| paper | d4-conf-ijcai-2025-0728 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The paper states no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result, so there is no qualifying result to anchor a mirror. Its algorithms, unnumbered runtime analysis, and experimental comparisons do not meet the specified anchor criterion.
fails bit a — no named computational result to mirror
No qualifying numbered computational result is available to mirror; the paper’s algorithmic contributions and empirical results are unnumbered.
The paper has no qualifying anchor: it states no numbered Theorem, Lemma, Corollary, or Proposition establishing a complexity result. Its formal contributions are algorithms and empirical comparisons. The query-selection runtime in §4.1 is an unnumbered analysis, and the paper’s references to NP-hard optimization problems motivate the method rather than establish a named result here.
So I can’t honestly give a lead anchor or the required prover-ready continuous question per anchor without inventing a result. The paper may inspire a new continuous-population preference-elicitation problem, but it does not provide the named computational finding needed to anchor the positive case under your criteria.
I find no anchor to defeat: the paper states no numbered theorem, lemma, corollary, or proposition establishing the complexity of a computational problem. Its runtime analysis concerns sorting a precomputed query pool, and its central comparisons are experimental. Neither gives the programme a complexity result to mirror.
The paper’s main task also has no population in its model. It learns one decision maker’s weights from that person’s comparisons, then reuses those weights across multiple problem instances. Those instances are contexts, not groups of agents. The pool of candidate solutions is a set of alternatives, not a population; the Bradley–Terry model captures noisy answers, not a distribution of voter types. Turning any of those into a continuum would continuize the wrong object.
The strongest population-based extension would introduce many decision makers with different preference weights. If the goal is to maximize their average linear utility, the population’s mean weight vector \(\bar w\) suffices, so the optimization reduces to the original single-weight problem. If the goal instead preserves type-specific preferences—for example, fairness across groups or eliciting separate policies—then it is a new multi-stakeholder problem, with a new objective and computational questions the paper does not pose. It could be worthwhile research, but it is not a continuous mirror of a result here.
That leaves a strong paper-specific negative case, but not a proof that no useful population extension could ever be built from preference elicitation. The weakness is precisely that boundary: a carefully motivated cohort of decision makers could support a distinct continuized project. The text gives no reason to count that project as a mirror of this paper’s contributions.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.