2601.11255v1.pdf
Extension
Size
1 MiB
Media Type
application/pdf
SHA-256 is a widely used cryptographic hash function.
This sequence of letters and numbers can be used as a unique identifier for this file
File Hash: SHA-256
7267405c7c0f30ec2a406a7d498bb6239986687f9f99310a909bb8c4eeeaa5bf
Annotations
Structured records describing the file.
Read moreTitle
Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
Keywords
Retrieval Augmented Generation, Large Language Models, Question Answering
Version
1.7
Page Count
10
Subject
- Computing methodologies -> Natural language generation.
Producer
pikepdf 8.15.1
{
"file/base": {
"record": {
"extension": ".pdf",
"hash": "7267405c7c0f30ec2a406a7d498bb6239986687f9f99310a909bb8c4eeeaa5bf",
"media_type": "application/pdf",
"media_type_prefix": "application",
"name": "2601.11255v1.pdf",
"size": 1250460
},
"source": {
"execution_id": "53e82565-33a8-47b3-a2d9-8eabb525f376",
"id": "file/base",
"type": "Model",
"version": "1.0.0"
}
},
"file/pdf": {
"private": false,
"record": {
"creation_date": "2026-01-19T01:44:17Z",
"keywords": [
"Retrieval Augmented Generation",
"Large Language Models",
"Question Answering"
],
"modified_date": "2026-01-19T01:44:17Z",
"page_count": 10,
"producer": "pikepdf 8.15.1",
"subject": "- Computing methodologies -\u003e Natural language generation.",
"title": "Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering",
"version": "1.7"
},
"source": {
"execution_id": "32283704-5132-4a55-89aa-1f55ccd827cd",
"id": "dorsal/pdf",
"type": "Model",
"version": "1.1.0"
}
}
}
The number of unique users who have indexed this public file record
The date this file's metadata was first publicly indexed by any user.
The most recent date this file's metadata was publicly indexed by any user.
File Statistics
- Views:
- 36
- Indexed by:
- 1 user
- Indexed:
- 2026-02-17