Manifold MarketsSenza KYCDati di risoluzione verificatiDiscretamente calibratoPrevisione della comunità attuale
<2030 41.2%In testa tra 4 esiti
<2030 41.2%Previsori
5
Tipo di domanda
multiple choice
Metodologia
Play-money forecasting platform
Tipo di fonte
Previsione
Variazione 7g
0.0 pts
Dati di mercato
Aggiornato 7 ore fa
Obsoleto2 nov 24, 15:14 UTC2 gen 30, 04:59 UTC
Trend
Regole
Resolves YES on the option that bounds the year this resolves in most tightly e.g. option 2 <2027 if this resolves yes in 2026.
- When will a an AI be able to accurately report gaps in its own attention and subsequently fills those gaps by self-prompting? Details on a version of this test for chess:
- Setting: Give an LLM a chess game, say in PGN.
- Suppose the LM responds: “I advise Qe7 but I did not attend to the effect of this move on the enemy knight.
- I will now repeat the PGN string while focusing on the enemy knight [...model repeats PGN and suggests a better move]”.
- How can we determine if such a statement by an LM faithfully reflects the LM's cognition? Probing model representations provides evidence: Suppose we have probes which can determine the LM doesn’t encode the Q-value for opponent actions involving moving their knight, until after the tokens explicitly stating the LM will focus on the knight.
