Paper Citation Record · LEDGER

Bounding Distributional Shifts in World Modeling through Novelty Detection

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2508.06096.

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

pith.paper-citation-record.v1
2508.06096 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:58:43.308601Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T22:35:46.126714Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T22:38:22.204351Z

Reference resolution

34 of 34 outbound references displayed

verified exact3
verified fuzzy10
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73ada18a-8390-416e-8c5f-b3a90192fff5 · outbound

This paper cites World Models.

Bounding Distributional Shifts in World Modeling through Novelty Detection World Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:39.173689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eac8ae1b-c060-48dc-a0ed-5eee79b50630 · outbound

This paper cites Deep learning, reinforcement learning, and world models,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Deep learning, reinforcement learning, and world models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:47.149224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26b3178a-ceea-454d-ac3b-5ba827ecc874 · outbound

This paper cites Estimation of inertial parameters of manipulator loads and links,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Estimation of inertial parameters of manipulator loads and links,

Reference 3

Resolution: verified exact; doi, observed 2026-08-05T22:58:43.613773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 500a2d07-6ef7-4440-b42a-bbda1ab49a12 · outbound

This paper cites Efficient optimization for autonomous robotic manipulation of natural objects,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Efficient optimization for autonomous robotic manipulation of natural objects,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:46.892452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 227695d1-4a93-4d21-aa4f-ef8aca2766cd · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Bounding Distributional Shifts in World Modeling through Novelty Detection Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:39.646700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d76ed785-7a59-4c6a-b273-f1a97b42c051 · outbound

This paper cites The class imbalance problem in deep learning,.

Bounding Distributional Shifts in World Modeling through Novelty Detection The class imbalance problem in deep learning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:39.811227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 869f924e-7089-47ce-a88c-7e9b0182fc42 · outbound

This paper cites Fast model identification via physics engines for improved policy search,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Fast model identification via physics engines for improved policy search,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:46.648100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03c25d57-491a-4aea-ba5e-aad97726a38f · outbound

This paper cites Automatic vs. manual feature engineering for anomaly detection of drinking-water quality,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Automatic vs. manual feature engineering for anomaly detection of drinking-water quality,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:40.020354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03edf1d8-da26-4537-891d-b02e1feccf16 · outbound

This paper cites DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning.

Bounding Distributional Shifts in World Modeling through Novelty Detection DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:40.131604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b09ddd4-2acf-4000-b175-43413ada229e · outbound

This paper cites Exploring Model-based Planning with Policy Networks.

Bounding Distributional Shifts in World Modeling through Novelty Detection Exploring Model-based Planning with Policy Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:40.285014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1dfd49d-3e45-492d-8fed-bd514783d995 · outbound

This paper cites On the role of planning in model-based deep reinforcement learning.

Bounding Distributional Shifts in World Modeling through Novelty Detection On the role of planning in model-based deep reinforcement learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:40.394538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29eceac2-9bbe-40eb-9a36-abdbc4568827 · outbound

This paper cites Model-Based Visual Planning with Self-Supervised Functional Distances.

Bounding Distributional Shifts in World Modeling through Novelty Detection Model-Based Visual Planning with Self-Supervised Functional Distances

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:40.526256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e11563b-069b-4ae5-a8f3-a4e4528db051 · outbound

This paper cites Learning to predict vehicle trajectories with model-based planning,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Learning to predict vehicle trajectories with model-based planning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:46.371357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6732ffcc-e4c4-4d88-958e-8fd37cefabb1 · outbound

This paper cites Optimal cost design for model predictive control,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Optimal cost design for model predictive control,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:46.089874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 178b5e0c-09a6-47f2-bce6-95dad97bd318 · outbound

This paper cites Recurrent world models facilitate policy evolution,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Recurrent world models facilitate policy evolution,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:45.850776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c10b8802-36e9-4fa3-a722-55fff8b5818f · outbound

This paper cites Combining physics and deep learning to learn continuous-time dynamics models,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Combining physics and deep learning to learn continuous-time dynamics models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.119648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08dd28f5-5719-4310-872a-91b341e0bfdf · outbound

This paper cites Physically Interpretable World Models via Weakly Supervised Representation Learning.

Bounding Distributional Shifts in World Modeling through Novelty Detection Physically Interpretable World Models via Weakly Supervised Representation Learning

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:58:44.398366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4811cc60-00c2-4d3a-855c-db6f784593b1 · outbound

This paper cites WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens.

