Pith. sign in

Paper Citation Record · LEDGER

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift

As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2607.09695.

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

pith.paper-citation-record.v1
2607.09695 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T17:36:18.728469Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

19 of 19 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 123a38d1-09cd-489e-a4a0-6a37a3c65355 · outbound

This paper cites AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:c270af6a7a2fa3615b16b7efd9245c9bdb08ef6ac836a23f4c6ed59d9ca53f89

Observation de4c49a7-ab5a-43a6-a374-64320e0ae549 · outbound

This paper cites Exploring efficiency frontiers of thinking budget in medical reasoning: Scaling laws between computational resources and reasoning quality.arXiv:2508.12140,.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Exploring efficiency frontiers of thinking budget in medical reasoning: Scaling laws between computational resources and reasoning quality.arXiv:2508.12140,

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:8d5e0acc2d80073909104223dbde512e08081676b533c9c71df847ecb51bca94

Observation 3f4dbff7-7813-4870-a450-fb26a879a030 · outbound

This paper cites Exploiting shared represen- tations for personalized federated learning.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Exploiting shared represen- tations for personalized federated learning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:d9e7f582cf2000ece107beb5659883d589585efe1d8392383b09c7fe467f7d09

Observation 6bd2006e-68c5-4a3e-86d6-ff89cb2b6d00 · outbound

This paper cites Graph inference towards icd coding.arXiv preprint arXiv:2601.07496,.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Graph inference towards icd coding.arXiv preprint arXiv:2601.07496,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:1a0b2258340f5137e6e0d5dbdded26cca6e9d1eee77470d9d5d45a694d72b1d0

Observation c2a181a1-e24c-49f3-bc43-b61ff67f4df9 · outbound

This paper cites Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:2157806e65535650fb5b7ee56bbda969f4878e7929c84f9bc943c0ad7d09e9c9

Observation ef808da9-45e2-45ef-a21f-22b8e4437f63 · outbound

This paper cites CoRe-Code: Collaborative Reinforcement Learning for Code Generation.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift CoRe-Code: Collaborative Reinforcement Learning for Code Generation

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:2e32c39509d835cb5ed7d824b8e532dc46736301c0b11ff62e6d2ea492ac5182

Observation c3266a6a-7378-434e-9358-d5d00b61e173 · outbound

This paper cites Multi-agent medical decision consensus matrix system: An intelligent collaborative framework for oncology mdt consultations.arXiv preprint arXiv:2512.14321,.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Multi-agent medical decision consensus matrix system: An intelligent collaborative framework for oncology mdt consultations.arXiv preprint arXiv:2512.14321,

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:f25c33d95618486bdf6d750468c1bd0811c57cfb6314ed6936201e566c980a62

Observation e9ae4c29-c84c-4bf4-ac33-a62da10bd5e7 · outbound

This paper cites Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:7fe20b39a3f9d01e76dacc3d8097116e1ce31bccfd107c81bdf7a4d69f3ce0ba

Observation 760acaa2-e914-4b82-8eef-412f45d0097c · outbound

This paper cites Abductive inference in retrieval-augmented language models: Generating and validating missing premises, 2025a.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Abductive inference in retrieval-augmented language models: Generating and validating missing premises, 2025a

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:266b43e376474533fae5897cc800f353f2b7e267148807329600d61dc0aad5a9

Observation af763e75-d29c-45d4-bb21-b9323cc2d457 · outbound

This paper cites From text to multimodality: Exploring the evolution and impact of large language models in medical practice, 2024a.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift From text to multimodality: Exploring the evolution and impact of large language models in medical practice, 2024a

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:9cf6da23800696d9863db2dd6f96f4ddcfdf97a62ef432f3f4c5b2fa5404fb78

Observation 83f746c3-0836-4f25-a829-5a2bb9779e30 · outbound

This paper cites Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework

Reference 11

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:74da3b6baaa89aa39579abc71a29d3ede812c4cfdd672e582987c660b747a434

Observation 6ddafcbb-7c26-449c-9d38-7de7c4b3eafd · outbound

This paper cites Huanhuan Wang, Xiao Zhang, Youbing Xia, and Xiang Wu.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Huanhuan Wang, Xiao Zhang, Youbing Xia, and Xiang Wu

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:db3a19badf25013894cc51c8ad18a7945d66e9a3b89118563480b0280cb614dc

Observation 621cf58c-e0d6-42ea-81c3-d49222504b18 · outbound

This paper cites Deep Learning Model Security: Threats and Defenses.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Deep Learning Model Security: Threats and Defenses

Reference 13

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:6d2b5232b078937ef21fe0b17de6603802d153250cc557f1f9ea898ee2947ac4

Observation 58dfb0a0-46e7-4d5f-b52f-68758983d879 · outbound

This paper cites Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:f47b2de813e27c758dd2a7373649c38c557b431c2389ca2b6922f135f7f778c7

Observation 135a8c4b-da03-4a58-ba4e-4b2fde954b0f · outbound

This paper cites Lumina-dimoo: An omni diffusion large language model for multi-modal generation and understanding.arXiv preprint arXiv:2510.06308, 2025a.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Lumina-dimoo: An omni diffusion large language model for multi-modal generation and understanding.arXiv preprint arXiv:2510.06308, 2025a

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:1f5aaee3a8bd36834dac40a99d6fc9e9440f003d5e37df6d4ae51530838f12c0

Observation 1f2a86ef-2010-46fd-9780-7f454d0928ef · outbound

This paper cites Chen Yang, Yangfan He, Aaron Xuxiang Tian, Dong Chen, Jianhui Wang, Tianyu Shi, Arsalan Heydarian, and Pei Liu.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Chen Yang, Yangfan He, Aaron Xuxiang Tian, Dong Chen, Jianhui Wang, Tianyu Shi, Arsalan Heydarian, and Pei Liu

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:f4aa02d0d18ab084497985eaeee70257dc86f5a4a4a4e5e63cc9ea56424a2a52

Observation 34424a21-f194-4348-ab4d-a6dfa0f5b614 · outbound

This paper cites Af- fective multimodal agents with proactive knowledge grounding for emotionally aligned marketing dialogue.arXiv preprint arXiv:2511.21728, 2025a.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Af- fective multimodal agents with proactive knowledge grounding for emotionally aligned marketing dialogue.arXiv preprint arXiv:2511.21728, 2025a

Reference 17

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:d3e3c0da12e49f700b5206e30e6346b76313edb3a019fa3b3d89746de7866b06

Observation 0e9124bc-3a94-467c-b661-2a4b50253fa4 · outbound

This paper cites Advanced Deep Learning Methods for Protein Structure Prediction and Design.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Advanced Deep Learning Methods for Protein Structure Prediction and Design

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:6c2e3009d822c545826067a050d2e88c01b9c281361da0637e7172bfdb157cd7

Observation 98e38a82-915f-4527-a8f8-5882a6c71685 · outbound

This paper cites ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:16c5cc0cdfcd5a9674603e087d22fe14c264745099f176706a71962aab98cac6

Pith citing papers

No inbound Pith citation observations are available.