CAAF uses recursive decomposition, formalized harnesses of invariants, and semantic gradients to enforce deterministic behavior in AI agents on commodity models.
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Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.
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Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)
CAAF uses recursive decomposition, formalized harnesses of invariants, and semantic gradients to enforce deterministic behavior in AI agents on commodity models.
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Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI
Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.