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MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
LLMs can pinpoint semantic bugs that traditional methods miss, thanks to a new framework that turns their free-form reasoning into verifiable, executable code constraints.
AI coding agents often produce functionally correct code that's a maintainability nightmare, failing structural oracles in 13% of cases even when passing all functional tests.
Learning-based spatial reasoning gets a boost by using geometry not just for refinement, but as a critical arbiter that validates and absorbs learned geometric observations.
