What is recorded here?
Scaled a hybrid linear/softmax attention expert-model architecture with optimized parallel execution and communication overlap, releasing text and vision-language models for long-context tasks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Integrated system builds on prior Lightning Attention and MoE methods. Synthetic retrieval or context extrapolation does not imply reliable reasoning over all four-million-token inputs. Text/VL and different context settings form one contribution.
Read the original work
- MiniMax-01: Scaling Foundation Models with Lightning Attention
- MiniMax: Lightning-Attention long-context expert system — official company record
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities. The recorded author list is incomplete.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
MiniMax: Lightning-Attention long-context expert system
Scaled a hybrid linear/softmax attention expert-model architecture with optimized parallel execution and communication overlap, releasing text and vision-language models for long-context tasks.
Scientific scope
Integrated system builds on prior Lightning Attention and MoE methods. Synthetic retrieval or context extrapolation does not imply reliable reasoning over all four-million-token inputs. Text/VL and different context settings form one contribution.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.