---
title: "Slides: Minimax M2: Building the #1 Open Model – Olive Song, MiniMax"
category: "slides"
video_id: "lY1iFbDPRlw"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Minimax M2: Building the #1 Open Model – Olive Song, MiniMax

## Source Video
[Minimax M2: Building the #1 Open Model – Olive Song, MiniMax](https://www.youtube.com/watch?v=lY1iFbDPRlw)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/lY1iFbDPRlw/slide-001.jpg]]

OCR text:

> MiniMax-M2:
> oo Agentic Model for Real
> Dev Experience
> wT
> Open-Weight, Fast, Efficient
> & Engineered for Real-World
> Coding
> a ; PN |
> 
> mun DT are Ono
> 
> withw Senior Research Engineer
> St a De (RL & Eval Lead)
> 
> Google DeepMind

![[assets/slides/lY1iFbDPRlw/slide-002.jpg]]

OCR text:

> MiniMax: Global Leading Independent Model Lab + App Developer
> ny - 7
> : Models:
> . 5 Musi 7 MiniMa
> Excel in all oo ; Speech 20 . MCP
> modalities P , Server
> pecesnee nn a ois oe eee
> Es a = sem
> j Preheat a
> Al-native Apps: : i . we
> Agent] -toffe video «gent tonics
> 150M users “a (a ‘9 I ~N
> ti! globally 4
> alls MENBHAX Intetgence +. th Everyene
> A ee MINIMAX M2
> Goose be BAVREE «OLIVE SONG / senior research Engineer ffi) MINIMAX

![[assets/slides/lY1iFbDPRlw/slide-003.jpg]]

OCR text:

> MiniMax-M2:
> 7 Open-Weight. Coding-First. Best-in-Class.
> e ~l0Bactivated e Agentic-by-design e Fast,
> parameters, for coding and cost-efficient, and
> open-weight workplace tasks ready to scale
> al rememtae Intetgence --tth Everyene
> MINIMAX M2
> eee ee OLIVE SONG / Senior Research Engineer afifin MINIMAX

![[assets/slides/lY1iFbDPRlw/slide-004.jpg]]

OCR text:

> Intelligence with Everyone
> AIE

![[assets/slides/lY1iFbDPRlw/slide-005.jpg]]
![[assets/slides/lY1iFbDPRlw/slide-006.jpg]]

OCR text:

> Interleaved Thinking Enables...
> © pecs wo toctng ne eee ean —: aD
> ste Oo cua Sos
> © Rie Bek ci Ria anc Om” «> Eb
> 1. Adaptation to Environment Noise — _—
> . | ah J nf
> 2. Focus on Long-Horizon Tasks nt rn NAY fe YN
> Saat ‘es ae
> yon ¢ ya 5 i ae
> | kt a Poy)
> ¥ ye ae
> allfo MINIMAX Intelligence +-ith Everyenc

![[assets/slides/lY1iFbDPRlw/slide-007.jpg]]

OCR text:

> AIE

![[assets/slides/lY1iFbDPRlw/slide-008.jpg]]

OCR text:

> M2 Scales and Collaborate in Multi-Agent Systems
> Research Agent Web Development Agent Report Agent
> ee Pe
> | oS
> ee
> Small & Cost-effective to run long agentic tasks
> oe
> wire
> all ‘} MINIMAX MiniMax Agent Intelligence “ith Evoryene

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.