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Agent-Omit trains LLM agents to omit redundant internal thoughts and observations using cold-start omission data plus omit-aware RL; includes a KL-divergence bound and Agent-Omit-8B results.
Describes Empirical-MCTS: a dual-loop MCTS that evolves meta-prompts (PE-EMP) and uses a Memory Optimization Agent to distill and reuse reasoning traces across complex problems.
A concise playbook to validate multi-node pretraining and staged inference on NVIDIA Hopper and GB200 NVL72 systems. Includes procurement and benchmark checklists and example job specs.
DeepMind's FACTS Benchmark Suite evaluates LLM factuality with claim-level tests, error taxonomies and provenance checks. Includes a 5-item quick-start checklist and decision framework.