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About CausalLM

A non-profit research initiative advancing the frontiers of artificial intelligence. We focus on omni-modal AI systems, efficient architectures, and synthetic data at scale.

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CausalLM Omni Research Initiative

A non-profit research initiative advancing omni-modal AI.

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Pioneering the Future of
Artificial Intelligence

Building Next-Generation
AI Systems
Omni-Modal Intelligence
Unified across all modalities
Duplex Processing
Real-time multi-stream input and output
Meet CausalLM
A non-profit research initiative advancing omni-modal AI.
We develop foundation models that understand and generate across all modalities.
TextAudioImageVideo
Independent research in efficient attention architectures, streaming systems, and synthetic data at scale

Advancing Omni-Modal

Artificial Intelligence

View research areas
Omni-Modal
Foundation Models

Developing large language models that understand and generate across text, images, audio, and video with near-zero latency.

Real-Time
Multi-Stream Processing

Building systems that process multiple concurrent streams of audio, video, and data inputs simultaneously without turn-taking constraints.

Multilingual
Knowledge Synthesis

Creating large-scale synthetic datasets grounded in factual knowledge across languages, documents, and long-context scenarios.

Million-Token
Context Windows

Scaling efficient attention mechanisms to 1M+ tokens for all-day task memory and in-context learning.

Latest Research Updates:

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A First Look at Our CausalLM Omni Model: Previewing Our Intermediate Checkpoint to Partners
Models
March 10, 2026

A First Look at Our CausalLM Omni Model: Previewing Our Intermediate Checkpoint to Partners

We are pleased to announce that an intermediate preview checkpoint of our CausalLM Omni model is now available to our strategic partners.

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Retrievatar: A Multimodal Dataset for Entity-Centric Retrieval-Augmented Generation
Datasets
December 14, 2025

Retrievatar: A Multimodal Dataset for Entity-Centric Retrieval-Augmented Generation

Retrievatar is a multimodal dataset designed to enhance the retrieval-augmented generation capabilities of vision-language models, specifically focusing on fictional anime characters and real-world celebrities.

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