# Inline Hardware KV-Cache Compression for Long-Context Transformer Inference: An Architectural Case for a Memory-Path Compression Engine

> As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically prove 75% of the Mizar theorems when the automated provers are helped by using only the premises used in the human-written ...

## Metadata
- Authors: Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra, Kaliszyk, Cezary, Olšák, Mirek, Piotrowski, Bartosz, Schulz, Stephan, Suda, Martin, Urban, Josef
- Journal: DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
- Published: 2023-01-01
- DOI: https://doi.org/10.4230/lipics.itp.2023.19
- Citations: 77,016
- Source: OpenAlex
- Access: Open Access

## Technology Hub
- Hub: Large Language Models
- Discipline: Computer Science / AI
- Hub URL: https://science-database.com/technology/large-language-models
- Hub llms.txt: https://science-database.com/technology/large-language-models/llms.txt

## Abstract
As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically prove 75% of the Mizar theorems when the automated provers are helped by using only the premises used in the human-written Mizar proofs. We describe the methods and large-scale experiments leading to these results. This includes in particular the E and Vampire provers, their ENIGMA and Deepire learning modifications, a number of learning-based premise selection methods, and the incremental loop that interleaves growing a corpus of millions of ATP proofs with training increasingly strong AI/TP systems on them. We also present a selection of Mizar problems that were proved automatically.

## Links
- DOI: https://doi.org/10.4230/lipics.itp.2023.19
- OpenAlex: https://openalex.org/W4385245566
- PDF: https://drops.dagstuhl.de/storage/00lipics/lipics-vol268-itp2023/LIPIcs.ITP.2023.19/LIPIcs.ITP.2023.19.pdf
- JSON API: https://science-database.com/api/v1/technology/large-language-models

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Paper ID: oa-W4385245566 | Hub: large-language-models