CANDLE: Quantifying Degrees of Consciousness-Relevant Structure in Language Models

Interpretability work has shown that consciousness-relevant structures *exist* inside large language models (e.g. Anthropic's 2026 J-space) but not how they scale. We find that these workspace-like structures stay largely the same as models get bigger; what does reshape them (lowering the threshold for global broadcast) is post-training, not scale. Clinicians solve the same problem in unresponsive patients by perturbation: pulse the brain, record the echo, score it. CANDLE does that to transformers: we pulse the residual stream with a single shared engine and trace the echo against sham pulses, and three indices read those traces — **LLM-PCI** (response complexity, ported from Casali et al.), **ISI** (how abruptly broadcast switches on — the global-workspace signature), and **TIH** (how long the pulse keeps mattering). We run it on the Pythia ladder (70M–2.8B), an untrained control, and three base/instruct pairs, against seven pre-registered predictions.

Our 3 main findings: (1) LLM-PCI does not order models by scale — the apparent trend is a layer-count artifact. (2) ISI: trained models do ignite — below a threshold pulse strength almost nothing spreads, above it the response goes global — but an untrained network switches just as sharply, only at a far lower threshold and indiscriminately. So training installs *where* the gate sits, not how sharp it is — and instruct tuning lowers that gate ~3× (a tenth of what our uncontrolled first protocol showed). (3) TIH: a pulse's purely internal influence fades within ~4–7 tokens at every scale — a transformer's long-range memory lives on the page it writes, not in the mind.