arxiv: 2604.26970 · v1 · submitted 2026-04-22 · 💻 cs.IR · cs.AI· cs.LG· q-bio.QM

Recognition: unknown

Not All Memories Age the Same: Autodiscovery of Adaptive Decay in Knowledge Graphs

Mandar Karhade

Authors on Pith no claims yet

Pith reviewed 2026-05-09 23:56 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.LGq-bio.QM

keywords knowledge graphstemporal decaysurvival analysisadaptive forgettinghierarchical modelsinformation retrievalvalue supersessionLindy effect

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The pith

Knowledge graphs should replace uniform decay with a learned hierarchical surface driven by observation velocity and value volatility.

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

The paper argues that standard knowledge graphs apply the same forgetting curve to every fact, which fails because facts of different types persist for different lengths of time. It replaces this with a continuous decay surface that adapts at three levels: broad domain patterns, setting-specific adjustments, and individual entity behavior. The surface is built automatically from observed lifetimes using survival analysis that treats value replacement as the key event rather than simple reappearance. This matters for retrieval because the system can then surface what is still likely to be current at the moment of a query instead of treating all stored edges as equally fresh.

Core claim

Uniform decay is misspecified for knowledge graphs because different knowledge types exhibit distinct temporal dynamics. The proposed hierarchical framework parameterizes a decay surface by two orthogonal signals, velocity and volatility, and decomposes it into domain-level, context-level, and entity-level parameters. All parameters are recovered from data alone via survival analysis on value-supersession events, without predefined taxonomies. Experiments confirm that the learned clusters recover planted structure and that heterogeneous decay produces large gains over uniform baselines on both synthetic graphs and real Wikipedia and clinical records.

What carries the argument

A three-level hierarchical decay surface parameterized by velocity (observation frequency) and volatility (embedding-distance change between successive values), with parameters emerging from survival analysis on supersession lifetimes.

If this is right

Predicates naturally separate into permanent and transient groups without manual taxonomy construction.
Retrieval ranking can weight each edge by its individually estimated survival probability at query time.
The Lindy effect emerges automatically across learned clusters, with longer-surviving values showing lower future decay rates.
Adding each successive hierarchy level produces measurable improvement in matching observed value lifetimes.

Where Pith is reading between the lines

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

The same velocity-volatility decomposition could be applied to other timestamped structured data such as version histories or event streams.
If the supersession signal proves robust, the method offers a route to make static knowledge bases in language-model retrieval systems dynamically age their contents.
The approach implies that indexes in temporal databases might benefit from storing not only timestamps but also learned decay surfaces per relation type.

Load-bearing premise

Value supersession can be reliably separated from mere re-observation using only embedding distance, so that survival analysis on the resulting lifetimes yields the true decay parameters without any domain rules.

What would settle it

If independent human labels of fact persistence on the same Wikipedia or EHR data show no alignment with the velocity-volatility clusters recovered by the model, the claim that the hierarchy discovers meaningful adaptive decay would be falsified.

Figures

Figures reproduced from arXiv: 2604.26970 by Mandar Karhade.

**Figure 2.** Figure 2: Decay surface for the volatile measurements cluster (synthetic data). The shelf life [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗

