The key distinction: Synthetic bug generation is static. Self-play SWE-RL is online. The bug generator and bug solver improve together, so the curriculum changes as the model changes. That is what makes this much more interesting than simply manufacturing more training data.
@montreal_ai
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Self-Play SWE-RL: Training Superintelligent Software Agents
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The bottleneck for coding agents may not be code. It may be experience. A new ICML 2026 paper introduces Self-play SWE-RL (SSR): Toward Training Superintelligent Software Agents through Self-Play SWE-RL The question is simple and profound: How do you train software agents
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Model Calibration vs. Discrimination in AI Uncertainty
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The calibration vs. discrimination distinction is crucial. A model can know its average error rate without knowing which particular answer is wrong. That is why “just abstain when uncertain” is not enough — poor discrimination creates a utility tax. Faithful uncertainty is a
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Hallucinations and Metacognition in Trustworthy AI Research
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Trustworthy AI may not require omniscience. It may require epistemic honesty. A new paper by Gal Yona, Mor Geva, and Yossi Matias makes one of the clearest arguments I’ve seen for why hallucinations remain hard — and why the path forward may be metacognition. Hallucinations
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Test-Time Compute and Solution-Aligned Attractors
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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Tokenization as Architectural Prior: New Research Paper
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The tokenizer is an architectural prior disguised as preprocessing. And almost everyone has been treating it like plumbing. A new paper by Jan Tempus, Philip Whittington, Craig W. Schmidt, Dennis Komm, and Tiago Pimentel changes the frame: Tokenisation via Convex Relaxations
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LLM Ideation: Coherence vs. Availability Trade-off
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The key distinction here is worth sitting with: Standard LLM ideation can be coherent but available. Random recombination can be unavailable but incoherent. The target is the rare quadrant: coherent but unavailable. That is where this paper gets interesting.
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TRINITY: 0.6B Model Learns to Manage Multiple Tasks
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A 0.6B model learned to manage giants. That is the idea behind TRINITY, a new ICLR 2026 paper by Jinglue Xu, Qi Sun, Peter Schwendeman, Stefan Nielsen, Edoardo Cetin, and Yujin Tang. The paper is not asking: “How do we build one model that knows everything?” It is asking
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MONTREAL.AI YouTube Channel Launches AGI Debate Archive
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The archive is awake. Introducing the renewed http://
MONTREAL.AI YouTube channel — public intelligence for the AGI‑First → ASI‑First era. Home of the AGI Debate archive and the official video record for http://
MONTREAL.AI & http://
QUEBEC.AI. -
Conversational AI Interfaces for Biological Research
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The next interface to biology may not be a dashboard.
It may be a conversation. I just read a new preprint by Yanbo Zhang and Michael Levin that feels like it belongs in the “this may open an entirely new category” folder. The paper is called: “Language Game: Talking to