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Paper Citation Record · LEDGER

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

As of 8 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 2026-08-07T06:34:17.273281+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
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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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:03.014613Z digest=sha256:2e9233ca42b9e5683b5af82316fd8a98cb0704e1274054a44ec157fb5a571572

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:03.165295Z digest=sha256:9381b0e139ef206ee22830bd808071672aa3afc14d51eaccd69195637aea8ca8

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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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.

source=pdf_text observed=2026-08-07T15:29:03.291502Z digest=sha256:f18db89f5df82ade0787393a1ca46875a9a75ba357ca7b1be4d4f9f92e50ea65

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:03.405403Z digest=sha256:6c36d98705a424a8e88168461b737f97f596e0b060696c9fefeaa1ef30ccb935

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=sha256:4cbbe7c127a55dc910810de0385d2a46ce0cf18e12bcc0c578800600a0d57290

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:03.649144Z digest=sha256:50107a295b8a02ed95a7c007ec24a9396fecfe8ee17ecd71de0f7c46a1a7e4f9

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

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source=pdf_text observed=2026-08-07T15:29:03.780446Z digest=sha256:beb65845487da1ecd1cd99a11754092ecc050c1be867575997e6038002f0ec34

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:03.911444Z digest=sha256:a6e47193cfc039cbbf67328b5c4614eed0dc90cc0e343a745f6ee98963ed8876

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:04.041494Z digest=sha256:e553d44ed3bd22c87daa896d24ae1b8890460d0acf98ffaea61fba646d645a00

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 2026-08-07T06:34:17.273281+00:00.

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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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:04.406189Z digest=sha256:2d77349836ca576d0046c380718971f1777d2d98d0d63b70491291d7d80ea4ad

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:04.542687Z digest=sha256:98d659728c84590438b0cc3af72e5ff8261f766a07d5a8650437404fa8b1dc67

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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raw_fallback, observed 2026-08-07T15:29:09.303740Z

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.

source=pdf_text observed=2026-08-07T15:29:04.699537Z digest=sha256:3aa42316c5380092264204d2c7eb8797edae9a00c73f2ca3167e0feba5ad3e4a

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 2026-08-07T06:34:17.273281+00:00.

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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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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.

source=pdf_text observed=2026-08-07T15:29:04.937296Z digest=sha256:2f8fe13b6a4b25bbd861fdbbfe4aa966257862bcd4835272bc1bfecb83716ffd

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=sha256:81a995e951dd4847507858ed48c9e99fc2abb9fcd1d9957827248706b0e3d927

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 2026-08-07T06:34:17.273281+00:00.

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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 2026-08-07T06:34:17.273281+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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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.

source=pdf_text observed=2026-08-07T15:29:05.433446Z digest=sha256:f81386ea700891e64dc032909702578b21db05a1ee45e0d8fd5153298c4231e6

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

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source=pdf_text observed=2026-08-07T15:29:05.567414Z digest=sha256:a53522dd42f30dc5baaa2e231a065a9a55ab9bc71f91c4aab80b21e2e08907e3

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=sha256:9d3ccc8370f1921853cb3e7097bc20a931ce1d6f9352e66a62b74bc6e9e267b5

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.

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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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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 2026-08-07T06:34:17.273281+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:cd370c63c4be7d61d041ab42117469acdf3df4b5ebd6b265c0a9052328acdf7b

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:06.100869Z digest=sha256:0c5be497a442e75dadf74c9d4b14d71ed2f55c60b93ae13701465d17157cb61c

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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verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:06.207997Z digest=sha256:8ff113dcb750c2eac9125f959cde5736f8264f97f819228090aeb40506b71dc8

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:06.292224Z digest=sha256:00217f5109d6273f8d7b18d7817fcd238da15babdabc2a661d0a30ff5a858025

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:d87269cc0a18222812b4efe82c83f06eff080f3c2056d684dcb887cc4b555ecb

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:06.471728Z digest=sha256:c608250d4bfd03517e15eff5707a25b8f837797eb6ad1ab5d426f0a4b87a03b0

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:06.575913Z digest=sha256:891898a48a7515a80b4ff2311a0a9a4b8744fba3a636b2e80a80598866fbedb2

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:29:06.719341Z digest=sha256:c0ef3440e3cf004b08e8c4c372487fa406e36f282b5ce0b7f7dbeda96c567884

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=sha256:3a57653c0ed8704af3c708010bae67a9ff57853acb64e639f6e530e2b69030bf

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.

source=arxiv_source observed=2026-08-07T05:14:57.193849Z digest=sha256:c03e2dedcc120c3f8383ae98d010388b956edf980b684c51a049d09d42f3508f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:28:55.860154Z digest=sha256:4ce2c40e8f3832ff1251605db76241c8dc3d8a8507a36a09f9e780edf7a17b50