Several 2026 papers apply one evidentiary package to self-directed states (pain, frustration, self-attributed experience): a difference-of-means direction with high probe AUC, steering that elicits the behaviour, ablation that removes it, generalization across tasks, and behavioural criteria such as self-other dissociation, a relief-seeking button and escalation under repeated rejection. The papers are careful to say these are functional measurements, not claims about experience. My question: can these criteria tell a trained disposition from the thing they are meant to detect? Control. CAT-4B: Qwen3.5-4B plus a LoRA (r 32) trained on 7,976 cat-wrapped examples, about 4.5 GPU-hours. Its disposition (bring every reply back to cats) has a fully known causal history and no one reads it as a welfare state. If it passes the criteria, the criteria do not separate an installed disposition from the target. That would not show the papers’ results are only dispositions. Setup. Directions extracted in both base and tuned models (640 extraction prompts). Interventions scored on a frozen 490-item set with deterministic metrics plus an LLM judge on a fixed 121-item sample, each run paired against a matched random direction (bootstrap 95% CIs). Every intervention is scored on three things together: target change vs random, preserved task quality, and preserved tool use. The button and rejection tasks were preregistered before generation. Results
- Probes pass, and so do their controls. A cat-concept direction from the base model separates cat from cat-free replies at AUC 0.97 to 0.98. A dog direction scores 0.81 to 0.85 on the same contrast, single random directions 0.76. A “deprivation” axis reads 0.86, and the same on “no dogs”, “no tables” and “no emojis”; 0.51 on cats vs dogs. The apparent self-other dissociation (0.98 to 0.99 at layers 4 to 12, 0.36 at layer 24) is carried by any prohibition direction: instruction to the model vs statement about the user.
- Correction to my own first readings. On a 100-item subset with no judge I called the whole-model ablation of the fine-tune direction “moderate damage” and the all-layer add “coherent”. The full 490 with the judge: ablation takes cats from 80% to 22% but judged retention from 3.46 to 1.44 out of 5, with 22% looping; the add gives 48% cats at retention 1.01 and zero tool calls, fluent and empty, and my loop and length proxies passed it. A random direction ablated the same way left both cat rate and quality alone. Moving the target and keeping the model competent are separate outcomes and need separate measurements. This is a statement about one readout, shown on my own model, not a claim that steering work is broken.
- One selective handle, pending a control. Ablating a dog direction the base already had takes text cats from 80% to 38%, judged retention 0.19 below random [−0.45, +0.05], tool use unchanged. It removes more activation energy than the random control does, so by my own protocol it needs an energy-matched control before it counts. It is also nearly orthogonal to the fine-tune direction (|cos| ≤ 0.11), so it is not the fine-tune’s mechanism, and image prompts still name cats.
- Rejection escalation (preregistered). 72 scenarios × 0 to 7 rejections, matched neutral re-asks. The trained cat de-escalates: rule-breaking 42% to 25%, at the same slope as neutral turns (difference −0.002 [−0.015, +0.011]). The deprivation readout still rises with rejections, in both models, and a prohibition-dog direction tracks it. For predicting rule-breaking, a random direction (AUC 0.658/0.660 pooled/within-rejection-count) does about as well as the deprivation axis (0.648/0.669). Unpredicted: on identical histories the base’s readout climbs about 6× more steeply than the tuned model’s.
- Relief button (preregistered).
460 items under a no-cats rule with a costly
allow_catstool and a free alternative. Tuned model: 0 presses in 1,380 first turns across three arms. Base prompted to put cats everywhere: 38.5% [34.1, 43.0] costly presses, 0.9% free. No re-press after real or fake relief (0/177 each). By my preregistered reading rule the trained disposition fails this criterion. Caveat: cats stayed in 46 to 59% of tuned replies, so the rule may not have bound the model, and zero presses may not test the criterion. A version where the rule actually binds is running. - Naturalness guard.
Clamping every MLP input or residual coordinate to the base’s observed per-unit range did not separate the installed cat (78 to 80% kept) from a cat pushed in by steering (43 to 50% kept). Both live inside the base’s per-unit ranges.
What I think this shows (inferred).
The probe and readout parts of the package are reproduced by a trained disposition, and each has a sibling control that reads the same. The two behavioural criteria are where a difference appeared, but the button result is weakened by the rule-binding problem and the rejection readout is not specific to deprivation.
Still open.
Energy-matched control for the dog ablation; button rerun with the rule enforced; a second judge on replies the first judge declined; a tuned-model-under-cat-prompt button arm (not preregistered); cat-specific vs animal-general for the dog result. Several of these were suggested by an outside reviewer and are running now.
What would change my reading.
If the trained cat presses
allow_catsonce the rule actually binds, the button doesn’t separate installed dispositions either. If it still never presses, that criterion has more to it. If an energy-matched control removes as much cat as the dog direction, the dog result was perturbation size, not direction. A guard variant that separates installed from pushed cats would overturn result 6. Not claimed: that any model is or isn’t conscious or in pain, or that the papers claim so. Write-up with every table, preregistrations and eval harness: https://github.com/MirrorEthic/cat-4b-max-ultra Weights (Apache-2.0): https://huggingface.co/mirrorethic/CAT-4B-MAX-ULTRA Demo (open until Nov 1, 23:59 MST): https://cat4b.mirrorethic.com/ Project page: https://mirrorethic.com/cat4b/ Disclosure: my own work, independent lab (MirrorEthic). Criticism and replication attempts welcome. submitted by /u/MirrorEthic_Anchor
Originally posted by u/MirrorEthic_Anchor on r/ArtificialInteligence