finding
active
finding:b10-final-accuracy-94-8-1-2B10 final accuracy 94.8 ± 1.2%
Accuracy at k=16 shots for B10.
Source paper
extracted_from(2025) · Edward Yi Chang · Kaya, Zeyneb N. · Ethan Chang
Neighborhood — ranked by edge-count
Communities (3)
community
- CoT effects on generalization, multimodal QA accuracy, and AI safety alignment training.
- ScienceQA and related vision-language tasks evaluated via explicit reasoning steps, spanning 738M-parameter models with 89-95% accuracy ranges.
- Three benchmarks (B8, B9, B10) with mean accuracy and standard deviation metrics.
Questions (1)
question
- Second research question in E2
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Accuracy at k=16 shots for B8.
- Accuracy at k=16 shots for B9.
- Binary detection adjusted accuracy reaches 97.3% at layer 0 with α=5 before baseline control is appliedfinding0.791The misleadingly high result that prior paradigm would report as evidence of introspection
- Lowest threshold condition in E2; near-zero/one-shot threshold consistent with high pretraining density
- Core negative result: the binary detection paradigm cannot distinguish genuine introspection from uniform output bias
- Strength comparison accuracy averages 47% at layers 15-30, indistinguishable from 50% chancefinding0.735Shows collapse of introspective capability at later layers in the strength comparison task
- Transition width (k90 – k10) for B10.
- High cosine similarity for Gemma3 steering vectors suggests strong linear reflection structure.