Hi,
Its really amazing how advanced the 80’s were concerning
the early expert system wave. You already found expert systems
or fault diagnosis systems, that sorted their abduction questions
via a clairvoyant value computation. It refers to a theoretical
benchmark in decision analysis, maintenance optimization,
and information economics. I didn’t find some highly visible
Prolog implementation yet, but I might be mistaken. s(CASP)
doesn’t have it yet right? Neither does Web Prolog mention
the concept yet? LLMs use clairvoyant value as well, obviously,
when they stir the dialog. Maybe as an emergent phaenomenon?
┌─────────────────────────────────────────┐
│ HOW LLMS VALUE PERFECT INFORMATION │
└─────────────────────────────────────────┘
│
┌─────────────────────────────┴──────────────────────────┐
▼ ▼
┌─────────────────────────────────┐ ┌─────────────────────────────────┐
│ EXPLICIT SCAFFOLDING │ │ EMERGENT PHENOMENON │
├─────────────────────────────────┤ ├─────────────────────────────────┤
│ The architecture forces a VPI │ │ Autoregressive next-token │
│ calculation (e.g., using │ │ prediction mirrors human │
│ information entropy metrics). │ │ cost-benefit dialogue. │
└─────────────────────────────────┘ └─────────────────────────────────┘
One might spin the idea further and just say the valuation
doesn’t predict some physical system behaviour, but rather
reflects user preferences. See also:
Evaluating User-Agent Collaboration
https://arxiv.org/html/2608.27818v1
LLMs can be also bad clairvoyants:
LLMs Fail to Let Go
https://arxiv.org/pdf/2609.25337
Bye