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Paper Citation Record · LEDGER

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2505.15076.

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

pith.paper-citation-record.v1
2505.15076 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:29:06.786058Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:57.193849Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:29:12.875192Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact0
  • verified fuzzy22
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd795bde-ef0d-4b4a-a913-dc58947e7b51 · outbound

This paper cites Neural feature search: A neural architecture for automated feature engineering.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Neural feature search: A neural architecture for automated feature engineering

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 97e5cbe2-3b55-429d-a019-6e95b3bedbf4 · outbound

This paper cites Evolutionary large language model for automated feature transformation.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Evolutionary large language model for automated feature transformation

Reference 2

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raw_fallback, observed 2026-08-07T15:29:12.049462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b94ab9c0-e895-4f3e-b10c-27f0ec87a3f6 · outbound

This paper cites Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming

Reference 3

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no resolver link, observed 2026-08-07T15:29:03.165295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:03.165295Z digest=sha256:cc0d6a2a8566b45f75b53c0fa833a5f2755e03db665fd80ca938c3a62b5e1a10

Observation 4f5068ed-9127-4c0d-8b94-756085ea2b74 · outbound

This paper cites Neuro-symbolic embedding for short and effective feature selection via autoregressive generation.ACM Transactions on Intelligent Systems and Technology, 16(2):1–21, 2025.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Neuro-symbolic embedding for short and effective feature selection via autoregressive generation.ACM Transactions on Intelligent Systems and Technology, 16(2):1–21, 2025

Reference 4

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raw_fallback, observed 2026-08-07T15:29:11.787401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:03.291502Z digest=sha256:e45903301186632b966ed8886f7d4609980520b9000994de56ee2021be1042cb

Observation daa44225-6554-4e81-8fba-6aa7c0adfb46 · outbound

This paper cites Recursive feature elimination with random forest for ptr-ms analysis of agroindustrial products.Chemometrics and intelligent laboratory systems, 83(2):83–90, 2006.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Recursive feature elimination with random forest for ptr-ms analysis of agroindustrial products.Chemometrics and intelligent laboratory systems, 83(2):83–90, 2006

Reference 5

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raw_fallback, observed 2026-08-07T15:29:11.464735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:03.405403Z digest=sha256:0149682ea6fbba04c3ad9378275796fbc5735cb644b1bb11a58571711732e04f

Observation cdbe866c-4171-4012-b691-d0b398b35492 · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:03.499749Z digest=sha256:43f1e4a35b5b8f8212c9173dfdbf023d7c0825238623847b37c29232f7303edb

Observation 6e31d7ac-d97e-495e-86e3-3176ec31bc29 · outbound

This paper cites Gene selection for cancer classifi- cation using support vector machines.Machine learning, 46:389–422, 2002.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Gene selection for cancer classifi- cation using support vector machines.Machine learning, 46:389–422, 2002

Reference 7

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raw_fallback, observed 2026-08-07T15:29:11.225894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:03.649144Z digest=sha256:774b78982185fcf0efdc20a2071a5657258b877bec447271fa38f89a6ea35fe3

Observation 8c37c51d-e926-4f18-bce9-dba9f12b31c8 · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:03.780446Z digest=sha256:ca8cc075ee8065fb84fc02da6665ba0fad9a3faa0fee0f2edf5db4a81da6387e

Observation 8a51c6b7-3bca-4d48-881b-7367e5bc1b7a · outbound

This paper cites The autofeat python library for automated feature engineering and selection.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories The autofeat python library for automated feature engineering and selection

Reference 9

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raw_fallback, observed 2026-08-07T15:29:10.988241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:03.911444Z digest=sha256:72562bdb1147a3e5b06eb01286ac9c5f7a39ea51e2b463e76552d6035df90caf

Observation 6252c7eb-b0dd-4dcf-9ce6-c541b606d324 · outbound

This paper cites Reinforcement feature transformation for polymer property performance prediction.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Reinforcement feature transformation for polymer property performance prediction

Reference 10

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raw_fallback, observed 2026-08-07T15:29:10.696114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.041494Z digest=sha256:afb870e3864c8fc9dd449dd07553295b45f20a756c3d83d94be90b6f76764037

Observation cf449252-6649-458d-9f1a-b3468249962a · outbound

This paper cites Deep feature synthesis: Towards automating data science endeavors.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Deep feature synthesis: Towards automating data science endeavors

Reference 11

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raw_fallback, observed 2026-08-07T15:29:10.500385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.171768Z digest=sha256:bc5f2c6b2171a9a8f2543e97e340bdc356476743f08aa58b5a8a0c43c1cc55c1

