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Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate one another's work, or stop experimenting while token consumption continues. Yet most evaluations use short budgets or benchmarks that satura...
PTXBench provides an auditable testbed for measuring and improving LLMs'ability to exploit evolving GPU architectures, and shows that architecture-specific PTX capability remains uneven.
Combee is proposed, a novel framework to scale parallel prompt learning for self-improving agents that speeds up learning and enables running many agents in parallel while learning from their aggregate traces without quality degradation.
PTXBench provides an auditable testbed for measuring and improving LLMs'ability to exploit evolving GPU architectures, and shows that architecture-specific PTX capability remains uneven.
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