PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $10K - $14K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (16)

[1]
Enhancing Training Data Attribution with Representational Optimization
2025Weiwei Sun, Haokun Liu et al.
[2]
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens
2025Jiacheng Liu, Taylor Blanton et al.
[3]
Deciphering the Interplay of Parametric and Non-parametric Memory in Retrieval-augmented Language Models
2024M. Farahani, Richard Johansson
[4]
Do Large Language Models Latently Perform Multi-Hop Reasoning?
2024Sohee Yang, E. Gribovskaya et al.
[5]
The Power of Noise: Redefining Retrieval for RAG Systems
2024Florin Cuconasu, Giovanni Trappolini et al.
[6]
FASTTRACK: Reliable Fact Tracing via Clustering and LLM-Powered Evidence Validation
2024Si Chen, Feiyang Kang et al.
[7]
Lost in the Middle: How Language Models Use Long Contexts
2023Nelson F. Liu, Kevin Lin et al.
[8]
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
2023Stella Biderman, Hailey Schoelkopf et al.
[9]
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
2022Alex Troy Mallen, Akari Asai et al.
[10]
Large Language Models Struggle to Learn Long-Tail Knowledge
2022Nikhil Kandpal, H. Deng et al.
[11]
Towards Tracing Knowledge in Language Models Back to the Training Data
2022Ekin Akyürek, Tolga Bolukbasi et al.
[12]
Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations
2021Jimmy J. Lin, Xueguang Ma et al.
[13]
How Much Knowledge Can You Pack into the Parameters of a Language Model?
2020Adam Roberts, Colin Raffel et al.
[14]
How Can We Know What Language Models Know?
2019Zhengbao Jiang, Frank F. Xu et al.
[15]
Anserini: Enabling the Use of Lucene for Information Retrieval Research
2017Peilin Yang, Hui Fang et al.
[16]
SQuAD: 100,000+ Questions for Machine Comprehension of Text
2016Pranav Rajpurkar, Jian Zhang et al.

Founder's Pitch

"NanoKnow provides a benchmark dataset to uncover and analyze the knowledge encoding in large language models."

LLM EvaluationScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

1/4 signals

2.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/23/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.