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Prime Intellect

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Selected work

Representative Papers

MirrorCode: AI can rebuild entire programs from behavior alone

Jun 29, 2026

Current AI programming evaluations are largely confined to short, isolated tasks and lack standardized benchmarks for assessing the ability to reproduce complete software systems end-to-end. This work proposes MirrorCode, a novel long-horizon programming benchmark that introduces a behavior-based reverse-engineering paradigm: under strict black-box conditions without access to source code, AI systems must precisely reconstruct the functionality of real-world software solely from its observable behavior. The benchmark encompasses 25 complex projects spanning Unix utilities, compilers, and bioinformatics tools. Leveraging large language model–driven autonomous agents and a high-fidelity test-validation framework, the strongest model achieves a 56% success rate, including the successful reproduction of large-scale tools such as gotree (16,000 lines), demonstrating a significant leap in AI’s capacity for complex software engineering tasks.

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Arcee Trinity Large Technical Report

Feb 18, 2026

This work proposes the Trinity series of sparse Mixture-of-Experts (MoE) models—Trinity-Large, -Mini, and -Nano—to enhance parameter efficiency and training stability in large language models. The architecture integrates interleaved local-global attention, gated attention, depth-scaled Sandwich normalization, and a Sigmoid-based routing mechanism, and is trained using the Muon optimizer. A key innovation is the introduction of Soft-clamped Momentum Expert Bias Updates (SMEBU), a novel load-balancing strategy that significantly improves MoE training stability. All variants complete training without any loss spikes: Trinity-Nano and -Mini are pretrained on 10 trillion tokens, while Trinity-Large uses 17 trillion tokens. The codebase has been publicly released.

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INTELLECT-3: Technical Report

Dec 17, 2025

This work addresses the challenge of efficiently training large-scale Mixture-of-Experts (MoE) models while achieving strong generalization on mathematical reasoning, code generation, scientific problem-solving, and complex reasoning tasks. We propose a 106B-parameter MoE model with only 12B activated parameters per forward pass, trained via a custom end-to-end reinforcement learning (RL) paradigm. Our approach leverages Prime-RL—an open-source, asynchronous RL framework we introduce, supporting thousand-GPU scalability and multi-turn interactive training—integrated with a curated Verifier environment library and the GLM-4.5-Air-Base foundation model. The resulting model achieves state-of-the-art performance on multiple authoritative benchmarks at comparable parameter counts, outperforming larger frontier models despite its modest active parameter count. We fully open-source the model weights, RL training infrastructure, reproducible training recipes, and diverse verification environments—establishing a systematic, transparent, and scalable foundation for open MoE-RL research.

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Recent publications

Latest Papers

MirrorCode: AI can rebuild entire programs from behavior alone

Jun 29, 2026

Current AI programming evaluations are largely confined to short, isolated tasks and lack standardized benchmarks for assessing the ability to reproduce complete software systems end-to-end. This work proposes MirrorCode, a novel long-horizon programming benchmark that introduces a behavior-based reverse-engineering paradigm: under strict black-box conditions without access to source code, AI systems must precisely reconstruct the functionality of real-world software solely from its observable behavior. The benchmark encompasses 25 complex projects spanning Unix utilities, compilers, and bioinformatics tools. Leveraging large language model–driven autonomous agents and a high-fidelity test-validation framework, the strongest model achieves a 56% success rate, including the successful reproduction of large-scale tools such as gotree (16,000 lines), demonstrating a significant leap in AI’s capacity for complex software engineering tasks.

0 citationsRead paper

Arcee Trinity Large Technical Report

Feb 18, 2026

This work proposes the Trinity series of sparse Mixture-of-Experts (MoE) models—Trinity-Large, -Mini, and -Nano—to enhance parameter efficiency and training stability in large language models. The architecture integrates interleaved local-global attention, gated attention, depth-scaled Sandwich normalization, and a Sigmoid-based routing mechanism, and is trained using the Muon optimizer. A key innovation is the introduction of Soft-clamped Momentum Expert Bias Updates (SMEBU), a novel load-balancing strategy that significantly improves MoE training stability. All variants complete training without any loss spikes: Trinity-Nano and -Mini are pretrained on 10 trillion tokens, while Trinity-Large uses 17 trillion tokens. The codebase has been publicly released.

0 citationsRead paper

INTELLECT-3: Technical Report

Dec 17, 2025

This work addresses the challenge of efficiently training large-scale Mixture-of-Experts (MoE) models while achieving strong generalization on mathematical reasoning, code generation, scientific problem-solving, and complex reasoning tasks. We propose a 106B-parameter MoE model with only 12B activated parameters per forward pass, trained via a custom end-to-end reinforcement learning (RL) paradigm. Our approach leverages Prime-RL—an open-source, asynchronous RL framework we introduce, supporting thousand-GPU scalability and multi-turn interactive training—integrated with a curated Verifier environment library and the GLM-4.5-Air-Base foundation model. The resulting model achieves state-of-the-art performance on multiple authoritative benchmarks at comparable parameter counts, outperforming larger frontier models despite its modest active parameter count. We fully open-source the model weights, RL training infrastructure, reproducible training recipes, and diverse verification environments—establishing a systematic, transparent, and scalable foundation for open MoE-RL research.

0 citationsRead paper