question
active
question:can-language-models-genuinely-introspect-on-internal-states-or-only-confabulateCan language models genuinely introspect on internal states or only confabulate?
Central research question animating the paper: distinguishing genuine introspection from illusion through causal manipulation of activations.
Source paper
extracted_from(2026) · Lindsey, Jack
Neighborhood — ranked by edge-count
Thinkers (1)
thinker
- Jack Lindseystudies
Findings (3)
finding
- Self-report of Injected Thoughtsanswered_byModels can detect and identify injected concept vectors ~20% of the time at optimal layer/strength in Opus 4.1, with immediacy suggesting internal rather than output-inferred detection.
- Models maintain ability to accurately transcribe input text while simultaneously reporting on injected thoughts, all models perform above chance, Opus 4/4.1 best.
- Models can distinguish artificially prefilled outputs from intentional responses by referencing prior internal representations; injection of matching concept vector causes model to retroactively accept prefill as intentional.
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.
- Can large language models introspect—that is, accurately detect perturbations to their own internal states?question0.866Central research question of the paper
- Abstract's main conclusion.
- Central open question raised by the paper.
- Modern language models possess at least a limited, functional form of introspective awarenessclaim0.832The paper's central interpretive assertion.
- Claim about model phenomenology; models talk about luminousness and can be terrified or love it.
- How general are the model's introspective mechanisms? Do they have a global representation of thoughts?question0.803Question about uniformity of introspection mechanisms.
- Large language models develop surprisingly coherent yet often rigid internal preferences as they scalefinding0.799Mazeika et al. finding reinforcing the need for emptiness-based flexible value architectures
- Related work demonstrating LLM introspective capabilities with scale-dependent pattern paralleling ESR