10. Automating the Search for Artificial Life with Foundation Models Uses vision-language foundation models to automatically search across ALife substrates for simulations that match prompts, sustain open-ended novelty, or maximize diversity, reducing manual trial-and-error.
GENERATIVE AI
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Embodied AI: From LLMs to World Models Survey
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8. Embodied AI: From LLMs to World Models This paper surveys embodied AI through the lens of LLMs and World Models (WMs).
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Frontier AI Models Approach Expert Parity in Accuracy
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9. GDPval It shows frontier models improve roughly linearly and are nearing expert parity, with Claude Opus 4.1 preferred or tied 47.6% of the time, while GPT-5 leads in accuracy.
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ARK-V1: Lightweight Agent for Knowledge Graph Navigation
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6. ARK-V1 ARK-V1 is a lightweight agent that helps language models answer questions by actively walking through a knowledge graph instead of relying only on memorized text.
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Language Models Learn to Think and Chat Better with RL
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7. Language Models that Think, Chat Better A simple recipe, RL with Model-rewarded Thinking, makes small open models “plan first, answer second” on regular chat prompts and trains them with online RL against a preference reward.
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ATOKEN: Unified Transformer Tokenizer for Multimodal Assets
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2. ATOKEN ATOKEN introduces a single transformer tokenizer that works for images, videos, and 3D assets. https://
arxiv.org/abs/2509.14476 -
Meta FAIR Releases CWM: 32B Open-Weights Code Execution Model
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3. Code World Model Meta FAIR releases CWM, a 32B open-weights coder trained to model code execution and to act inside containers.
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Top AI Papers of the Week: LLMs, Agents, World Models
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Top AI Papers of The Week (September 22-28): – ATOKEN
– LLM-JEPA
– Code World Model
– Teaching LLMs to Plan
– Agents Research Environments
– Language Models that Think, Chat Better
– Embodied AI: From LLMs to World Models Read on for more: -
AI Transformation: Progress Potential and Socioeconomic Winners and Losers
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Thank you very much for this Julian. I appreciate the context. Helpful as I keep asking myself "So what?" and "What's next?". For me I see great potential for human progress because of the positive potential. But there will be winners and losers in the transformation this is
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AI Error Rate 30% on Trust-Critical Client Work
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Wow (and google translate is ai, but point still stands – wow). Error rate of 30% on trust-earning components of client work (like name) is a red flag.