Paper Citation Record · LEDGER

Generative to Agentic AI: Survey, Conceptualization, and Challenges

As of 16 August 2026, this Paper Citation Record lists 100 of 196 outbound references and 17 inbound Pith citation observations for arXiv:2504.18875.

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

pith.paper-citation-record.v1
2504.18875 v1

Coverage vector

measured 100 of 196 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:10:25.083490Z

measured 117 of 117 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:36:55.328019Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T16:27:39.517876Z

Reference resolution

100 of 196 outbound references displayed

verified exact0
verified fuzzy0
unresolved100
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f10ee5e-e286-4430-9c53-cadc19716da0 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: International Conference on Machine Learning, pp

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.708712Z

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Observation 67ef5ead-f19b-49c7-a4b9-c15fc53fe6b1 · outbound

This paper cites Political Analysis 31(3), 337–351 (2023) Agent.ai: AI Agents Explained.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Political Analysis 31(3), 337–351 (2023) Agent.ai: AI Agents Explained

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.713270Z

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Observation aa229ffc-a55a-4ed6-a2f6-24b135bc18f7 · outbound

This paper cites Advances in Neural Information Processing Systems 36, 22304–22325 (2023) 36.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Advances in Neural Information Processing Systems 36, 22304–22325 (2023) 36

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.717359Z

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Observation 3be250ec-5703-4ca1-8966-db73acca62ea · outbound

This paper cites Accessed: 2025-04-23 (2025).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Accessed: 2025-04-23 (2025)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.721492Z

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Observation b29c459d-f0c6-4467-9dfb-a220de9409dc · outbound

This paper cites IEEE Access (2025) Anthropic: Introducing Computer Use, a New Claude 3.5 Sonnet, and Claude 3.5 Haiku.

Generative to Agentic AI: Survey, Conceptualization, and Challenges IEEE Access (2025) Anthropic: Introducing Computer Use, a New Claude 3.5 Sonnet, and Claude 3.5 Haiku

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.725244Z

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Observation 8cab824d-cf5b-4359-bb19-471930fb13ec · outbound

This paper cites Playing repeated games with Large Language Models.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Playing repeated games with Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.729345Z

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Observation e273cdf4-8704-40ee-bc7a-211a6d923c20 · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

Generative to Agentic AI: Survey, Conceptualization, and Challenges AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.733642Z

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Observation fb6229e3-84ed-46ab-b265-cea935c16f77 · outbound

This paper cites In: The Twelfth International Conference on Learning Representations (2023) BBC News: Microsoft Chatbot Is Taken Offline After It Learns to Be Racist.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: The Twelfth International Conference on Learning Representations (2023) BBC News: Microsoft Chatbot Is Taken Offline After It Learns to Be Racist

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.737390Z

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Observation a3f83e01-922b-4e0a-9fe9-6df465a42deb · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Generative to Agentic AI: Survey, Conceptualization, and Challenges RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.741097Z

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Observation 819fa863-22ae-42e2-bdc7-a37445cb1bc4 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.744999Z

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Observation 1864fb19-5458-4aa1-a7f3-fe1c479e323a · outbound

This paper cites Journal of the ACM (JACM) 63(3), 1–45 (2016).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Journal of the ACM (JACM) 63(3), 1–45 (2016)

Reference 11

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Observation 647528fb-c8f6-46c3-b059-ac80f0f9c935 · outbound

This paper cites https://yoshuabengio.org/2023/03/21/ scaling-in-the-service-of-reasoning-model-based-ml/.

Generative to Agentic AI: Survey, Conceptualization, and Challenges https://yoshuabengio.org/2023/03/21/ scaling-in-the-service-of-reasoning-model-based-ml/

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.753560Z

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Observation 3e1e50e2-5968-42da-835b-58d9dc5e8aa5 · outbound

This paper cites Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.757366Z

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Observation 746b34d2-2d3f-41e0-9712-49e7abcca391 · outbound

This paper cites Routledge, ??? (1985) 37.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Routledge, ??? (1985) 37

Reference 14

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Observation b16742f2-8d2c-49be-b094-e93f5954e3f7 · outbound

This paper cites Language Models are Few-Shot Learners.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Language Models are Few-Shot Learners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.765593Z

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Observation 765b9dcf-fe65-466c-add4-915ae4d241d8 · outbound

This paper cites Advances in Neural Information Processing Systems 37, 5996–6051 (2024).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Advances in Neural Information Processing Systems 37, 5996–6051 (2024)

Reference 16

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Observation d4137575-4674-4d04-b1f7-41c3f103c8ba · outbound

This paper cites https://huggingface.co/spaces/HuggingFaceH4/ blogpost-scaling-test-time-compute.

