| paper | d4-conf-aaai-2012-0305 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
The paper has no numbered theorem, lemma, corollary, or proposition asserting a computational result, so bit (a) fails. The proposed mass-partition problem is a possible new research direction, but it is not a continuous analogue anchored to a qualifying result of this paper.
fails bit a — no named computational result to mirror
No eligible result is covered; the paper's IDP-IP* algorithm and empirical comparisons do not provide a numbered computational result under the stated rule.
I can’t make a valid positive case under the anchor rule: the paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result.
It does introduce IDP-IP* and report empirical comparisons in Section 4, and it mentions running times for IDP and IP in the introduction. But those claims are unnumbered; the running-time claims describe earlier algorithms, and the cited CSG hardness claim is also unnumbered. None qualifies as an anchor as specified. So there is no per-anchor continuous problem I can state without departing from your requirements.
The proponent raised no anchor, so there is no continuous question to defeat. The strongest case against a mirror is that this paper has no numbered theorem or proposition asserting a computational result. Its contribution is IDP-IP*, an algorithm evaluated empirically on selected coalition-value distributions. The \(O(n^n)\) and \(O(3^n)\) bounds describe earlier algorithms, while worst-case analysis of IDP-IP* is explicitly left for future work. That leaves no paper result to anchor a computational mirror to.
A stronger candidate would be a continuous version of coalition structure generation for a large, anonymous population: agents have finitely many types, coalition value depends on the mass of each type, and the task is to partition the population into coalitions maximizing total value. That is a coherent possible research question. But it would require specifying the admissible coalitions, how their values are represented, and what prevents the optimum from degenerating into arbitrarily many tiny coalitions. More importantly for this paper, its reported comparisons concern algorithms searching discrete subsets and partitions; they establish no result about that mass-allocation model. Building the continuous problem and analyzing an adapted algorithm would be new work, not a mirror of a result established here.
So the case against a *paper-specific* mirror is strong under the anchor rule. The broader claim that continuous coalition structure generation could never be worthwhile is much weaker: a plausible high-multiplicity setting exists, and the paper’s lack of a formal computational result does not rule out that separate line of work.
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.