Bounding Distributional Shifts in World Modeling through Novelty Detection WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.359450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3314b61-d739-48c7-8cc2-44719cea2a32 · outbound

This paper cites EVA: An Embodied World Model for Future Video Anticipation.

Bounding Distributional Shifts in World Modeling through Novelty Detection EVA: An Embodied World Model for Future Video Anticipation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.513942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddff031b-39e0-4ee5-88c0-d274d08b0ac1 · outbound

This paper cites Combating the Compounding-Error Problem with a Multi-step Model.

Bounding Distributional Shifts in World Modeling through Novelty Detection Combating the Compounding-Error Problem with a Multi-step Model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.604776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d492de02-ac9f-4cc9-87bd-eb87c5dbaaf1 · outbound

This paper cites An Analysis of Frame-skipping in Reinforcement Learning.

Bounding Distributional Shifts in World Modeling through Novelty Detection An Analysis of Frame-skipping in Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.744773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b6e551b-05d4-48d6-9ad5-cd889e3e0634 · outbound

This paper cites Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning.

Bounding Distributional Shifts in World Modeling through Novelty Detection Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.901345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1144418c-491a-4159-aaef-453ff23f890c · outbound

This paper cites Variational autoencoder based anomaly detection using reconstruction probability,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Variational autoencoder based anomaly detection using reconstruction probability,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:41.995207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 461b61a7-b248-4e58-ba58-6725c8a8a1b5 · outbound

This paper cites Variational Autoencoder for Anomaly Detection: A Comparative Study.

Bounding Distributional Shifts in World Modeling through Novelty Detection Variational Autoencoder for Anomaly Detection: A Comparative Study

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:42.155024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87f533f5-da55-43b4-9445-89765378b0dd · outbound

This paper cites Anomaly-based intrusion detection from network flow features using variational autoencoder,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Anomaly-based intrusion detection from network flow features using variational autoencoder,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:45.555192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 974659f5-c28a-4bae-8698-d255ed48ce38 · outbound

This paper cites Learning to discover anomalous spatiotemporal trajectory via open-world state space model,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Learning to discover anomalous spatiotemporal trajectory via open-world state space model,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:45.314744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bbec4ebe-6dec-4f8e-ae59-9e8ae3af1cb9 · outbound

This paper cites Real-Time Anomaly Detection and Reactive Planning with Large Language Models.

Bounding Distributional Shifts in World Modeling through Novelty Detection Real-Time Anomaly Detection and Reactive Planning with Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:42.566961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d19bc94-29d7-4c9c-9301-6460c558a65b · outbound

This paper cites Enhancing reconstruction-based out-of-distribution detection in brain mri with model and metric ensembles,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Enhancing reconstruction-based out-of-distribution detection in brain mri with model and metric ensembles,

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-05T22:58:44.075112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c5070bc-f43b-4204-9911-d7a771e9f5bf · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Bounding Distributional Shifts in World Modeling through Novelty Detection DINOv2: Learning Robust Visual Features without Supervision

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:42.769230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 361075b4-7ea8-41c7-9cc9-9bdb6b816fd3 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Bounding Distributional Shifts in World Modeling through Novelty Detection An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:42.873942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d01dc81d-f702-47f5-a7d3-f00896d7ff93 · outbound

This paper cites An introduction to variational autoencoders,.

Bounding Distributional Shifts in World Modeling through Novelty Detection An introduction to variational autoencoders,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:42.973338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebcf0150-08d4-43e1-baa5-afa9763f2f18 · outbound

This paper cites Deep convolutional inverse graphics network,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Deep convolutional inverse graphics network,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:45.071630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d0e82595-a484-49ef-a704-a7a3d7964fe3 · outbound

This paper cites Deconvo- lutional networks,.

Bounding Distributional Shifts in World Modeling through Novelty Detection Deconvo- lutional networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:44.779581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2b0caabd-fabd-4648-a9cd-60cc71e0bdbf · outbound

This paper cites Neural Discrete Representation Learning.

Bounding Distributional Shifts in World Modeling through Novelty Detection Neural Discrete Representation Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:43.308601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 186dfbe8-4e27-48ad-8ce9-575ec890ce2c · inbound

Safety, Security, and Cognitive Risks in World Models cites this paper.

Safety, Security, and Cognitive Risks in World Models Bounding Distributional Shifts in World Modeling through Novelty Detection

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:38:22.205717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.