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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Small models matter more now. If Haiku 5.5 trades quality evenly with larger alternatives while staying cheap and fast, that changes ROI math for anyone building latency-sensitive features or running at scale. The real question is which workloads actually need the larger models anymore. Run your benchmarks against this before upgrading to the next tier up.
This is immediately useful. The method is simple (one forward pass, one dot product), requires no generation, and works across model families. For builders using small LLMs (1-8B), this is a better hallucination detector than existing approaches. You can train it on fifty examples and get strong AUROC on factual tasks. For inference, the overhead is negligible. Test this on your domain and consider swapping it in for probability-based baselines. The directions are interpretable too—correctness is a learnable signal.