You compartmentalize your emotions, why not your AI?
Emotionally partitioned AI agents are single AI systems that switch between different personas for regulators, customers, and internal teams, adjusting tone, priorities, and emotional style to match each audience. This
MACHINE LEARNING
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Emotionally partitioned AI agents switch personas for different audiences
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SAM Audio: Unified Model for Sound Isolation from Complex Mixtures
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🔉 Introducing SAM Audio, the first unified model that isolates any sound from complex audio mixtures using text, visual, or span prompts.
— AI at Meta (@AIatMeta) 16 décembre 2025
We’re sharing SAM Audio with the community, along with a perception encoder model, benchmarks and research papers, to empower others to… pic.twitter.com/FuMJyULmJRIntroducing SAM Audio, the first unified model that isolates any sound from complex audio mixtures using text, visual, or span prompts. We’re sharing SAM Audio with the community, along with a perception encoder model, benchmarks and research papers, to empower others to
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SAM Audio: Advanced Audio Separation Technology Outperforms Previous Models
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SAM Audio represents a significant advancement in audio separation technology, outperforming previous models across a wide range of benchmarks and tasks.
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Video Models as World Simulators: The Next Technological Frontier
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Universal World Simulator Soon, everyone will have access to their own world simulator. This will be the most important technological development of our time. models trained at sufficient scale become world models. To predict the next frame, a video model must learn how
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GPT-5 Autonomously Optimizes Molecular Cloning Laboratory Protocols
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We’re also testing our models on real world lab experience. We worked with Red Queen Bio to test models to optimize protocols in the lab. GPT-5 proposed, ran (via a controlled framework), and iterated on experiments — increasing a standard molecular cloning protocol's
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Advanced AI Benchmarks Reveal Model Progress and Development Needs
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Combining harder benchmarks like FrontierScience with real-world lab evaluations gives us a clearer map of where models are effective today and where further development is needed. We see strong early promise, alongside well-defined limitations, and we’ll continue iterating
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Weight Decay: Soft Pruning of Noisy Features in Neural Networks
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Weight decay is usually presented as “encouraging simpler solutions”, but I tend to think that the real benefit is the soft pruning of noisy / unhelpful features. Without decay, a weight can random-walk to a large value even if the input is completely random. Momentum and
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SWE-Playground: Synthetic Data Generation for Versatile Coding Agents
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There are many good training methods for improving agents on SWE-bench: SWE-Gym, SWE-Smith, R2E-Gym. But what about broader software engineering tasks? In SWE-Playground, we introduce a new, more diverse synthetic data generation strategy to train divers software agents. Yiqi Zhu (@StephenZhu0218) Introducing SWE-Playground: A fully automated pipeline that generates synthetic environments to train versatile coding agents. 🤖✨ Training software engineering agents often relies on existing resources like GitHub issues and focuses on solving SWE-bench style issue resolution tasks. While this has driven incredible progress, real-world engineering involves a wider spectrum of tasks —from designing new libraries to writing reproduction scripts. 🌐 Rather than mining existing repositories, SWE-Playground synthetically generates projects, tasks, and verifiable unit tests from scratch. This approach offers two exciting opportunities: 1️⃣ Flexibility: We can generate tasks without being constrained by the availability or structure of existing open-source data. 2️⃣ Versatility: We extend training beyond Issue Resolution to include Issue Reproduction and Library Generation from Scratch. The results? 🚀 Our agents achieve strong performance across SWE-bench Verified, SWT-Bench, and Commit-0, demonstrating high data efficiency compared to baselines trained on larger datasets. Huge thanks to my amazing collaborators @apurvasgandhi and @gneubig for their incredible efforts on bringing this work to life! 👇 🧵 A deep dive into how we build versatile agents synthetically. Paper: arxiv.org/pdf/2512.12216 Project Page: neulab.github.io/SWE-Playgro… Code: github.com/neulab/SWE-Playgr… Data & Models: huggingface.co/collections/S… — https://nitter.net/StephenZhu0218/status/2000754124019683469#m
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Frontier Models Struggle with Abstract Reasoning Tasks
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NEW research on abstract reasoning. Frontier models like GPT-5 and Grok 4 still can't do what humans find trivially easy: infer transformation rules from a handful of examples. The default approach to solving ARC-AGI (the leading benchmark for abstract reasoning) treats these
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Implementing Tree of Thoughts Reasoning for AI Models
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3. Tree of Thoughts (ToT)
— God of Prompt (@godofprompt) 16 décembre 2025
Linear thinking gets linear results. ToT explores multiple reasoning paths simultaneously.
Instead of A→B→C, the model explores A→B→C, A→D→E, and A→F→G, then picks the best path.
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Explore 3 different approaches to solve: [problem]… pic.twitter.com/oVbP6OECvk3. Tree of Thoughts (ToT) Linear thinking gets linear results. ToT explores multiple reasoning paths simultaneously. Instead of A→B→C, the model explores A→B→C, A→D→E, and A→F→G, then picks the best path. Template: Explore 3 different approaches to solve: [problem]