How much can we actually trust the current wave of agentic systems? This week pulls together three answers. LiteResearcher introduces a scalable agentic reinforcement learning framework that reportedly outperforms Claude 4.5 Sonnet on the GAIA and Xbench deep-research benchmarks, suggesting real-world search competence can be trained rather than hand-crafted. A second study documents diversity collapse in multi-agent LLM ideation, showing that structural coupling between agents narrows the solution space instead of widening it. The third paper probes reliability on OSWorld, finding that computer-use agents often fail on repeated runs of identical tasks, a sobering note on reproducibility.