Models Training Models: The Promise and Peril of Synthetic Data
Microsoft's Phi-4 trained on more than 50% synthetic data and beat GPT-4o on graduate science benchmarks. The old rules about training data are changing fast.
Technical AI research, explained clearly for researchers, builders, and anyone trying to understand what actually matters.
Microsoft's Phi-4 trained on more than 50% synthetic data and beat GPT-4o on graduate science benchmarks. The old rules about training data are changing fast.
Some enterprise agent projects fail because autonomy was added where a bounded single-call LLM design would have delivered cleaner behavior and lower operational risk.
Open source AI used to be the cheaper substitute. In 2026, that is too small.
A paper from Tran and Kiela tested 28 multi-agent configurations across four architectures: Sequential, Parallel, Debate, and Ensemble. Every single one...
The reactive/deliberative/hybrid taxonomy is broken. The 2026 classification that actually helps: coding agents, research agents, computer-use agents, task agents, multi-agent orchestrators, and self-improving agents.
Vector databases power most retrieval-augmented generation systems in production today. They're fast, simple, and good enough for single-hop lookups...
Every frontier lab now ships models that see, hear, and read. The assumption is that more modalities mean more capable agents. The benchmarks tell a...
Token prices dropped 280x over two years. Enterprise AI budgets rose 320% in the same period. That's not a paradox. It's what happens when agentic...
GitHub reports that 46% of all new code is now AI-generated. Ninety-two percent of US developers use AI coding tools daily. Claude Code hit $2.5 billion...
Gartner predicts that [40% of enterprise...
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