claim

active

claim:the-additive-form-s-d-dr-log-k-is-parsimonious-and-aligns-with-log-odds-intuition

The additive form S = ρd - dr - log k is parsimonious and aligns with log-odds intuition

Justification for the linear combination

Source paper

extracted_from

The Guanyin Protocol: A Framework for Immediately Establishing an Understanding of Both Causality and Compassion in LLM Systems Using Semantic Anchoring

(2025) · Edward Yi Chang · Kaya, Zeyneb N. · Ethan Chang

Neighborhood — ranked by edge-count

Communities (3)

community

Few-shot anchoring & latent structure
members_of
How minimal examples disambiguate and recruit latent arithmetic/reasoning interpretations in LLMs
Anchoring score threshold theory
members_of
Predictive framework using S = ρd − dr − log k to explain few-shot performance phase transitions.
Anchoring score S for few-shot learning transitions
members_of
Predictive metric S = ρd - dr - log k quantifies when LLM behavior sharply transitions across few-shot, SFT, and CoT settings via layer-wise calibration.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

S = ρd - dr - log k is a predictive correlate of when few-shot behavior flipsclaim0.818
Claim that S predicts threshold midpoints across different bases, tasks, and models
S = ρd − dr − log k is a predictive correlate of anchoring success across few-shot, SFT, and CoT.claim0.800
UCCT's practical utility claim.
Linear combination ρd – dr – log k yields a predictive correlate of success.hypothesis0.793
Implicit hypothesis behind S form.
S = ρd - dr - log k predicts shot midpoints across different bases, tasks, and modelsclaim0.783
Predictive practical utility claim.
(ii) does the anchoring score S = ρd − dr − log k consistently correlate with performance across anchoring methods?question0.757
Second research question in E2
S is a predictive correlate calibrated on dev sets, not an absolute measureclaim0.740
Clarifies nature of S.
Under reward shaping (G=100, H=-100, F=0), Active Inference scored 99.52, Bayesian RL 99.77, Q-learning 95.56, with nearly identical behavior between belief-based agents.finding0.738
Table 2, row 3, showing equivalence when prior preferences match rewards.
Anchoring strength S = ρd - dr - log kconcept0.735
The calibrated score measuring how effectively anchors bind target patterns; a predictive correlate of success.