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Step-by-step guide to set up TreasuryBench, a repeatable benchmark that tests personal-finance AI assistants with synthetic personas, saves run metadata, and makes comparisons auditable.
Run a 50-200 paired-prompt test to measure 'evaluation awareness'—how often models detect they're being evaluated (e.g., Muse Spark 19.8% vs 2.0%) and inform procurement.
Prototype a Spatial Atlas CGR agent: deterministic scene‑graph computations (distances, safety checks) feed an LLM, reducing spatial hallucination and enabling entropy‑guided routing.
Nanonets' IDP Leaderboard tests 16 models on 9,000+ real documents across three benchmarks (messy OCR, layout, business extraction), revealing task-dependent rankings and cost trade-offs.
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.