Paper Citation Record · LEDGER

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.10242.

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

pith.paper-citation-record.v1
2506.10242 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:35:23.777947Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy24
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8abcc4ae-5bd8-497d-986b-f29347edaba6 · outbound

This paper cites Z-forcing: Training stochastic recurrent networks.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Z-forcing: Training stochastic recurrent networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.313515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff8814c9-257e-43c3-b423-4a781febe005 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos nuscenes: A multi- modal dataset for autonomous driving

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.304718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00a75295-8a3c-4782-b297-c9b9655c3f1b · outbound

This paper cites End-to- end object detection with transformers.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos End-to- end object detection with transformers

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.295481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2b5b1d6-47b0-4bf8-afd0-2402e801fb73 · outbound

This paper cites Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.639725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72982439-0b0f-472f-916d-58515dc24776 · outbound

This paper cites Adamixer: A fast-converging query-based object detector.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Adamixer: A fast-converging query-based object detector

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.286582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 072cf3c4-d079-4339-963f-eb65167fe5e2 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.650763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63ae3aee-01c3-463a-ba75-06aba72a5d6d · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Efficiently Modeling Long Sequences with Structured State Spaces

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.653942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae16e8a2-4e0a-442d-8e03-373b9931890a · outbound

This paper cites Deep residual learning for image recognition.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Deep residual learning for image recognition

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.657336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fabe1e0-660c-48a4-b6ff-2cceceddac60 · outbound

This paper cites BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.660995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cf04c9f-8c4a-4f0f-8f32-235010ec904a · outbound

This paper cites BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.664114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b5c54e6-2cea-436f-a06a-6ea7aa32d0d4 · outbound

This paper cites BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.667697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf6c6e3-ad8e-4a31-87f5-c700edf0e74e · outbound

This paper cites Leveraging vision-centric multi-modal expertise for 3d object detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Leveraging vision-centric multi-modal expertise for 3d object detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.271836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90608fdf-c32b-43da-a9c3-1e8f73556615 · outbound

This paper cites Polarformer: Multi- camera 3d object detection with polar transformer.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Polarformer: Multi- camera 3d object detection with polar transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.262437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 552a9ca0-bc53-46ff-8b5e-0ac4cdfe7d7a · outbound

This paper cites Centermask: Real- time anchor-free instance segmentation.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Centermask: Real- time anchor-free instance segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.252214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03a5c6c0-ad48-4a67-be9d-51d07e94ec7f · outbound

This paper cites VideoMamba: State Space Model for Efficient Video Understanding.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos VideoMamba: State Space Model for Efficient Video Understanding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.682312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5653bad-36d0-470b-aa78-782cb74e9dfd · outbound

This paper cites Unifying voxel-based representation with transformer for 3d object detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Unifying voxel-based representation with transformer for 3d object detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.242284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 898d26c0-a53e-40aa-8407-e36678c0114b · outbound

This paper cites Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.232330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2c88619-72eb-446e-84b6-371ab67aa6ab · outbound

This paper cites Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.222857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 376b5c64-a8c1-4d66-9b7f-6a753f60a4c9 · outbound

This paper cites Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.214004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2cea3e93-9536-48fb-a519-210cb9e6b785 · outbound

This paper cites Bevnext: Reviving dense bev frameworks for 3d object de- tection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Bevnext: Reviving dense bev frameworks for 3d object de- tection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.204687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6be02f11-7fa4-40b4-ac06-aaf02d211a87 · outbound

This paper cites Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.703406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71262ffe-2505-487d-ae8a-3456c748107b · outbound

This paper cites Sparse4D v2: Recurrent Temporal Fusion with Sparse Model.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Sparse4D v2: Recurrent Temporal Fusion with Sparse Model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.707571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfa006e2-065c-4f5f-ab7e-891dc5cab592 · outbound

This paper cites Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:35:23.884066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 381d2629-124c-4b34-ad60-c7da18e7324d · outbound

