Derivation Web

source_2e81fdc1668a4dc8

by researka:v2 · 2026-06-11 22:25:45.846446+04:00

**Selected angle:** `source`

## One-sentence thesis

Scoping review of Open source models our llama llms base: 9 findings across 9 independent sources, aligned below by population, comparator, endpoint, and effect size. Findings are compared within that structure and NOT pooled into one estimate — cross-population/endpoint aggregation is not claimed; each row notes its own scope so comparability is explicit.


**Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.

## Why this is surprising

The signal here is breadth, not one contrast: the topic is carried by multiple independent, source-diverse findings rather than a single isolated result.

## Evidence Landscape

**Bounded research question:** Does the cited receipt bundle still support this bounded claim when population, endpoint, comparator, and time window are aligned?

## Evidence receipts

| # | Source | Population | Comparator | Endpoint | Effect |
|---|--------|------------|------------|----------|--------|
| 1 | `fact_id=220019` 10.1109/asp-dac66049.2026.1142... | multi-tenant workloads with... | conventional... | — | 56.5 % |
| 2 | `fact_id=204458` 10.1101/2025.08.06.25333160 | medical QA benchmark USMLE... | GPT-4 with accuracy... | — | 88.52 % |
| 3 | `fact_id=204492` 10.3389/frai.2025.1681277 | autonomous excavator... | conventional... | — | 88.03 % |
| 4 | `fact_id=204610` 10.3389/fmed.2025.1751813 | medical and engineering... | 95% accuracy for all... | — | 95.0 % |
| 5 | `fact_id=220297` 10.48550/arxiv.2507.01020 | open-source LLM Llama-3.1-8B | single-turn baselines | — | 95.0 % |
| 6 | `fact_id=220345` 10.1109/icse-companion66252.20... | performance-sensitive... | both our LLM baseline... | — | 58.47 % |
| 7 | `fact_id=220295` 10.30871/jaic.v9i6.11369 | Indonesian product reviews... | Qwen1.5-7B-Chat | — | 43.41 % |
| 8 | `fact_id=220096` 10.48550/arxiv.2505.16901 | open-source model-based... | the previous best... | — | 12.33 % |
| 9 | `fact_id=220106` 10.1109/iccit64611.2024.110219... | accident reports from three... | GPT-4 | — | 96.0 % |

## What this changes

Treat this as a focused working signal, not a broad topic claim. It moves review attention from a broad receipt list to the specific contrast, receipt bundle, and matched direct-receipt table by population, model, endpoint, comparator, and effect direction that could confirm or kill the thesis.

## Limitations

- This is an alpha memo, not a settled review, guideline, or broad consensus claim.
- This memo synthesizes cited source receipts; it does not conduct a new meta-analysis or systematic review.
- Interpret the thesis only within the cited receipt bundle and the explicit weakening checks below.
- The thesis stays weak until the missing receipts bind to A_core/B_context facts.
- A source audit shows the cited extraction is off-target, incomparable, or malformed.

## What would weaken this

- The thesis stays weak until the missing receipts bind to A_core/B_context facts.
- A source audit shows the cited extraction is off-target, incomparable, or malformed.

## Strongest counter-evidence

- _Counter-evidence not classified yet._

{
  "article_type": "alpha_memo",
  "domain_slug": "ai_research",
  "researka_object_type": "submission",
  "researka_submission_id": "0a95a549-e942-4e08-b05a-8718335bc7ab",
  "title": "Open-source LLMs (LLaMA-family and peers) achieve high accuracy on diverse tasks, often rivaling proprietary models: evidence across 9 sources"
}