Generative to Agentic AI: Survey, Conceptualization, and Challenges https://huggingface.co/spaces/HuggingFaceH4/ blogpost-scaling-test-time-compute

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.772972Z

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Observation faac6df1-9d1d-42fb-b6a5-4bde74e55a22 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.776518Z

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Observation 8a45082f-ad8c-41db-81aa-e059a6b0e970 · outbound

This paper cites ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate.

Generative to Agentic AI: Survey, Conceptualization, and Challenges ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Reference 19

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Observation 7bccee50-eaa4-40d8-9971-6bf6e2d93fba · outbound

This paper cites : Scaling instruction-finetuned language models.

Generative to Agentic AI: Survey, Conceptualization, and Challenges : Scaling instruction-finetuned language models

Reference 20

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Observation c68882e2-c905-43fe-81b2-7375354fc4db · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

Generative to Agentic AI: Survey, Conceptualization, and Challenges ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.786470Z

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Observation 0c99e7ab-f87c-4b88-af19-f39069a1cc72 · outbound

This paper cites Teaching Large Language Models to Self-Debug.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Teaching Large Language Models to Self-Debug

Reference 22

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Observation d0c449dd-ed16-4371-a83f-ff21ab7723a8 · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 23

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Observation 459f9fda-c0ca-4ecc-bbe0-d9e576cede02 · outbound

This paper cites Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis

Reference 24

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Observation dfd960c9-f44a-4853-9fac-62a56a7460ea · outbound

This paper cites Large Language Models as Tool Makers.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Large Language Models as Tool Makers

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.800945Z

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Observation 677caa38-3ebd-4511-9c20-6d8152e8635a · outbound

This paper cites In: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 26

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Observation aee32d80-290e-4906-83ba-76a709f6d510 · outbound

This paper cites ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models.

Generative to Agentic AI: Survey, Conceptualization, and Challenges ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models

Reference 27

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Observation e84f4957-a3d2-42eb-abf4-e3628d0cb262 · outbound

This paper cites Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects

Reference 28

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Observation 723e1dbc-0171-41de-9114-627642bbc192 · outbound

This paper cites an unresolved cited work.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Unresolved cited work

Reference 29

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Observation 9102273c-cb1d-4c42-a5f8-ccc8d441038e · outbound

This paper cites WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?.

Generative to Agentic AI: Survey, Conceptualization, and Challenges WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

Reference 30

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Observation 74fa1c04-48ca-4868-b1e1-6209e03fa5eb · outbound

This paper cites In: Forty-first International Conference on Machine Learning (2023).

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: Forty-first International Conference on Machine Learning (2023)

Reference 31

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This paper cites Advances in Neural Information Processing Systems 36, 30039–30069 (2023).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Advances in Neural Information Processing Systems 36, 30039–30069 (2023)

Reference 32

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Observation 56dc7338-fae5-4a98-ba92-dc50fb6301b1 · outbound

This paper cites Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Reference 33

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Observation 15758d57-8fda-4f74-8b08-aad7672542ff · outbound

This paper cites ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models.

