Silen Naihin
Founder of Experiential Labs, World models for agents
Company
Silen Naihin is listed as a founder of Experiential Labs. An applied AI research lab building world models for agents. We simulate reality faster and more accurately based on the technology self-driving uses to get 99.7% reconstruction fidelity.
Founder traction evidence
Public posts and activity attributed directly to this founder.
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Experiential Labs announces its digital-twin approach
Experiential Labs announces its digital-twin approach
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Had fun presenting at ICML with @StamblerLev Til next year :)
Had fun presenting at ICML with @StamblerLev Til next year :)
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It's 90% humidity and pouring in Korea Talk about latent spaces and well shaped loss curves for hours at ICML Started some training runs ...
It's 90% humidity and pouring in Korea Talk about latent spaces and well shaped loss curves for hours at ICML Started some training runs at a WeWork, now getting kbbq A good life
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Got authoritative numbers for the speedup Longer setup time = text world model is more valuable
Got authoritative numbers for the speedup Longer setup time = text world model is more valuable
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Qwen trained a model on 10m environment interactions to turn it into a world model 🌎 They show a) training on simulated environment > r...
Qwen trained a model on 10m environment interactions to turn it into a world model 🌎 They show a) training on simulated environment > real environment training alone b) it acts as a highly effective warm-up that improves downstream performance across 7 agentic benchmarks
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Introducing environment-capture: a lightweight package to turn any benchmark into a dataset of standardized traces Shipping with 10 benc...
Introducing environment-capture: a lightweight package to turn any benchmark into a dataset of standardized traces Shipping with 10 benchmarks, 9 domains, 27,000+ real environment transitions captured 🧵
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Fable is great at creating perfect LLM judges Iteratively - run the judge on your dataset - identify the false positives and negatives -...
Fable is great at creating perfect LLM judges Iteratively - run the judge on your dataset - identify the false positives and negatives - identify why they failed - propose experiment to modify of judge (prompt, model, context)
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Research with @OKfallah and @barakwidawsky CLaaS: Continual learning as a service for sample efficient online learning: https://arxiv.or...
Research with @OKfallah and @barakwidawsky CLaaS: Continual learning as a service for sample efficient online learning: https://arxiv.org/pdf/2606.05559