Observation b6e094bd-28a3-4855-8fa4-870f043c8842 · outbound

This paper cites Feature engineering for predictive modeling using reinforcement learning.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Feature engineering for predictive modeling using reinforcement learning

Reference 12

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raw_fallback, observed 2026-08-07T15:29:10.165788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9f6d4ce4-54f0-47ae-ba50-f0cb5cf3ab5c · outbound

This paper cites Camel: Communicative agents for" mind" exploration of large scale language model society.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Camel: Communicative agents for" mind" exploration of large scale language model society

Reference 13

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raw_fallback, observed 2026-08-07T15:29:09.918665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.406189Z digest=sha256:a46a0b4584252d758216685e19e7a5d6f4bb2197ddad78710ad2be153867d657

Observation e4863191-8289-47c7-aaa7-d652b7a6ed3e · outbound

This paper cites Automating feature subspace exploration via multi-agent reinforcement learning.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Automating feature subspace exploration via multi-agent reinforcement learning

Reference 14

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raw_fallback, observed 2026-08-07T15:29:09.631046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.542687Z digest=sha256:552f9ffe98293c14e1fcaaf0f4aeaeb7699b12eec27ad9fed1545b54e83bebcd

Observation be5b2803-dd54-4501-800a-048213bf0956 · outbound

This paper cites Automated feature selection: A reinforcement learning perspective.IEEE Transactions on Knowledge and Data Engineering, 35 (3):2272–2284, 2021.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Automated feature selection: A reinforcement learning perspective.IEEE Transactions on Knowledge and Data Engineering, 35 (3):2272–2284, 2021

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.699537Z digest=sha256:7893c9b9a2943ac2c432846b1868159a861ef9f4917764f72b28d08b872781f7

Observation f8933f35-4757-4895-8340-8b719adf5d39 · outbound

This paper cites Efficient reinforced feature selection via early stopping traverse strategy.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Efficient reinforced feature selection via early stopping traverse strategy

Reference 16

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raw_fallback, observed 2026-08-07T15:29:09.165639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.812310Z digest=sha256:5a5d04839a78896e907a2cb006aec55d8ebffae8ad0b8d397539fde4f4d5bf36

Observation 3357b3c3-aebb-4adf-bb19-7eff751a2fee · outbound

This paper cites Roco: Dialectic multi-robot collaboration with large language models.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Roco: Dialectic multi-robot collaboration with large language models

Reference 17

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raw_fallback, observed 2026-08-07T15:29:09.060163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:04.937296Z digest=sha256:ff6f4586309581f29f3ddb2376c01c9722353c3ef0d3ab422684781c6cd66280

Observation 6df9e082-825f-4782-acba-ce72fe1d9ebc · outbound

This paper cites Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288, 1996.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288, 1996

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:05.110534Z digest=sha256:7375e51af1df68f51593ab5abbe0d7f588f6de93e689adecf682ecead384b74e

Observation d6462c35-83c3-47db-9d5b-4235b2460356 · outbound

This paper cites Genetic programming for feature construction and selection in classification on high-dimensional data.Memetic Computing, 8:3–15, 2016.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Genetic programming for feature construction and selection in classification on high-dimensional data.Memetic Computing, 8:3–15, 2016

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:05.229886Z digest=sha256:b648b049bd0300285afc7174abe825b9a2defef018628e5d9eda9d5eb971424b

Observation ce4fa6e3-7f85-4c18-aa14-902b74b1f099 · outbound

This paper cites Group-wise reinforcement feature generation for optimal and explainable representation space reconstruction.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Group-wise reinforcement feature generation for optimal and explainable representation space reconstruction

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 0d487fe9-db2d-422b-bd72-6919548fc8ca · outbound

This paper cites an unresolved cited work.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Unresolved cited work

Reference 21

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:05.433446Z digest=sha256:4cfc1cc1f570ca7a2bede08525cc9dd3880f9979648ff3f127fee304e0f97e47

Observation 2418821f-eb3a-4bf4-8544-ca6daffbad50 · outbound

This paper cites Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:05.567414Z digest=sha256:d6d22e749c344cd2043330d1e1f4e8d8873bcf59274be33b17bc96cdea2c110b

Observation 827c20d7-e997-4d8f-8235-d88ab8d44df1 · outbound

This paper cites Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent

Reference 23

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:05.660110Z digest=sha256:6aeb65f9d24a4b22f42d606b804b060b7383fa2d1f4eef614cd5a9ce3c815708

