Paper Citation Record · LEDGER

In-Context Learning of Temporal Point Processes with Foundation Inference Models

As of 5 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2509.24762.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.24762 v3

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:54:23.735517Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:15:42.984242Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T14:22:23.976178Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation ee8b88d7-63df-4327-a4ec-e24c2ba5e0a6 · outbound

This paper cites write newline.

In-Context Learning of Temporal Point Processes with Foundation Inference Models write newline

Reference 1

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Observation f9ce3041-7bea-4955-bdb6-7e83115ee257 · outbound

This paper cites Modeling financial contagion using mutually exciting jump processes.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Modeling financial contagion using mutually exciting jump processes

Reference 2

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 3

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Observation fc9ea536-5b78-413a-a6e3-1e53ca987f6a · outbound

This paper cites Neural ordinary differential equations.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Neural ordinary differential equations

Reference 4

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Observation 51c3d809-0adb-4949-97f1-61fec925441d · outbound

This paper cites Hawkes process modeling of covid-19 with mobility leading indicators and spatial covariates.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Hawkes process modeling of covid-19 with mobility leading indicators and spatial covariates

Reference 5

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Observation a7a56dff-6070-41be-9e34-02ba8953be9c · outbound

This paper cites Recurrent point review models.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Recurrent point review models

Reference 6

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Observation 3d9b0744-4b6b-4b5d-9cbe-165d1b40aee8 · outbound

This paper cites Dynamic Review-based Recommenders.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Dynamic Review-based Recommenders

Reference 7

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Observation a9622537-9ee3-4657-9f9d-f6f90b39b97a · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 8

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Observation cef3c118-c5f8-4a90-a2b1-b018f1bb9b45 · outbound

This paper cites ODEF ormer: Symbolic regression of dynamical systems with transformers.

In-Context Learning of Temporal Point Processes with Foundation Inference Models ODEF ormer: Symbolic regression of dynamical systems with transformers

Reference 9

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Observation 29bfb54e-2a8b-4145-9fdc-dee09f44cf61 · outbound

This paper cites Long horizon forecasting with temporal point processes.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Long horizon forecasting with temporal point processes

Reference 10

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This paper cites Recurrent marked temporal point processes: Embedding event history to vector.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Recurrent marked temporal point processes: Embedding event history to vector

Reference 11

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Observation 377c85ba-b1c7-4546-9280-f34e4f6f1e3f · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 12

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Observation 591e43e3-55bc-4e93-bc26-a358150c6bab · outbound

This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

In-Context Learning of Temporal Point Processes with Foundation Inference Models TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 13

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Observation e7c8fa21-6889-4b7e-82f4-4719f54bd709 · outbound

This paper cites EventFlow: Forecasting Temporal Point Processes with Flow Matching.

In-Context Learning of Temporal Point Processes with Foundation Inference Models EventFlow: Forecasting Temporal Point Processes with Flow Matching

Reference 14

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Observation 02b7bc66-9efc-47d2-8504-12c4028b93e3 · outbound

This paper cites Neural controlled differential equations for irregular time series.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Neural controlled differential equations for irregular time series

Reference 15

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This paper cites Poisson processes, volume 3.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Poisson processes, volume 3

Reference 16

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Observation 22bf25d5-2867-4c98-bfc4-9817aef7c655 · outbound

This paper cites Hawkes Processes.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Hawkes Processes

Reference 17

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Observation 0be3345f-c85d-4073-8cc1-2f7e9d1ee92a · outbound

This paper cites SNAP Datasets : Stanford large network dataset collection.

In-Context Learning of Temporal Point Processes with Foundation Inference Models SNAP Datasets : Stanford large network dataset collection

Reference 18

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Observation b0493014-d582-4e68-9bd1-b51c8d530c8c · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 19

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Observation 44915e8c-1f40-4e74-b94f-8f1ebf7e90f2 · outbound

This paper cites An Empirical Study: Extensive Deep Temporal Point Process.

In-Context Learning of Temporal Point Processes with Foundation Inference Models An Empirical Study: Extensive Deep Temporal Point Process

Reference 20

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Observation 873f6423-ca96-4e7a-8505-2201f688f541 · outbound

This paper cites Discovering latent network structure in point process data.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Discovering latent network structure in point process data

Reference 21

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Observation 03bf2b14-c156-4b7c-8368-3a71c8a49921 · outbound

This paper cites Decoupled Weight Decay Regularization.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Decoupled Weight Decay Regularization

Reference 22

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This paper cites u dke, Marin Bilo s , Oleksandr Shchur, Marten Lienen, and Stephan G \.

