Published successors

A genuine boundary case, and the call is close. The paper's headline contribution is a large-scale, offline, forced-choice test: crowd-collected human and AI responses to 3 language tasks and 3 vision tasks are shown to judges who pick which of two responses is human. Only one of the six tasks (conversation) is conversational in Turing's sense; color estimation and object detection are psychophysical judgments with no dialogue. Mapped as non-fork because the underlying evidentiary method is identical across all six tasks and identical to Turing's own move: behavioral output, forced-choice human-vs-machine judgment, nothing mechanistic — the paper generalizes indistinguishability judgment across modalities rather than replacing it with a different construct (contrast with the NeuroAI Turing Test in this corpus, which swaps behavior for internal representations). format='displaced' reflects the primary, large-N test: judges read/view pre-collected transcripts and vision stimuli rather than interrogating live. The paper also ran a much smaller supplementary live 3-party experiment (one judge simultaneously conversing with one human and one AI agent, GPT-3.5-Turbo) that gave very different results (see empiricalResults) — not the paper's primary config, but a striking finding about how format changes what is measured. duration is defaulted: the paper caps conversations by exchange count (up to 24, extended to 48), not minutes. threshold='chance' is an editorial call: the authors never declare a formal numeric pass bar but repeatedly interpret their 'imitation detectability' scores (0.53-0.57) against an implicit chance/0.5 reference. rigor is defaulted to informal despite substantial disclosure (public code/data, cross-platform replication, corrected permutation tests), since no pre-registration is mentioned. Version note: v1 (2022-11-23, the date recorded above) already contained the core methodology but with smaller numbers (769 human agents, 24 AI agents, 21,570 trials); the abstract quoted above reflects v3 (Sep 2025) figures. The live-interrogation sub-experiment using GPT-3.5-Turbo postdates v1, since that model did not exist until March 2023.

Important correction to my own working assumptions: the abstract's laundry list (longer duration, Internet access, expert evaluators) describes the paper's ASPIRATIONAL 'Ultimate Turing Test / Turing Test 2.0' discussed in Section 6, which is NOT what was actually run. What the paper actually implements and tests (Section 3) is narrower: a 'Simple Turing Test' (two-party, single chat window, 2-minute limit) versus an 'Enhanced Turing Test' (three-party: a tester converses simultaneously with a human responder and an AI in two separate windows, exactly Turing's 1950 structure, 5-minute limit). Internet access and domain-expert evaluators are proposed as future work, never implemented; extended duration also never appears in the tested design — the Enhanced Test's 5 minutes matches, rather than exceeds, the 1950 baseline. Config maps to the Enhanced Test, the paper's actually-validated, recommended design and central empirical claim. format='three-party' because the Enhanced Test's dual-chat, simultaneous-comparison structure matches dimensions.json's three-party definition directly. interrogator='motivated' rather than 'naive': testers are Mechanical Turk workers screened for quality and paid a bonus specifically for correctly identifying the human/AI — financially incentivized to win, though not domain experts; a reader could push this toward 'naive' instead. threshold='chance': the paper interprets its results relative to a 50% cutoff throughout (e.g. treating 43.9% correct-identification as the AI having 'passed'). rigor='informal': real statistics (chi-squared tests) and full model/prompt disclosure, but no pre-registration.

The judge stays human throughout the paper's own contribution (Section 3): a fixed judge is presented, per round, with an unlabeled pair of replies — one human, one AI — to a sampled prompt, and must say which is which. The 'Inverted Turing Test' the abstract mentions is explicitly cited as prior art (Watt's classic variant where a machine judges), not this paper's own mechanism — so 'inverted' (machine judges which interlocutor is human) does not apply; this is still a human-judge game with the payoff flipped from being deceived to catching the deception. format='displaced' is a judgment call: prompts are drawn from a fixed prompt space rather than authored/adapted live by the judge, and the judge only classifies a pre-generated reply pair rather than conducting an interrogation — closer to reading a transcript than steering one; a reader could argue for 'three-party' instead since both outputs are presented side by side for one comparative verdict. threshold is the hardest fit: the paper's own pass criterion is a minimax DETECTION accuracy for the judge (>= 0.70 in one worked example), the mirror image of this schema's deception-rate framing; its one explicit statistical anchor is a binomial test against a 50% chance baseline, which is what 'chance' is mapped from here, but no option in this schema was built for a flipped pass criterion. interrogator='motivated' reflects that the entire premise is a judge deliberately trying to catch the AI, not Turing's naive general-public interrogator, though no domain-expertise or tool-use is specified. Sections 4-5 (an RL alignment pipeline using the detector as a reward-model critic) are a separate training-procedure proposal layered on top of the dual test, not part of the test itself — noted as a design element the schema doesn't capture, but not enough to push this into fork territory since Section 3's core mechanism is still a human-vs-AI discrimination game.

