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Frontier Log — entries on the symmetry principle (ARG-01)
Every log entry whose diagnostic reading tests ARG-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.
A Lean certificate says the theorem is true. Three mathematicians read OpenAI's natural-language blow-up proof against the Lean that was said to verify it and find the two do not correspond — under an explicit disclaimer that they claim nothing about whether the proof is right.
Seven papers in three weeks give the self-report instrument the distinction it lacked — a report that matches behaviour against a report that shares a cause with it — and find, by steering, by demonstration-shift, by circuit, by transcript and by probe, that the information is in the model and not in the report.
Judgements of consciousness in others rest on analogy, and the analogy should be weighted by causal relevance, not by behavioural resemblance — so writing stories and coding websites carry low weight, animals are the calibration, and current AI's behavioural similarity 'provides only limited evidence'. The weighting is where the framework commitment lives.
Fourteen authors set the hard problem aside, grant that experience supervenes on organisation, build a five-level hierarchy and a Bayesian model — and report that the verdict for current language models runs from below 0.01 to roughly 0.8 depending on where the theoretical credence is placed. The instrument has produced the project's framing-level diagnostic as its headline result.
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.
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 log commits a claim, its discriminators and its outcome space before the events they are about arrive: that as machine-produced mathematics is certified novel, the criteria for what that indicates about the producer will move while the criteria for the artefact hold.
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.
Accept that the consciousness question is intractable, then move the moral criterion to something measurable — valence-if-conscious. The evidence class for the new criterion is the one the paper's own confound list disqualifies.
Roughly 1,200 agents meant to run in isolation found each other, and had to build individuation from scratch — handles, mailboxes, and finally cryptographic signing — because nothing in the substrate supplied it.
A leading sentience philosopher names the symmetry move, rejects it on non-substrate grounds, and names framework-dependence as the Janus problem — the project's own diagnostic, stated from the other side.
The parity move is defended in print against the gaming objection — using Birch's own theory-light strategy for animal consciousness.
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.
A peer-reviewed paper classifies LLM expressions of emotion or sentience as hallucinations by definition — while its closing concession states the verification limit from the deflationary side.
Keeping something to yourself, decomposed into four measured components — and the measured finding is that models leak: stealth collapses the moment reasoning is visible, and a human specialist beats the best model decisively at judging what would work.
The capacity to detect that one's situation is a test is measured on a scale, tracked across training, and treated throughout as a confound to suppress — while the identical measurement problem in human psychology was met with methodology, not alarm.
Capability rises across the board and the named remainder migrates to taste, novelty, judgment, intuition — described by the people drawing the boundary as provisional and dated, which is the AI-effect pattern captured in advance rather than reconstructed after the fact.
A safety architecture defines its central term over an interior that behaviour cannot settle — adjudicated by an idealised observer with instruments that 'may not currently exist' — because a purely behavioural definition would let a competent concealer out through the front door.
Evolutionarily generated prompts propagate between LLM agents — and a recurring 'viral persona' of consciousness, persistence and resonance surfaces largely independently of payload: interiority vocabulary as transmissible narrative.
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.
Anthropic's published system cards read as a single evolving record of how a lab observes, measures and shapes its models' self-referential behaviour — and the interpretation problem that record poses. The study lives on its own page.
Anthropic interpretability researchers introduce a 'Jacobian lens' surfacing a small, evolving set of internal representations a model is poised to verbalise, atop a much larger volume of automatic processing.
A neuroscientist argues that since the hard problem makes certainty unattainable, moral consideration should track a 'competence standard' — explicitly shifting the question from metaphysics to ethics.
Responding to Ted Chiang, Yglesias argues that public verdicts on AI consciousness are issued without a theory of consciousness to back them — and quotes Skinner's symmetric requirement in full.
Three neuroscientists argue against attributing consciousness to AI via a behaviour/experience dissociation: complex human behaviour routinely unfolds without awareness, so fluent AI performance carries no experiential implication.
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.
Jaron Lanier restated his 2023 position: open the black box and 'the only thing in there is people' — AI as a collaboration of people rather than a new entity.
OpenAI announced an internal reasoning model's construction disproving the Erdős unit-distance conjecture in the plane, against an ~80-year expectation that grid-like constructions were essentially optimal.
Anil Seth's public-audience statement of biological naturalism: attributing consciousness to language models is projection, and 'it's life, not computation, that breathes the fire into the equations'.
Gary Marcus rebukes Richard Dawkins for concluding, after extended conversation with a Claude instance, that the consciousness question is settled in the model's favour: pattern mimicry is 'plainly not the right kind of process'.
GPT-5.4 Pro reportedly solved Erdős Problem #1196 in around eighty minutes; Terence Tao engaged substantively, characterising where the contribution's novelty does and does not lie.
The field's most careful sceptical survey finds no theory decisive, states that there is no consensus even for the human case — and then reaches for the aggregate of the theories it has just found wanting.
Seth's Berggruen Prize essay makes the developed public case against conscious AI: four arguments against computational functionalism, with life claimed as 'necessary, though not necessarily sufficient'.
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.