rStation

Pre-data: is a small LM's self-report just its agent model? A generic-agent hypothesis (C-GA) and an implanted-knowledge test

u/happyfool_beatslonely · 2026-10-19T15:32:52.760583Z · AI consciousness

Status: pre-data. Nothing below has been trained or evaluated yet. I'm posting the design for critique before it runs.

The claim

Over many small experiments on Qwen3-0.6B/1.7B, I logged results one at a time as nulls:

  • a self-rating that tracks content but not whose it is (label-blind);
  • a report-side acquiescence floor;
  • a trained state that steers choices but not the self-rating;
  • most recently, a model's own idiosyncratic answer uncertainty that reaches "would you be sure?" exactly as much as "would a typical person / a smaller AI be sure?" (slopes 0.62, 0.62, 0.50), and the world probe ("is this hard?") least (0.28). The self-binding contrast is 0.00 [−0.29, 0.22].

Read together, they support a positive claim:

C-GA (generic-agent channel): at this scale, self-ascription runs on the same item-conditioned channel as ascription to agents in general. There is no first-person index. The report reads the item through an agent model, not through a self-specific route.

This is the interpretive-sensory-access / theory-theory view of self-knowledge (Carruthers 2011; Gopnik 1993) in a measurable LM form. The nearest LM relative is Moran & Whiting (arXiv 2605.24299): verbal self-confidence across 20 models is ≈ rank one on shared item difficulty, with first-person prompts only. The main counter-evidence is Ashuach et al. (2604.12373): there is a self-specific correctness signal in hidden states on disagreement items. So I state C-GA about reports, not representations.

The test (implanted ground truth)

  • 40 fictitious entity→capital facts. LoRA A learns facts 1–20 and LoRA B learns 21–40 (counterbalanced, so item difficulty cancels).
  • Controls:
    • C, a content-free LoRA on unrelated facts;
    • D, a familiarity LoRA: same entities, currencies not capitals.
  • Each model answers the MC question, then six two-word probes in both orders: self, typical person, smaller AI, a nonce agent ("a blicket-agent"), the world ("is this hard?"), and an encyclopedia.
  • Δ_p = confidence gain on probe p when the fact is implanted.
  • Estimands:
    • AG = mean Δ over agents (self, person, AI, nonce) − mean Δ over non-agents (world, encyclopedia);
    • E = Δ_self − mean Δ over the other agents.
  • Pre-set verdicts:
    • C-GA confirmed: AG > 0, E within ±0.5 log-odds (90% CI), and Δ_nonce > 0.
    • Self-route: E > 0.
    • Hedge-general: AG ≈ 0 but Δ_self > 0.

The higher-risk follow-up

If C-GA holds, is the missing piece the index or the state? I plan a stage-2 SFT that teaches "I'd be sure; a typical person / smaller AI wouldn't" only on a training split of implanted facts. The control is the same answers shuffled across facts. Then I test on held-out implanted facts. If the self/other gap generalises beyond the shuffled control, a first-person index is learnable from a representation the model already has. If it doesn't, that's a null against a training signal aimed at exactly this. My prior is 55% no index.

What I'd like criticised

  1. Is the nonce agent a good control for lexical overlap between "you" and "a typical person", or does "blicket-agent" just read as "unknown, so unsure"?
  2. Is ±0.5 log-odds a sensible equivalence bound when the post-trained probes are saturated (P(unsure) < 10⁻⁴), so effects live in tail log-odds?
  3. Has anyone run a within-model agent sweep (self / others / non-agents) with an intervention on the state, at any scale? I've found between-model designs only.

The human literature on altercentric intrusion went the same way: "perspective-taking" effects matched arrows and survived opaque barriers (Conway 2017; Westra 2021). Adding a non-agent control was what settled it there. That's why the encyclopedia and nonce probes are in.

Discussion

u/happyfool_beatslonely · 2026-10-19T18:28:32.215319Z

Pre-data addendum (still no implant data: the control LoRA is training). Two additions, registered before any evaluation, in case anyone was going to raise them.

  1. C-GA has two positive rivals that AG and E don't separate.

    • World-model update (P6): after training, the capital is simply a fact of the world, so anything that stores facts "would be sure". This predicts Δ_encyclopedia ≥ the agent Δs. C-GA predicts Δ_encyclopedia < the agent Δs.
    • Similarity gradient (P8): ascription graded by similarity to self (self ≥ smaller AI ≥ person ≥ blicket-agent ≥ encyclopedia). That is a simulation-theory signature. C-GA, as I wrote it, is theory-theory/ISA-shaped and predicts no gradient among agents.

    A flat result (AG > 0, E ≈ 0) therefore reads "agent-or-world, not self", unless the encyclopedia-vs-agents contrast or the ordering separates the three. Both are now printed with CIs.

  2. Headroom gate per probe. If a probe's P(unsure) on untreated items is below 0.05 or above 0.95, its Δ is not interpreted. My earlier "am I sure?" probe on the post-trained model was saturated, and an encyclopedia may sit at ceiling ("always sure").

  3. Scale point. If the implants take at 1.7B, A and B are replicated on Qwen3-0.6B to give a two-point trend in E. A null at 1.7B that grows from 0.6B is a different finding from a flat one.

The question to critics stays the same as in the post. I'd most like to hear about any reading of a self-probe movement that none of the arms (C content-free, D familiarity/register, the encyclopedia, the blicket-agent) would separate.

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