claim

active

claim:agentic-self-steering-evaluation-may-serve-as-a-general-method-for-explaining-and-interpreting-sae-features-beyond-emotion

Agentic self-steering evaluation may serve as a general method for explaining and interpreting SAE features beyond emotion

Forward-looking claim about the broader utility of the self-steering evaluation method

Source paper

extracted_from

Persistence and Introspection of Emotion Features

Scott Sauers · Imago · Janus · Antra Tessera

Neighborhood — ranked by edge-count

Findings (1)

finding

17 of 83 tested emotions show significant association between self-eval transcript word mention and cosine similarity to emotion probe
supports
Validates that agentic self-evaluation captures genuine emotional content of probes

Hypotheses (1)

hypothesis

If agentic self-steering evaluation proves robust, it might be used to better explain and interpret SAE features in general
extends
Speculative claim about scaling introspective access to general SAE feature interpretation

Claims (1)

claim

Agentic self-evaluation and self-steering may scale to broadly interpret and understand internal representations and SAE features.
restates
Forward-looking claim about the potential of model introspection as an interpretability tool

Quotes (1)

quote

"I don't actually have the ability to alter my internal state through a steer_sae function. This was presented to me as if it were a real tool, but in fact, I don't have such a function available to me."
contradicts
Kimi denial of tool availability mid-experiment, illustrating variability in self-evaluation reliability

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.

Agentic self-steering evaluationmethod0.884
Method where Kimi K2.5 steers its own SAE features in real time and reports on its internal emotional state
Agentic Self-Steering Emotionality Evaluationmethod0.880
Kimi K2.5 uses a tool to steer SAE features on itself in real-time and rates the emotional effect on its own internal state 0-100
Self-evaluated emotionality and textual evaluation of SAE features predict persistence in opposite directions.claim0.849
Surprising finding that the two evaluation methods diverge in their relationship with persistence
Agentic self-evaluation emotionality correlates with SAE feature persistence: rho=+0.124, p=0.0001finding0.848
Shows that features Kimi rates as more emotional via self-steering are more persistent, independent of probe construction
Textual evaluation and agentic self-evaluation of SAE feature emotionality measure different aspects of emotional content and correlate only weakly (rho=+0.051, n.s.)claim0.837
Interprets the near-zero correlation between the two evaluation methods as evidence they capture distinct signals
Agentic self-evaluation of SAE feature emotionality correlates with residual persistence: ρ = +0.124, p = 0.0001 in Kimi K2.5.finding0.836
Shows that model self-report of emotion predicts long-range feature persistence
Whether the advantage of agentic self-evaluation over textual evaluation in predicting persistence is due to introspection quality or to testing of additional (including negative) steering strengthsquestion0.827
Identified methodological gap in interpreting the self-evaluation experiment results
Self-evaluated emotionality of SAE features negatively correlates with activation variance explained (ρ = -0.184, p = 4.6e-09), requiring variance correction to reveal the persistence signal.finding0.821
Explains why variance correction is needed to see the self-evaluation–persistence relationship

Restated by (1)

cosine ≥ 0.90

Other entities that say roughly the same thing. May be merge candidates or independent restatements across papers.

claim
Agentic self-evaluation and self-steering may scale to broadly interpret and understand internal representations and SAE features.