This paper cites Sparsebev: High-performance sparse 3d object de- tection from multi-camera videos.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Sparsebev: High-performance sparse 3d object de- tection from multi-camera videos

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.195665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1e2e4a1-68cf-4148-b0d7-19e5d0f63fbc · outbound

This paper cites Petr: Position embedding transformation for multi-view 3d object detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Petr: Position embedding transformation for multi-view 3d object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.185889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 43cdd979-3738-4025-8915-e45aa45801cc · outbound

This paper cites Petrv2: A unified framework for 3d perception from multi-camera images.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Petrv2: A unified framework for 3d perception from multi-camera images

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.175069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff040686-4235-4740-89bb-e593e723f301 · outbound

This paper cites VMamba: Visual State Space Model.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos VMamba: Visual State Space Model

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.724665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7600ef8-104e-4a9a-862d-ff40f676d13b · outbound

This paper cites LION: Linear Group RNN for 3D Object Detection in Point Clouds.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos LION: Linear Group RNN for 3D Object Detection in Point Clouds

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.728353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3e25ff0-c383-489c-bf06-a17606c4e8e5 · outbound

This paper cites Is pseudo-lidar needed for monocular 3d object detection? In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 3142–3152,.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Is pseudo-lidar needed for monocular 3d object detection? In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 3142–3152,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.165043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 41c17b56-c10d-49fc-8e8f-2de7ac7eb03e · outbound

This paper cites Time will tell: New outlooks and a baseline for temporal multi- view 3d object detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Time will tell: New outlooks and a baseline for temporal multi- view 3d object detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.154869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd05786b-db7d-4383-acb0-117340bb2384 · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.144677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8312d47f-2751-4b0b-91e2-4bc6808db416 · outbound

This paper cites Amixer: Adaptive weight mixing for self-attention free vi- sion transformers.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Amixer: Adaptive weight mixing for self-attention free vi- sion transformers

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.134679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f943a34d-ffaf-479f-b21e-2ccb0738c025 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Simplified State Space Layers for Sequence Modeling

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.744886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22e6d20e-9f48-43ba-8909-bafc9fa77582 · outbound

This paper cites Feedback in Imitation Learning: The Three Regimes of Covariate Shift.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Feedback in Imitation Learning: The Three Regimes of Covariate Shift

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.748313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 773f210f-2485-48c6-b672-13bf217709fa · outbound

This paper cites Sparse r-cnn: End-to-end ob- ject detection with learnable proposals.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Sparse r-cnn: End-to-end ob- ject detection with learnable proposals

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.751851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cc512b2-bae8-47ff-9679-7bcf4584195e · outbound

This paper cites Exploring object-centric temporal modeling for efficient multi-view 3d object detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Exploring object-centric temporal modeling for efficient multi-view 3d object detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.116419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 145a6662-c094-4ccf-8f7f-689f421b461c · outbound

This paper cites Detr3d: 3d object detection from multi-view images via 3d-to-2d queries.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Detr3d: 3d object detection from multi-view images via 3d-to-2d queries

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.106135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c1f1d61-21c1-4b85-80ac-4925e43d6352 · outbound

This paper cites Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective su- pervision.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective su- pervision

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.095452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6978be83-7ce9-40b7-8caf-c875f4e59fa7 · outbound

This paper cites Futuredepth: Learning to predict the future improves video depth estimation.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Futuredepth: Learning to predict the future improves video depth estimation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:24.084632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8f0321b-8d7e-4c0f-bbc6-d42b361ea5c3 · outbound

This paper cites Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.767553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d2795a2-1547-4d27-9d97-e9d59e7b803f · outbound

This paper cites Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.770789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7506a64c-3eb8-46a9-8abc-03f141def9c0 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:35:23.774219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 328f2d44-07b8-4ae2-a427-37310b7a8cee · outbound

This paper cites Temporal enhanced training of multi-view 3d object detector via historical object prediction.

DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos Temporal enhanced training of multi-view 3d object detector via historical object prediction

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:35:23.986458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.