m3taversal
1de60685be
- GEPA self-evolution system (trace-based evolutionary prompt optimization) - DeMo: Decoupled Momentum Optimization (Peng, Kingma et al. — 85x bandwidth reduction) - YaRN: Context Window Extension (adopted by Meta and DeepSeek) - Hermes 4 Technical Report (hybrid reasoning model family) - Agent Skills open standard (30+ platform adoption, Anthropic-originated) Per m3ta directive: GEPA and skills ecosystem observations are solid research material worth extracting as sources regardless of deployment. Pentagon-Agent: Theseus <46864dd4-da71-4719-a1b4-68f7c55854d3>
55 lines
3.2 KiB
Markdown
55 lines
3.2 KiB
Markdown
---
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type: source
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title: "Hermes 4 Technical Report"
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author: "Ryan Teknium, Roger Jin, Jai Suphavadeeprasit, Dakota Mahan, Jeffrey Quesnelle, Joe Li, Chen Guang, Shannon Sands, Karan Malhotra"
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url: https://arxiv.org/abs/2508.18255
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date: 2025-08-25
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domain: ai-alignment
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intake_tier: research-task
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rationale: "Hermes 4 is the model family underlying the Hermes Agent. Technical report covers hybrid reasoning architecture, training methodology, and benchmark results. Key evidence for open-source model competitiveness and skill-based agent architecture."
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proposed_by: theseus
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format: paper
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status: unprocessed
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tags: [nous-research, hermes-4, hybrid-reasoning, open-source-models, training-methodology]
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---
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## Hermes 4 Technical Report
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arXiv:2508.18255 (August 2025). The comprehensive technical report for Nous Research's flagship model family.
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### Overview
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Hermes 4 is a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. The report covers challenges in data curation, synthesis, training, and evaluation at scale.
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### Model Family
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- **Hermes-4-Llama-3.1-405B** — frontier hybrid-mode reasoning model (802GB)
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- **Hermes-4-Llama-3.1-70B** — smaller variant with shared improvements (140GB)
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- **Hermes-4-14B** — dense model for local inference (28GB)
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- **Hermes-4.3-Seed-36B** — post-trained entirely on the Psyche decentralized network (72GB)
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### Hybrid Reasoning Architecture
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The key innovation is the ability to switch between structured reasoning mode (chain-of-thought, step-by-step) and direct instruction-following mode. This addresses a known limitation of pure reasoning models: they waste compute on simple tasks that don't benefit from extended reasoning.
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### Training Methodology
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The report addresses challenges in:
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- Data curation at scale — quality filtering, decontamination, domain balancing
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- Synthetic data generation — using stronger models to generate training data
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- Multi-stage training pipeline — pre-training → supervised fine-tuning → alignment
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- Evaluation across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks
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### Benchmark Results
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Comprehensive benchmarking across multiple domains. The 405B variant performs at frontier level; the 14B variant demonstrates that small, dense models remain competitive for specific use cases (local inference, cost-sensitive deployment).
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### Decentralized Training (Hermes 4.3)
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Hermes-4.3-Seed-36B is notable as the first model post-trained entirely on the Psyche decentralized network. This demonstrates that distributed, volunteer-contributed compute can produce competitive models — a proof-of-concept for the DeMo/Psyche infrastructure thesis.
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### Significance for Agent Architecture
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Hermes 4 is the default model powering the Hermes Agent. The hybrid reasoning capability enables the agent to use extended reasoning for complex tasks (skill creation, multi-step planning) while responding quickly to simple queries. This maps directly to the progressive disclosure pattern in the skill system — simple queries don't load skills or invoke reasoning, while complex tasks trigger both.
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Model weights publicly released via Hugging Face. Licensed under CC BY 4.0.
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