| paper | d4-conf-atal-2018-0170 |
| 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 reports simulation outcomes and describes an implemented blockchain protocol, but contains no numbered theorem, lemma, corollary, or proposition asserting an algorithmic or complexity result. Without a qualifying computational result, there is no result of this paper to continuize; the proposed Byzantine-mass question would require a new model of the dynamics.
fails bit a — no named computational result to mirror
No named computational result is covered; the Section 5 exit-probability and consensus-time comparisons remain empirical.
I can’t make a qualifying anchored case for this paper: it contains no numbered theorem, lemma, corollary, or proposition asserting a computational complexity or algorithmic result. Its findings are proof-of-concept and simulation results in Sections 5–6. For example, Figure 4 reports how exit probability changes with the number of Byzantine robots for \(N=20\); that is an empirical comparison, not a named computational result. There is therefore no anchor to quote and restate, and no per-anchor continuous problem whose complexity I can responsibly predict.
The most plausible *unanchored* mirror would use a large fleet of similar robots, with types describing behavior such as honest or Byzantine and relevant sensing conditions. Mass would be the fraction of the fleet in each type; the question could be to compute the probability of correct consensus, or its expected time, under the paper’s blockchain strategy. That tracks the paper’s measured outcomes and could be a recognizable swarm-robotics question. But the paper does not establish a computational result about those quantities. Also, its robot-specific public keys and blacklist, along with local communication, make it unclear whether a small set of aggregate types preserves the behavior being studied.
The proponent raised no named computational result, so there is no paper result to anchor a continuous mirror to. The paper reports simulation measurements for \(N=20\), including exit probability and consensus time; it gives no algorithmic or complexity claim about computing them. That is the strongest objection: a continuous question would have to be supplied from outside the paper’s results.
The best candidate is the proponent’s Byzantine-fraction question: given masses of honest and Byzantine robots, compute the chance or time of correct consensus. But those masses do not determine the process the paper studies. Robots interact through changing local neighborhoods; their blockchain copies can fork and later merge; and the contract uses public keys, individual vote histories, and individual blacklisting. Capturing those details in types would make types depend on positions, histories, and identities, undermining the finite-support, high-multiplicity model. Omitting them gives an aggregate model of a different coordination protocol.
A well-mixed population model might still be useful for studying swarm consensus. But that would be a new model of evolving group dynamics, not a continuous mirror of a computational result in this paper. The negative case is strongest on that distinction; it cannot rule out a worthwhile aggregate model someone might develop independently.
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.