How good is a model if you let it just keep doing more and more test-time compute? Maybe the sky's the limit.
RESEARCH
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Anthropic system card PDF link shared by @arrakis_ai
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System card : https://
www-cdn.anthropic.com/d00db56fa754a1
b115b6dd7cb2e3c342ee809620.pdf
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MLA implemented using LoRA in Hugging Face Transformers
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I actually agree and just realize that I totally skipped autoencoders here. Small fun fact though: (in Hugging Face transformers) MLA is implemented using the LoRA tooling
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Lineage of techniques: from eigen decomposition to LatentMoE
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Always go back to basics: LatentMoE was probably inspired by MLA, which was inspired by LoRA, which was inspired by SVD, which was inspired by eigenvalue decomposition.
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Latent Spatial Memory for Video World Models
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Latent Spatial Memory for Video World Models pic.twitter.com/sJIpofmrmQ
— AK (@_akhaliq) 9 juin 2026Latent Spatial Memory for World Models
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Anthropic’s Claude Fable model announced with truth in advertising
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Truth in advertising: Anthropic’s latest model is called Claude Fable.
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Neurosymbolic AI dominates recent AI breakthroughs, deep learning outdated
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Every major AI success of the last 3 years uses neurosymbolic AI. Pure deep learning is history.
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Evaluation of interaction and spatial reasoning of multimodal agents
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SpatialWorld Evaluation of interaction and spatial reasoning of multimodal agents in real-world tasks
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Small AI model: 30B parameters, 3B active, open source
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Small: 30 billion parameters, 3B active.
Efficient: Benchmarks reaching 33.4 on Artificial Analysis coding index, competitive among similarly sized models.
Open Source: Apache 2.0 license so developers can experiment, test and -
Gary Marcus: LLMs useful but long road ahead per 2020 article
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LLMs are useful but we still have a long way to go. See my 2020 article The Next Decade in Arxiv, which remains a good guide to the future and offers a road map that the companies are increasingly following (without really saying so out loud).