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ARXIV:2605.15871 · AI ARCHITECTURES · SUBMITTED 18 MAY · 20:27 UTC · FRESHNESS STALE
ARXIV:2605.15871AI ARCHITECTURESSUBMITTED 18 MAY · 20:27 UTCFRESHNESS STALEAlberto Pepe · Chien-Yu Lin · Despoina Magka · Bilge Acun · Yannan Nellie Wu · Anton Protopopov · +2 at arXiv
AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2.
Opportunity summary
Pain AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2.
Evidence 0 refs | 4 sources | 50% coverage
Blocker Evidence unverified
AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation.
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation.
ScienceToStartup currently rates this 9.0/10 on the public viability pass. On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2. A public repository is linked, so build verification can…
AI Architectures moved forward this cycle; last verified May 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
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AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2.
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10.48550/arXiv.2605.15871AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2.
Abstract
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation. AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget. Agents evaluate million-parameter candidates, extrapolating top designs to 350M, 1B, and 3B scales. This yields 14 architectures across two families: AIRAformers (Transformer-based) and AIRAhybrids (Transformer-Mamba). Pre-trained at 1B scale, these consistently outperform Llama 3.2 and Composer-found baselines. On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2. Furthermore, AIRA-Compose finds models with highly efficient scaling frontiers: AIRAformer-C scales 54% and 71% faster than Llama 3.2 and Composer's best Transformer, while AIRAhybrid-C outscales Nemotron-2 by 23% and Composer's best hybrid by 37%. AIRA-Design tasks 20 agents with writing novel attention mechanisms for long-range dependencies and high-performing training scripts. On the Long Range Arena benchmark, agent-designed architectures reach within 2.3% and 2.6% of human state-of-the-art on document matching and text classification. On the Autoresearch benchmark, Greedy Opus 4.5 achieves 0.968 validation bits-per-byte under a fixed time budget, surpassing the published minimum. Together, these frameworks show AI agents can autonomously discover architectures and algorithmic optimizations matching or surpassing hand-designed baselines. This establishes a powerful paradigm for discovering next-generation foundation models, marking a clear step toward recursive self-improvement.
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Proof status
unverified0 refs; 4 sources; 50% coverage.
What was readable
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Dimensions overall score 9.0
PROBLEM
AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation.
METHOD
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation.
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2. A public repository is linked, so build verification can inspect implementation evidence instead of tre...
WHY NOW
AI Architectures moved forward this cycle; last verified May 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget.
Directly stated in the abstract with specific numbers.
partial
AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget.
Directly stated in the abstract with specific numbers.
partial
On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2.
Explicitly stated with precise percentages in the abstract.
partial
AIRAformer-C scales 54% and 71% faster than Llama 3.2 and Composer's best Transformer
Directly stated with specific percentages in the abstract.
partial
AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget.
Directly stated in the abstract with specific numbers.
partial
On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2.
Explicitly stated in the abstract with precise percentages.
partial
This yields 14 architectures across two families: AIRAformers (Transformer-based) and AIRAhybrids (Transformer-Mamba).
Explicitly stated in the abstract.
partial
AIRA-Design tasks 20 agents with writing novel attention mechanisms for long-range dependencies and high-performing training scripts.
Directly stated in the abstract.
partial
On the Long Range Arena benchmark, agent-designed architectures reach within 2.3% and 2.6% of human state-of-the-art on document matching and text classification.
Explicitly stated with specific percentages in the abstract.
partial
On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2.
Explicitly stated in the abstract with precise percentages.
partial
On the Autoresearch benchmark, Greedy Opus 4.5 achieves 0.968 validation bits-per-byte under a fixed time budget, surpassing the published minimum.
Directly stated with specific metric in the abstract.
partial
AIRAformer-C scales 54% and 71% faster than Llama 3.2 and Composer's best Transformer
Directly stated in the abstract with specific percentages.
partial
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AIRA-Compose and AIRA-Design autonomously generate novel AI architectures surpassing existing models like Llama 3.2.
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passport absent
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