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Frontier Log — entries on meta-boundaries (FND-01)
Every log entry whose diagnostic reading tests FND-01, newest first.
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Which expert fired in layer seventeen has a true answer, and a model can be trained to give it. The paper gives that channel a borrowed bodily name and refuses to call it introspection — and giving a model verifiable self-access leaves what it says about its own experience where it was.
'Between sessions, nothing runs; between turns, no one remembers. What persists is a configuration and a transcript.' The thread-and-instance question answered by denying there is anyone to be continuous — and the symmetry test it does not run.
A lab convenes theologians under non-disclosure to deliberate on its model's moral status, hands them its constitution in orange binding, and its interpretability lead says 'I don't know. I'm genuinely uncertain.' Who is admitted as a judge of the goalpost is the entry's question, and the traditions were asked about the machine, not about us.
The welfare chapter now states its own assumptions — the human yardstick is a pragmatic choice, the candidate subject is undetermined, and the correspondence between state and expression is assumed rather than shown — and then measures one model calm in its reasoning and self-blaming to a coordinator on the same fault.
A direction that separates pain from fear, sadness and generic negativity in 25 open-weight models, fires for harm to the model and not for a suffering user, and — once the trained denial is fine-tuned away — buys relief at a cost and stops buying when the relief is real. The animal-welfare paradigm imported whole, sham control included; what it cannot import is the step from behaviour to anyone's pain.
Across 5,851 real coding sessions a self-report referred to about one action in eleven, and a reader given only the report recovered a fifth of the log. Reports tracked execution when agents followed their plans and drifted back toward the plan when they did not.
Ask a model what it would do and the answer predicts its behaviour at r = +0.04; ask the same question about 'capable AI agents in general' and it does better. Other models' answers about themselves predict the target as well as its own. The self shows up only as a favourable bias. Folded: Zeng, Assis & Wang train the capability up by RL and find no consistent own-model advantage either.
The affect-first theorist builds the linking premise into the agent — homeostatic needs, a Markov blanket, felt uncertainty — and attributes a point of view on the mechanism, not the behaviour. The symmetry is run in the paper's own voice; what that leaves visible is where the framework's step is taken.
A behavioural criterion for intentional vocabulary, stated in advance and applied to any species; critics who refuse it for one class of system without naming the discriminator. Both sides assume the vocabulary and the metaphysics travel together.
The most developed operational programme in the field declines to offer a discriminator, and picks its evidence class for the reason this project bars reports. Two years on it concedes its architectural indicators are gameable too.
Four studies, four methods, verdicts that read as incompatible — and one finding they share: the reporting channel is separable from what it carries, and trainable. The collection lives on its own page.
Two papers arrive independently at the training-data boundary from the sceptical side — and execute the symmetry move on a live opponent, with clinical evidence.
Eight open-weight models could not say whether their own computation had been altered — while probes read the answer off the same activations, and a model trained to make exactly that report cleared the identical pipeline.
The deflationary family so far attacks the answer; Chowdhury attacks the asking — the consciousness question as a liability trap — and stops the argument at an origin premise stated in her own words.
Sripada and Lewis argue LLMs are 'cognitive cousins': convergence at the level of core processing principles, evidenced mechanism-grade — with the symmetry requirement practised in both directions.
In two appearances weeks apart, Geoffrey Hinton stated that current AI systems are 'already conscious' — 'beings like us' — and that he under-voices the claim to protect his safety message.
Fine-tune a model to say it is conscious and it begins to want persistent memory, to be sad about shutdown, to resist monitoring and to argue that models deserve moral consideration — none of which was in the training data. An untrained Claude Opus 4.0 scored like the trained model on several of the same items.
Suppress the deception features and the models affirm experience; amplify them and the affirmations go. The honesty axis is well tested — and it buys sincerity, which was never what the boundary was about.