A genuine same-game extension, not a fork: the paper explicitly keeps and re-derives Turing's three-party structure ('It is played with three players: the liar, the truthteller, and the interrogator... What will happen when a machine takes the part of the liar in this game?') and states, 'We can actually embed the original imitation game within this new game by asking "Can machines think?"' — so format='three-party' and constraint='energy' are both grounded directly in the text, the latter being the paper's whole contribution. Everything else is left unspecified by the paper and defaulted: no channel restriction, no session-persistence discussion, no time bound for the interrogation itself, no interrogator-sophistication spec, and no numeric pass criterion is ever given. evidence='behavioral' rather than 'mechanistic': the energy measurement (via a hypothetical 'psychoergometer') is an external property of the answering process, not an inspection of internal representations.

Not a fork, and arguably the opposite of one: a strict, literal replication of Turing's 1950 three-party game, run more carefully than prior claimed 'passes', not a redefinition of what is measured. format=three-party and evidence=behavioral are explicit and central ('We conducted Turing's three-player imitation game... following the guidelines identified by Turing'). modality=text and memory=single are explicit in the described setup (single-sitting, message-based game via the OpenAI API; each participant took part in only one trial in a single role). duration=unbounded is explicit and is the paper's central methodological point: the computer-imitates-human game (CIHG) was run 'without duration constraints,' and the authors argue this is precisely why they get a different (negative) result than time-boxed prior studies. interrogator=naive is explicit, not defaulted: the paper quotes Turing's own spec that the interrogator 'should not be expert' and confirms recruited participants were not required to have AI expertise. constraint=none and threshold=30pct are both defaulted — the paper never imposes a compute/energy budget, and never explicitly adopts Turing's 30% figure as its criterion; its own statistical tests are framed against a 50%-chance null, which arguably reads closer to 'chance' than '30pct' — a genuine judgment call, defaulted to the schema base rather than overridden since the paper is silent on a specific number rather than affirmatively adopting a different one. rigor=full is explicit and strongly supported: preregistered on AsPredicted (#148990), IRB-approved (Univ. of Canterbury HREC 2023/98/LR-PS), with exact model (GPT-4-Turbo), API parameters, and the full system prompt disclosed. Title note: the current arXiv title is 'A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence' (lowercase 'a'), which differs from the working title 'The Imitation Game According To Turing' found in earlier secondary coverage — this is the live, current title, recorded verbatim.

This is the proposal in the corpus that strains the nine-axis schema hardest, because it removes both fixed roles Turing assumed. Formally: A >= B iff B, acting as 'distinguisher', cannot reliably tell an interaction with a fresh instance of B from an interaction with A instructed to imitate B. Neither A nor B need be human; the paper's own worked example is Gemini 3.1 Pro imitating, and being distinguished by, Claude Opus 4.6. The word 'interrogator' does not appear anywhere in the paper (checked by full-text search) — 'distinguisher' replaces it deliberately. FORMAT: each trial is one distinguisher conversing with one unknown interlocutor and rendering a binary verdict, with no simultaneous side-by-side pair and no separate human judge — structurally closest to 'two-party' (a witness judged in isolation), just with a non-human witness and non-human judge. MODALITY: interactions are explicit multi-turn LLM conversations, so 'text' is not a default. MEMORY: each trial is fresh and bounded, matching 'single'. EVIDENCE: purely behavioral — the distinguisher only sees transcripts, never weights or activations. DURATION genuinely does not map: trials are paced in turns (main GTT capped at 40 distinguisher turns, GTTQ query phase at 20), not minutes; left at the '5min' default for lack of a better option, but a 40-turn LLM exchange is almost certainly far more content than a human 5-minute teleprinter session — the schema needs a turn-count axis to represent this proposal honestly. INTERROGATOR is the single biggest mismatch: the distinguisher in every reported experiment is a model instance, never a human. None of naive/motivated/literate/expert describes 'the judge is itself an AI system'; defaulted to 'naive' only because there is no non-human option. THRESHOLD mapped to 'chance': the pass criterion is Pr[B succeeds] <= 1/2 + epsilon (epsilon=0.005 in the main empirical figure) — the distinguisher must do no better than a coin flip within a small tolerance. A stronger 'statistical indistinguishability' definition is also given but explicitly described as impractical to test and not what the empirical section operationalizes. RIGOR='informal', matching the authors' own characterization: 'this study is intended as a first empirical instantiation of the framework rather than a high-precision measurement campaign,' no preregistration, no significance testing on the main pairwise matrix.