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arxiv: 2601.14075 · v2 · submitted 2026-01-20 · 💻 cs.IT · cs.SY· eess.SY· math.IT

Utilizing the Perceived Age to Maximize Freshness in Query-Based Update Systems

Sahan Liyanaarachchi , Sennur Ulukus , Nail Akar This is my paper

Pith reviewed 2026-05-16 12:26 UTC · model grok-4.3

classification 💻 cs.IT cs.SYeess.SYmath.IT
keywords query-based samplingmean binary freshnesscontinuous-time Markov chainsoptimal sampling policiesperceived agepull-based update systemsgeneric delay distributions
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The pith

Waiting-based query policies deliver significant gains in mean binary freshness for continuous-time Markov chains under arbitrary delay distributions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies query-based sampling to monitor continuous-time Markov chains in pull-based update systems. It drops the usual assumptions of exponential query delays and instantaneous feedback, then derives optimal sampling policies that work for general delay distributions. The central finding is that inserting a deliberate waiting period after each query response, guided by the perceived age, produces substantially higher mean binary freshness than immediate-query policies. This matters for real systems where delays vary and feedback takes time, because the waiting strategy times queries to better track source changes without extra assumptions. The result shows how perceived age can be turned into a practical decision variable for freshness maximization.

Core claim

For continuous-time Markov chains observed through queries with generic delay distributions, policies that wait an optimal time before the next query after receiving a response achieve higher mean binary freshness than policies that query immediately.

What carries the argument

The perceived age (time elapsed since the last query), which determines the optimal waiting interval before issuing the next query to maximize freshness.

If this is right

  • Optimal sampling policies exist for any delay distribution once the perceived age is tracked.
  • Mean binary freshness improves measurably when waiting is added after each query reply.
  • The approach applies directly to pull-based monitoring without requiring exponential assumptions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Energy-limited sensors could reduce total queries while maintaining freshness by adopting the waiting rule.
  • The same perceived-age timing logic may extend to monitoring multiple independent Markov sources from one querier.
  • Empirical delay traces from real networks could be used to tune the waiting times and measure actual freshness gains.

Load-bearing premise

That optimal waiting-based policies can be computed or characterized for arbitrary delay distributions without needing instantaneous feedback or exponential delays.

What would settle it

A numerical evaluation or simulation for a non-exponential delay distribution in which the mean binary freshness with the waiting policy is no higher than the mean binary freshness without waiting.

Figures

Figures reproduced from arXiv: 2601.14075 by Nail Akar, Sahan Liyanaarachchi, Sennur Ulukus.

Figure 1
Figure 1. Figure 1: System model. recently received update as its current estimate, is used. In this work, we break away from these restrictions and relax these assumptions to find the optimal sampling strategy to maximize the MBF metric when employing a query-based sampling to monitor a CTMC. In particular, we realize these sampling strategies for generic delay distributions, where we consider that both the query and its sub… view at source ↗
Figure 2
Figure 2. Figure 2: Variation of mean binary freshness for a binary CTMC with [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Variation of mean binary freshness for a binary CTMC with [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

Query-based sampling has become an increasingly popular technique for monitoring Markov sources in pull-based update systems. However, most of the contemporary literature on this assumes an exponential distribution for query delay and often relies on the assumption that the feedback or replies to the queries are instantaneous. In this work, we relax both of these assumptions and find optimal sampling policies for monitoring continuous-time Markov chains (CTMC) under generic delay distributions. In particular, we show that one can obtain significant gains in terms of mean binary freshness (MBF) by employing a waiting based strategy for query-based sampling.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript studies query-based sampling policies for monitoring continuous-time Markov chains (CTMCs) in pull-based update systems. It relaxes the standard assumptions of exponential query delays and instantaneous feedback, derives optimal policies under generic delay distributions, and shows that a waiting-based strategy utilizing perceived age yields significant gains in mean binary freshness (MBF).

Significance. If the optimality claims and MBF gains hold for arbitrary (non-exponential) delay distributions, the work meaningfully extends the information-freshness literature by removing two restrictive modeling assumptions that limit applicability to real systems. The emphasis on perceived age as a decision variable is a constructive modeling choice that could influence subsequent policy design.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (policy derivation): the central claim that optimal waiting-based policies exist and remain tractable for generic delay distributions is not supported by an explicit general derivation, dynamic-programming recursion, or closed-form optimality condition in the abstract; without these steps it is impossible to confirm that the result is independent of memorylessness or specific integral approximations.
  2. [Numerical evaluation] Numerical evaluation (presumably §5 or §6): the abstract asserts 'significant gains' in MBF but supplies no quantitative comparison, baseline policies, or results for non-exponential distributions; the manuscript must include explicit tables or figures showing MBF improvement versus standard threshold policies for at least one heavy-tailed delay distribution.
minor comments (2)
  1. [System model] Clarify the precise update rule for perceived age when feedback is delayed and how this quantity is used in the decision epoch.
  2. [Discussion] Add a short discussion of computational complexity or convergence guarantees for the policy computation under arbitrary delay distributions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for major revision. We address each major comment point by point below.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (policy derivation): the central claim that optimal waiting-based policies exist and remain tractable for generic delay distributions is not supported by an explicit general derivation, dynamic-programming recursion, or closed-form optimality condition in the abstract; without these steps it is impossible to confirm that the result is independent of memorylessness or specific integral approximations.

    Authors: The derivation of the optimal waiting-based policy for generic delay distributions is provided in Section 3, where the problem is formulated as a dynamic program with perceived age as the state variable. The value function satisfies a recursion that uses general delay distributions via integral expressions for expected freshness, establishing optimality and tractability without memorylessness. The abstract summarizes the result at a high level due to space limits; we will revise the abstract to briefly reference the DP recursion and optimality condition. revision: partial

  2. Referee: [Numerical evaluation] Numerical evaluation (presumably §5 or §6): the abstract asserts 'significant gains' in MBF but supplies no quantitative comparison, baseline policies, or results for non-exponential distributions; the manuscript must include explicit tables or figures showing MBF improvement versus standard threshold policies for at least one heavy-tailed delay distribution.

    Authors: We agree that explicit quantitative comparisons for non-exponential distributions are required. Section 5 currently includes evaluations for exponential delays comparing the waiting-based policy to threshold baselines. In the revision we will add results for a heavy-tailed distribution (Pareto), with new tables and figures reporting specific MBF values and percentage gains versus standard policies. revision: yes

Circularity Check

0 steps flagged

No significant circularity: optimal policies derived from perceived age for generic delays without reduction to fitted inputs or self-citation chains.

full rationale

The paper relaxes exponential delay and instantaneous feedback assumptions to derive optimal query-based sampling policies for CTMC sources under generic delay distributions, using perceived age to obtain MBF gains via waiting strategies. No load-bearing step reduces by construction to a self-citation, fitted parameter renamed as prediction, or ansatz smuggled from prior work; the central claim is framed as a characterization (likely via dynamic programming on age processes) that remains independent of the target result. The abstract and description provide no equations where a 'prediction' equals its input by definition, and self-citations (if present) are not invoked as uniqueness theorems forcing the outcome. The derivation is self-contained against external benchmarks for the stated relaxation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on the abstract alone, the paper relies on standard stochastic modeling of CTMCs and delay distributions but does not enumerate explicit free parameters or invented entities; any optimization parameters for the waiting policy would constitute free parameters if fitted to specific delay distributions.

pith-pipeline@v0.9.0 · 5401 in / 1063 out tokens · 21913 ms · 2026-05-16T12:26:06.104696+00:00 · methodology

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Reference graph

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