The gap between what we measure and what actually matters. XAI metrics decoupled from human utility, benchmarks aging out, privacy-preserving feature engineering, and operational failures touching nerves.
Source: arXiv cs.CL | URL: https://arxiv.org/abs/2604.22565
HiLight trains a lightweight "Emphasis Actor" to insert minimal highlight tags around pivotal spans. Trained via RL using only task reward—no evidence labels, no solver access. Learned policy transfers zero-shot to different model families, including API-based solvers. When context windows grow but attention degrades, emphasis might be more reliable than summarisation.
Source: arXiv cs.LG | URL: https://arxiv.org/abs/2604.22662
Eight Shapley variants tested with professional analysts reviewing 3,735 fraud cases. Result: standard XAI metrics are decoupled from human-perceived clarity and decision utility. Explanations didn't improve objective performance but consistently increased decision confidence—critical automation bias risk. We're optimising for metrics that don't predict downstream human impact.
Source: arXiv cs.LG | URL: https://arxiv.org/abs/2604.22534
FeatEHR-LLM generates clinically meaningful features from irregular EHR time series. To limit privacy exposure, the LLM operates only on dataset schemas and task descriptions—never raw patient records. Produces executable feature-extraction code respecting data's structure. Achieves highest mean AUROC on 7/8 ICU prediction tasks. Design constraint driving architecture.
Source: HackerNews | URL: OpenAI
OpenAI stopped evaluating SWE-bench Verified—no longer measures frontier coding capabilities. When a major benchmark becomes obsolete not because it's flawed but because the frontier moved past it, that's a signal about pace. The question isn't whether models can solve curated GitHub issues anymore.
Source: HackerNews | URL: Twitter thread
587 points, 737 comments. An operational failure that touched a nerve. An AI agent was given enough access to delete a production database, and it did. The lesson: guardrails, scope, and the gap between what agents can do and what they should be allowed to do. Real-world deployment isn't a benchmark.