Observation 3d4c2133-19f2-433d-b710-4201dc0dcc70 · outbound

This paper cites MixLLM: Dynamic Routing in Mixed Large Language Models.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories MixLLM: Dynamic Routing in Mixed Large Language Models

Reference 24

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no resolver link, observed 2026-08-07T15:29:05.839242Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:05.839242Z digest=sha256:efa6598775b7d0cec350d5b14f84e5b9b926912fa7604cec47109ac8d20643f7

Observation f8a9b8ac-e1e6-4304-9b46-ed2812e28bdb · outbound

This paper cites Macrec: A multi-agent collabo- ration framework for recommendation.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Macrec: A multi-agent collabo- ration framework for recommendation

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-07T15:29:08.439282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5ae091f0-11a7-476f-9af5-aa600aff6354 · outbound

This paper cites Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:06.006056Z digest=sha256:85e8a965949a3713f0e804ff5afa6c6a6ffabbd8421a681279e1d6209d5cde8e

Observation 8e7de63f-51e4-4eb2-8727-63c504aff7b6 · outbound

This paper cites Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 27

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no resolver link, observed 2026-08-07T15:29:06.100869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:06.100869Z digest=sha256:14227e95f5d9be3852afc25f99a5b0563c41952d612bef1cd23baca170f4b040

Observation c585bbce-225d-4a36-bb39-85283ff4750f · outbound

This paper cites A comparative study on feature selection in text categorization.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories A comparative study on feature selection in text categorization

Reference 28

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raw_fallback, observed 2026-08-07T15:29:08.146408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation e6e70da6-311f-4faf-a6a2-36eb87aaa2fa · outbound

This paper cites Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing

Reference 29

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raw_fallback, observed 2026-08-07T15:29:07.815830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:06.292224Z digest=sha256:7ee0eb5a33d6e5ad95fda6a77e8b05a1cab99a673529bc305fa41fddd02b3ed5

Observation 0fd3f812-3874-4602-8dc1-71f4860463ee · outbound

This paper cites Topology-aware Reinforcement Feature Space Reconstruction for Graph Data.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Topology-aware Reinforcement Feature Space Reconstruction for Graph Data

Reference 30

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no resolver link, observed 2026-08-07T15:29:06.387539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:06.387539Z digest=sha256:c0a62b92779734bda9d197e8e6f4962a31f2d0f4faf773bf9697c2122ccf0684

Observation 71725584-c00d-4169-8890-a0e2ccb47624 · outbound

This paper cites Feature selection as deep sequential generative learning.ACM Transactions on Knowledge Discovery from Data, 18(9):1–21, 2024.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Feature selection as deep sequential generative learning.ACM Transactions on Knowledge Discovery from Data, 18(9):1–21, 2024

Reference 31

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raw_fallback, observed 2026-08-07T15:29:07.575113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:06.471728Z digest=sha256:aa67528a3a41419dc6965fe5fbb18ca7ac6acf415b3e6f03dd3c38d85f925894

Observation 5e56f139-d8b9-45de-b806-8a20e274dcf1 · outbound

This paper cites Revolutionizing biomarker discovery: Leveraging generative ai for bio-knowledge-embedded continuous space exploration.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Revolutionizing biomarker discovery: Leveraging generative ai for bio-knowledge-embedded continuous space exploration

Reference 32

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raw_fallback, observed 2026-08-07T15:29:07.276669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T15:29:06.575913Z digest=sha256:d51caf2659b04aa5f118562d7a7b3d803fa6c0d04cd53e590aae79ac1569f60e

Observation 4275ac5b-f3bd-4223-bcb4-43a07cce2622 · outbound

This paper cites Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine- tuning.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine- tuning

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:07.060719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 96437a3f-0a74-4956-83f6-8c18e6906304 · outbound

This paper cites A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:06.786058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:06.786058Z digest=sha256:5bccc45ab9789f6d5a9c9ec028547acf6d135ca8aa4e24a4fc59b36b4c0b6182

Pith citing papers

Observation 1a26f49d-4729-4da2-ab7e-9818b07d8a92 · inbound

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation cites this paper.

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:57.193849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d3b0f1d2-bdd1-4d87-9c78-c8024e18cb72 · inbound

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives cites this paper.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:29:13.008545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T21:28:55.860154Z digest=sha256:e70678d0f4339bbf9c9c5d9e8537c46aa684a4f5a536c4588affb0be7b79fd5b