshness** = `recency × (0.35 + 0.30·has_code + 0.20·has_venue)` — a baseline
  for recent papers (so a strong paper published this week isn't scored 0 just for
  lacking citations), where `recency` is 1.0 for papers ≤60 days old and decays
  linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old
uncited papers score ~0. Useful for filtering training data by quality, not just age.

## 👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this
**refreshed every day** on whatever topic you describe — new papers in, dedup and quality
scoring automatic, export as JSONL/Parquet or push straight to the Hub.

**Tell me the topic you'd want and I'll run the pipeline on it** — open a discussion on this
dataset, it's free and it's how I decide what to build next.

→ [fineset.io](https://fineset.io) — describe what you want to train on, get a dataset.
Early-access waitlist open (referral skip available).