In-Context Learning of Temporal Point Processes with Foundation Inference Models u dke, Marin Bilo s , Oleksandr Shchur, Marten Lienen, and Stephan G \

Reference 23

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This paper cites Variational bayesian inference for nonlinear hawkes process with gaussian process self-effects.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Variational bayesian inference for nonlinear hawkes process with gaussian process self-effects

Reference 24

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Observation b9c3c1bc-e67b-490c-95ff-b02437d9352f · outbound

This paper cites Amortized in-context mixed effect transformer models: A zero-shot approach for pharmacokinetics.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Amortized in-context mixed effect transformer models: A zero-shot approach for pharmacokinetics

Reference 25

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Observation cd026246-6100-48eb-a34f-88d6c32b440d · outbound

This paper cites The neural hawkes process: A neurally self-modulating multivariate point process.

In-Context Learning of Temporal Point Processes with Foundation Inference Models The neural hawkes process: A neurally self-modulating multivariate point process

Reference 26

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This paper cites Imputing missing events in continuous-time event streams.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Imputing missing events in continuous-time event streams

Reference 27

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Observation 0fb3944f-b1b3-4637-ac47-3d17473b79b0 · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Transformer embeddings of irregularly spaced events and their participants

Reference 28

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Observation 5490c4f4-0dd1-4685-b345-e9dd6cae40cc · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Transformers can do bayesian inference

Reference 29

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This paper cites Position: The Future of Bayesian Prediction Is Prior-Fitted.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Position: The Future of Bayesian Prediction Is Prior-Fitted

Reference 30

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This paper cites Justifying recommendations using distantly-labeled reviews and fine-grained aspects.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Justifying recommendations using distantly-labeled reviews and fine-grained aspects

Reference 31

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 32

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Observation b2bf15f0-8243-49e7-a430-54f90fb61a77 · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Learning deep generative models for queuing systems

Reference 33

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Decomposable transformer point processes

Reference 34

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Bayesian inference for hawkes processes

Reference 35

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Observation 8f65fa41-5b88-4f7c-a734-353317f505be · outbound

This paper cites Lecture Notes: Temporal Point Processes and the Conditional Intensity Function.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Lecture Notes: Temporal Point Processes and the Conditional Intensity Function

Reference 36

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In-Context Learning of Temporal Point Processes with Foundation Inference Models In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

Reference 37

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Zero-shot imputation with foundation inference models for dynamical systems

Reference 38

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This paper cites Intensity-free learning of temporal point processes.

In-Context Learning of Temporal Point Processes with Foundation Inference Models Intensity-free learning of temporal point processes

Reference 39

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Multi-time attention networks for irregularly sampled time series

Reference 40

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations

Reference 41

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This paper cites A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

In-Context Learning of Temporal Point Processes with Foundation Inference Models A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects

Reference 42

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Attention is all you need

Reference 43

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Observation fd3c52c1-2828-4a1c-b31f-2f7fd30f8eb9 · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Learning granger causality from instance-wise self-attentive hawkes processes

Reference 44

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Wasserstein Learning of Deep Generative Point Process Models

Reference 45

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Learning granger causality for hawkes processes

Reference 46

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Zhang, and Hongyuan Mei

Reference 47

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Zhang, Qingsong Wen, JUN ZHOU, and Hongyuan Mei

Reference 48

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Transformer Embeddings of Irregularly Spaced Events and Their Participants

Reference 49

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Interacting diffusion processes for event sequence forecasting

Reference 50

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Self-attentive H awkes process

Reference 51

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Seismic: A self-exciting point process model for predicting tweet popularity

Reference 52

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Learning triggering kernels for multi-dimensional hawkes processes

Reference 53

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Learning tree-based deep model for recommender systems

Reference 54

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Transformer Hawkes Process

Reference 55

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In-Context Learning of Temporal Point Processes with Foundation Inference Models @esa (Ref

Reference 56

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Observation 46b494c5-a903-4911-92ca-2fe7de471eb0 · outbound

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 57

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In-Context Learning of Temporal Point Processes with Foundation Inference Models Unresolved cited work

Reference 58

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Pith citing papers

Observation b3174438-c801-4bd7-ac9f-3b151690a407 · inbound

Foundation Inference Models for Ordinary Differential Equations cites this paper.

Foundation Inference Models for Ordinary Differential Equations In-Context Learning of Temporal Point Processes with Foundation Inference Models

Reference 2024

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Observation 69ff1a0d-941e-4908-a3fc-a876e17754df · inbound

SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference cites this paper.

SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference In-Context Learning of Temporal Point Processes with Foundation Inference Models

Reference 3

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