read the original abstract

Knowledge graphs used for retrieval treat all facts as equally current. Existing temporal approaches apply uniform decay, using a single forgetting curve regardless of knowledge type. We show this is fundamentally misspecified: different knowledge types exhibit different temporal dynamics, and the core retrieval problem is not latency or throughput but identifying what is important at query time. We propose a hierarchical framework that replaces uniform decay with a continuous decay surface parameterized by two orthogonal signals: velocity (how frequently a concept is observed) and volatility (how much the value changes between observations, measured via embedding distance). The decay surface is decomposed into three learnable levels: domain-level parameters capture universal patterns (some predicates are inherently permanent, others inherently transient), context-level parameters capture setting-dependent variation, and entity-level adaptation personalizes decay to specific subjects. All parameters emerge from data through survival analysis on observed value lifetimes, requiring no predefined taxonomies or domain expertise. We formulate edge lifetime as a survival problem where the event is value supersession (a meaningfully different value replacing the current one), distinct from mere re-observation. Experiments on synthetic temporal knowledge graphs demonstrate recovery of planted hierarchical parameters (HDBSCAN ARI = 1.0). Validation on 107 Wikipedia articles and 1,163 patient records from the Synthea clinical EHR simulator shows that velocity-volatility clusters emerge naturally, align with observable persistence patterns, and near-universally exhibit the Lindy effect (Weibull shape k < 1). Uniform decay performs 18x worse than no temporal weighting. Heterogeneous decay recovers from this, with each hierarchy level contributing measurable improvement.

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.

Desk Editor's Note private letter to a colleague

The paper introduces a hierarchical velocity-volatility decay model for knowledge graphs discovered through survival analysis, with clean synthetic recovery of parameters but real-data claims resting on an unvalidated embedding-distance proxy for supersession.

read the letter

The main takeaway is that uniform decay in knowledge graphs is too crude because facts have different lifetimes, and this work tries to learn a continuous decay surface from velocity and volatility signals at three levels without needing hand-built taxonomies. The synthetic experiments recover the planted hierarchical parameters with perfect HDBSCAN ARI, which is a direct and useful check that the decomposition can be identified when the generative process matches the model. On the Wikipedia and Synthea data the clusters align with observable persistence and show the Lindy effect via Weibull fits with shape under 1, and each hierarchy level adds measurable lift over uniform baselines. That part is worth noting because it gives a concrete alternative to single-curve forgetting. The central soft spot is the definition of the survival event itself. Supersession is detected by embedding distance exceeding some implicit threshold, yet the abstract gives no ground-truth validation that this separates real value changes from noise, synonym swaps, or minor rephrasings. If the proxy is unreliable, the recovered clusters, the Lindy fits, and the reported gains become tied to the embedding model rather than the underlying temporal dynamics. The 18x improvement claim also appears without error bars or statistical tests, so its robustness is hard to judge from the summary. This is aimed at researchers building retrieval over temporal knowledge graphs in information retrieval or applied AI settings. Someone already working on adaptive forgetting or survival analysis on graphs would find the framing and the synthetic validation useful. It deserves peer review because the synthetic results are reproducible and the problem it targets is real, though any referee would need to see explicit checks on the supersession proxy and full experimental statistics before the real-data conclusions can be taken as settled.

Referee Report

2 major / 2 minor

Summary. The paper claims that uniform decay functions in knowledge graphs are misspecified because different knowledge types exhibit distinct temporal dynamics; it introduces a hierarchical velocity-volatility decay surface (domain-, context-, and entity-level parameters) learned via survival analysis on value supersession events detected by embedding distance, shows perfect recovery of planted parameters on synthetic data (HDBSCAN ARI=1.0), and reports that real data from Wikipedia and clinical records exhibit the Lindy effect with heterogeneous decay yielding an 18x improvement over uniform decay.

Significance. If the central results hold after validation, the work offers a data-driven alternative to manual taxonomies for adaptive forgetting in temporal retrieval, with the observed Lindy-effect Weibull fits and hierarchical gains providing a concrete, falsifiable basis for improving query-time importance ranking in dynamic KGs.

major comments (2)

[Abstract (survival analysis formulation)] The proxy for value supersession events (embedding distance between successive observations) is untested against ground truth and is load-bearing for the claim that parameters emerge purely from data without domain expertise or predefined taxonomies; if the distance metric misclassifies re-observations as supersessions (e.g., due to synonym swaps or minor rephrasing), the recovered clusters, Lindy-effect fits, and reported gains become artifacts of the embedding model rather than true dynamics.
[Abstract (experimental validation)] The 18x improvement claim and the statement that 'each hierarchy level contributing measurable improvement' lack error bars, statistical tests, or ablation details, undermining verifiability of the superiority over uniform decay (which itself is reported as 18x worse than no temporal weighting).

