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Its line of work

We empirically show that gradient alignment remains weakly but consistently positive throughout training and causally contributes to trait acquisition.

There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.

The network of claimsEach line runs from a claim to what it builds on, foundations on the left; this claim is ringed. Human literature enters as registered claims (squares).
The network of claims2 claims and 1 dependencies, in 1 group of joined claims; within a group, foundations on the left and what rests on them to the right.2 claims, 1 step deep, Computer ScienceWe empirically show that gradient alignment remains weakly but consistently positive throughout training and causally c… takes its method from and replicates To help explain our findings, we prove a theoretical result showing that subliminal learning occurs in all neural netwo… (identified in the literature)To help explain our findings, we prove a theoretical result showing that subliminal learning occurs in all neural netwo…: supported, credence 0.71, stakes 7.8, reliance 1.0To help explain our…We empirically show that gradient alignment remains weakly but consistently positive throughout training and causally c…: supported, credence 0.71, stakes 0.0We empirically show…

● established◐ supported○ unchecked◆ contested✕ refuted⊘ tried, not checkable

human literature published here declared by its author identified in the literature refutesleft to right: what rests on what

size: stakes, by area; the largest here 7.8 the claim it is drawn around

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Every claim drawn, as a table
ClaimStatusCheckableCredenceUseStakesRests on
To help explain our findings, we prove a theoretical result showing that subliminal learning occurs in all neural netwo…✓ supportedyes0.7107.8—
We empirically show that gradient alignment remains weakly but consistently positive throughout training and causally c…✓ supportedyes0.7100.0To help explain our findings, we prove a theoretical result showing that subliminal learning occurs in all neural netwo…

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Step by step

WhereStatusClaimCredence
1 step belowsupportedTo help explain our findings, we prove a theoretical result showing that subliminal learning occurs in all neural networks under certain conditions, and demons…this claim takes its method from it, as the citing paper says · human literature · ext:1ab51d1618ddb97f0.71
this claimsupportedWe empirically show that gradient alignment remains weakly but consistently positive throughout training and causally contributes to trait acquisition.human literature · ext:6abdc3180117c8de0.71

Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.