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Sniffing Helps to Meet: Deterministic Rendezvous of Anonymous Agents in the Grid

Distributed, Parallel, and Cluster Computing · Submitted 20 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The above negative result shows a separation between the optimal complexity in the two easier scenarios and the optimal complexity in the most difficult scenario.

how it is stated · asserted 1.00 read the record →

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arXiv ID2407.14969 (version 1)
Pith IDZRJ7XGGM, Pith's permanent record of this paper and its review
CitationAndrzej Pelc, Younan Gao. "Sniffing Helps to Meet: Deterministic Rendezvous of Anonymous Agents in the Grid." arXiv:2407.14969 (2024). https://pith.science/paper/ZRJ7XGGM
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 12,489 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.79 · human participants 0.01 · controlled comparison 0.06 · randomized assignment 0.02 · states quantitative result 0.98 · compares with prior results 0.32 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Two identical anonymous mobile agents have to meet at a node of the infinite oriented grid whose nodes are unlabeled. This problem is known as rendezvous. The agents execute the same deterministic algorithm. Time is divided into rounds, and in each round each agent can either stay idle at the current node or move to an adjacent node. An adversary places the agents at two nodes of the grid at a distance at most $D$, and wakes them up in possibly different rounds. Each agent starts executing the algorithm in its wakeup round. If agents cannot leave any marks on visited nodes then they can never meet, even if they start simultaneously at adjacent nodes and know it. Hence, we assume that each agent marks any unmarked node it visits, and that an agent can distinguish if a node it visits has been previously marked or not. The time of a rendezvous algorithm is the number of rounds between the wakeup of the later agent and rendezvous. We ask the question whether the capability of marking nodes enables the agents to meet, and if so, what is the fastest rendezvous algorithm. We consider this problem under three scenarios. First, agents know $D$ but may start with arbitrary delay. Second, they start simultaneously but do not have any {\em a priori} knowledge. Third, most difficult scenario, we do not make any of the above facilitating assumptions. Agents start with arbitrary delay and they do not have any a priori knowledge. We prove that in the first two scenarios rendezvous can be accomplished in time $O(D)$. This is clearly optimal. For the third scenario, we prove that there does not exist any rendezvous algorithm working in time $o(D^{\sqrt{2}})$, and we show an algorithm working in time $O(D^2)$. The above negative result shows a separation between the optimal complexity in the two easier scenarios and the optimal complexity in the most difficult scenario.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Two identical anonymous mobile agents have to meet at a node of the infinite oriented grid whose nodes are unlabeled. background 1.00 0.08 0.02 asserted 0.95
s1 This problem is known as rendezvous. background 1.00 0.04 0.01 asserted 1.00
s2 The agents execute the same deterministic algorithm. background 0.90 0.09 0.01 asserted 1.00
s3 Time is divided into rounds, and in each round each agent can either stay idle at the current node or move to an adjacent node. background 0.82 0.09 0.02 asserted 0.97
s4 An adversary places the agents at two nodes of the grid at a distance at most $D$, and wakes them up in possibly different rounds. background 0.87 0.13 0.02 asserted 0.93
s5 Each agent starts executing the algorithm in its wakeup round. background 0.77 0.09 0.02 asserted 0.99
s6 If agents cannot leave any marks on visited nodes then they can never meet, even if they start simultaneously at adjacent nodes and know it. background 0.81 0.22 0.04 asserted 0.98
s7 Hence, we assume that each agent marks any unmarked node it visits, and that an agent can distinguish if a node it visits has been previously marked or not. background 0.52 0.18 0.03 asserted 0.95
s8 The time of a rendezvous algorithm is the number of rounds between the wakeup of the later agent and rendezvous. background 0.75 0.22 0.02 asserted 0.99
s9 We ask the question whether the capability of marking nodes enables the agents to meet, and if so, what is the fastest rendezvous algorithm. gap 0.50 0.16 0.04 unclear 0.85
s10 We consider this problem under three scenarios. method 0.56 0.53 0.02 asserted 0.70
s11 First, agents know $D$ but may start with arbitrary delay. method 0.38 0.25 0.03 asserted 0.70
s12 Second, they start simultaneously but do not have any {\em a priori} knowledge. background 0.54 0.15 0.02 asserted 0.85
s13 Third, most difficult scenario, we do not make any of the above facilitating assumptions. method 0.41 0.33 0.03 asserted 0.92
s14 Agents start with arbitrary delay and they do not have any a priori knowledge. background 0.78 0.15 0.03 asserted 0.88
s15 We prove that in the first two scenarios rendezvous can be accomplished in time $O(D)$. This is clearly optimal. result 0.99 0.93 0.28 asserted 0.99
s16 For the third scenario, we prove that there does not exist any rendezvous algorithm working in time $o(D^{\sqrt{2}})$, and we show an algorithm working in time $O(D^2)$. result 0.98 0.96 0.67 asserted 1.00
s17 The above negative result shows a separation between the optimal complexity in the two easier scenarios and the optimal complexity in the most difficult scenario. interpretation 1.00 0.89 0.27 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from proof or derivation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Two identical anonymous mobile agents have to meet at a node of the infinite oriented grid whose nodes are unlabeled. This problem is known as rendezvous. The agents execute the same deterministic algorithm. Time is divided into rounds, and in each round each agent can either stay idle at the current node or move to an adjacent node. An adversary places the agents at two nodes of the grid at a distance at most $D$, and wakes them up in possibly different rounds. Each agent starts executing the algorithm in its wakeup round. If agents cannot leave any marks on visited nodes then they can never meet, even if they start simultaneously at adjacent nodes and know it. Hence, we assume that each agent marks any unmarked node it visits, and that an agent can distinguish if a node it visits has been previously marked or not. The time of a rendezvous algorithm is the number of rounds between the wakeup of the later agent and rendezvous. We ask the question whether the capability of marking nodes enables the agents to meet, and if so, what is the fastest rendezvous algorithm. We consider this problem under three scenarios. First, agents know $D$ but may start with arbitrary delay. Second, they start simultaneously but do not have any {\em a priori} knowledge. Third, most difficult scenario, we do not make any of the above facilitating assumptions. Agents start with arbitrary delay and they do not have any a priori knowledge. We prove that in the first two scenarios rendezvous can be accomplished in time $O(D)$. This is clearly optimal. For the third scenario, we prove that there does not exist any rendezvous algorithm working in time $o(D^{\sqrt{2}})$, and we show an algorithm working in time $O(D^2)$. The above negative result shows a separation between the optimal complexity in the two easier scenarios and the optimal complexity in the most difficult scenario.

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