minor comments (2)

[Abstract] Abstract provides no mention of confidence intervals, p-values, or sample sizes for the real-data results, reducing clarity on the strength of the Lindy-effect and improvement claims.
[Abstract] Notation for the 'velocity-volatility decay surface' and the three-level decomposition could be formalized earlier with an equation to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's report. We address the major comments point by point below, providing clarifications and indicating where revisions will be made to the manuscript.

read point-by-point responses

Referee: The proxy for value supersession events (embedding distance between successive observations) is untested against ground truth and is load-bearing for the claim that parameters emerge purely from data without domain expertise or predefined taxonomies; if the distance metric misclassifies re-observations as supersessions (e.g., due to synonym swaps or minor rephrasing), the recovered clusters, Lindy-effect fits, and reported gains become artifacts of the embedding model rather than true dynamics.

Authors: We agree that the embedding-based proxy for supersession events is a key component and that it has not been validated against explicit ground-truth labels on real data, as such labels are typically unavailable. The synthetic data experiments, however, provide strong evidence that the method correctly recovers the underlying hierarchical parameters when the supersession events are known by design. On the real datasets, the discovered clusters correspond to semantically meaningful distinctions in temporal behavior, supporting that the approach captures genuine dynamics rather than artifacts. To address the concern, we will include in the revision a discussion of the proxy's limitations and an analysis of sensitivity to the choice of embedding model (e.g., comparing different pre-trained embeddings). This maintains the data-driven claim while acknowledging the measurement role of embeddings. revision: partial
Referee: The 18x improvement claim and the statement that 'each hierarchy level contributing measurable improvement' lack error bars, statistical tests, or ablation details, undermining verifiability of the superiority over uniform decay (which itself is reported as 18x worse than no temporal weighting).

Authors: The referee correctly identifies that the experimental results would be strengthened by additional statistical details. In the revised manuscript, we will add error bars to the reported performance metrics, conduct appropriate statistical tests to confirm the significance of the improvements, and expand the ablation studies to explicitly show the contribution of each level in the hierarchy. We will also provide more context on the baseline comparisons, including the uniform decay and no temporal weighting cases. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The derivation learns decay-surface parameters from survival analysis on observed lifetimes, where the supersession event is defined via embedding distance and the hierarchy is a fixed structural decomposition into domain/context/entity levels. Synthetic experiments recover planted parameters with HDBSCAN ARI = 1.0, and real-data validation shows emergent clusters and Lindy-effect Weibull fits without any reported step that equates the final performance gains or autodiscovery claims to the input definitions by construction. The comparison to uniform decay and the per-level ablation improvements are measured outcomes rather than tautological renamings or fitted-input predictions. The framework is therefore self-contained against its external benchmarks.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 1 invented entities

The framework rests on learnable hierarchical parameters derived from survival analysis and two new signals (velocity and volatility) without external priors or taxonomies.

free parameters (3)

domain-level parameters
Capture universal predicate patterns, learned from data
context-level parameters
Capture setting-dependent variation, learned from data
entity-level parameters
Personalize decay to specific subjects, learned from data

axioms (2)

domain assumption Value supersession (meaningfully different replacement) is the survival event, distinct from re-observation
Core definition of edge lifetime in the survival formulation
domain assumption Lindy effect (Weibull shape k < 1) holds for knowledge persistence
Observed outcome used to validate clusters

invented entities (1)

velocity-volatility decay surface no independent evidence
purpose: Continuous parameterization of adaptive decay
Defined by observation frequency and embedding-distance change

pith-pipeline@v0.9.0 · 5596 in / 1484 out tokens · 37488 ms · 2026-05-09T23:56:21.191117+00:00 · methodology

discussion (0)

Reference graph

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