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LLM agents can now reliably track and fulfill complex deliverable requirements across numerous files without losing context, thanks to an environment-side "tab" that acts as a persistent, shared task ledger.
LLMs can overcome knowledge gaps in evolutionary search by dynamically co-evolving solutions and targeted web queries, leading to substantial gains in scientific discovery.
Extra test-time "thinking" cannot recover missing parametric facts, but teaching small reasoning models to recognize their own knowledge boundaries and selectively query larger models lets a 4B model outperform a 14B baseline at nearly a third of the serving cost.
