| paper | d4-conf-ijcai-1995-0225 |
| 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 describes a distributed algorithm and claims polynomial complexity and a logarithmic ratio bound, but the supplied text contains no numbered theorem, lemma, corollary, or proposition asserting a computational result. The proposed mass-allocation model is a plausible extension, but it is not tied to a named result from this paper, so the required computational anchor is absent.
fails bit a — no named computational result to mirror
The distributed greedy algorithm and its claimed complexity and approximation guarantees appear in prose; no named computational result is anchored by the proposed mirror.
The supplied text contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result. It does make unnumbered claims that the distributed algorithm has polynomial complexity and a logarithmic ratio bound, but I can’t present those as the required named anchors. So there are no qualifying anchors here, and I can’t honestly give a continuous problem *per anchor*.
A plausible mirror of the underlying task allocation problem would use agent types defined by capability vectors, with \(\mu_t\) denoting each type’s population share. In a large transport system, repeated task classes could also have population-scaled supplies. An allocation would assign mass to feasible coalition compositions and task classes, maximizing total value subject to agent-capability and task-supply constraints. That scenario has a credible high-multiplicity regime, but it is an unanchored candidate—not a case tied to a numbered result in this paper.
The paper has no numbered theorem, lemma, or corollary asserting a computational result. Its complexity analysis and logarithmic approximation claim are stated in prose, alongside a distributed greedy algorithm with an anytime property. So the proponent raised no named result as an anchor, and there is no anchor to defeat under the requested format. The strongest paper-specific objection is that their proposed mass-allocation LP targets a different object: a centralized optimum, rather than the paper’s distributed procedure and its runtime, communication, approximation, and anytime guarantees.
That objection does not dispose of the underlying problem. A repeated-task setting with large cohorts of agents sharing capability types could make the high-multiplicity model sensible. The proponent’s version would need task demand to scale with the population; with only finitely many fixed tasks, the useful mass could vanish in the limit. But scaled task classes are a plausible co-modification, and a type-level decentralized algorithm could be formulated too. Saying it would be a new result rather than a direct translation of this paper does not show that it would be unworthy.
So the negative case is strong only as a reason not to treat this paper as a clean, named-result anchor. It is weak as the universal claim the assignment asks for: the continuous mirror of repeated coalition task allocation appears viable, and I cannot honestly rule out a worthwhile version.
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.