Showing 1-12 of 12
Public AI leaderboard scores are easy to read but can be gamed and misrepresent real-world performance. Learn how to use them to shortlist models, then verify on your own data.
Practical guide to running Terminal-Bench-Science v0.1: reproduce the 70-task benchmark, compute resolution rates (top model ≈30%), and build a small audit-ready evaluation pipeline.
Laravel's Boost shows frontier AI models now pass all 17 Pest evals. The article urges teams to gate for idiomatic, maintainable Laravel and measure efficiency like 'correctness per token'.
Step-by-step workflow to turn Google Ads and Analytics' new AI summaries, prompt-driven visual reports, and peer benchmarks into quick checks, briefs, and decisions.
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.