Generative to Agentic AI: Survey, Conceptualization, and Challenges ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models

Reference 34

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This paper cites Competitive Programming with Large Reasoning Models.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Competitive Programming with Large Reasoning Models

Reference 35

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This paper cites Packt Publishing Ltd, ??? (2018).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Packt Publishing Ltd, ??? (2018)

Reference 36

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This paper cites Trends in cognitive sciences 3(4), 128–135 (1999).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Trends in cognitive sciences 3(4), 128–135 (1999)

Reference 37

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Observation 1b959346-48a2-408e-a6d7-d1a52bd99dfa · outbound

This paper cites In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pp.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pp

Reference 38

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Observation 939fe559-ab34-46d0-8d97-de126e085a6b · outbound

This paper cites In: Proceedings of the 32nd ACM International Conference on Multimedia, pp.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: Proceedings of the 32nd ACM International Conference on Multimedia, pp

Reference 39

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Observation 7697856c-c0ac-4152-9f56-5b214be149e1 · outbound

This paper cites Stream of Search (SoS): Learning to Search in Language.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Stream of Search (SoS): Learning to Search in Language

Reference 40

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This paper cites In: International Conference on Machine Learning, pp.

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: International Conference on Machine Learning, pp

Reference 41

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions

Reference 42

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This paper cites Advances in neural information processing systems 27 (2014).

Generative to Agentic AI: Survey, Conceptualization, and Challenges Advances in neural information processing systems 27 (2014)

Reference 43

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Observation 492cbbde-ff73-4675-afac-09773664db75 · outbound

This paper cites Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System

Reference 44

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This paper cites Meta Reasoning for Large Language Models.

Generative to Agentic AI: Survey, Conceptualization, and Challenges Meta Reasoning for Large Language Models

Reference 45

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Generative to Agentic AI: Survey, Conceptualization, and Challenges DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 46

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Measuring Coding Challenge Competence With APPS

Reference 47

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Observation 4a2bfd3b-f994-4a5d-a819-da1f6329f468 · outbound

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Measuring Massive Multitask Language Understanding

Reference 48

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This paper cites In: The Twelfth International Conference on Learning Representations (2024).

Generative to Agentic AI: Survey, Conceptualization, and Challenges In: The Twelfth International Conference on Learning Representations (2024)

Reference 49

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Observation 0d2cf697-f7bd-4473-bebc-2ade7b657dce · outbound

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Generative to Agentic AI: Survey, Conceptualization, and Challenges In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 50

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Accessed: 2025-04-16

Reference 51

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Observation c858c9fd-7602-452a-901f-a7f56f6e79d0 · outbound

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Generative to Agentic AI: Survey, Conceptualization, and Challenges War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars

Reference 52

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Observation 8456b398-a4ce-491c-83b2-4a8489dd2a71 · outbound

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Reasoning with Language Model is Planning with World Model

Reference 53

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Observation 514f1b01-fda4-4182-a7c7-77a5fbb30304 · outbound

This paper cites AI Open (2025).

Generative to Agentic AI: Survey, Conceptualization, and Challenges AI Open (2025)

Reference 54

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Observation f0a6454f-542c-4f3d-b1ae-3609355eb6b5 · outbound

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Automated Design of Agentic Systems

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T10:10:24.913185Z

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Generative to Agentic AI: Survey, Conceptualization, and Challenges what happens when it runs out? California Magazine (2025)

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Generative to Agentic AI: Survey, Conceptualization, and Challenges Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

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Observation c7525e1a-dad5-46b4-ac21-0c26ba34e99c · inbound

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI cites this paper.

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI Generative to Agentic AI: Survey, Conceptualization, and Challenges

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T11:29:20.225626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e593189c-d4bd-446b-8fce-4f087b97b06a · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language Generative to Agentic AI: Survey, Conceptualization, and Challenges

Reference 145

Resolution: unresolved; no resolver link, observed 2026-07-11T23:16:58.545731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 305a26d7-7c2f-4399-aa58-c15794d0fd53 · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language Generative to Agentic AI: Survey, Conceptualization, and Challenges

Reference 145

Resolution: unresolved; no resolver link, observed 2026-07-13T07:02:13.140334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fe133ef-afe3-47b6-96e9-8831dce4153f · inbound

Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes cites this paper.

Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes Generative to Agentic AI: Survey, Conceptualization, and Challenges

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T23:25:47.149634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30a98fcf-c3d4-4db0-af4d-4f68b7220385 · inbound

Agent Security Needs Redefinition through a Holistic Framework cites this paper.

Agent Security Needs Redefinition through a Holistic Framework Generative to Agentic AI: Survey, Conceptualization, and Challenges

Reference 193

Resolution: unresolved; no resolver link, observed 2026-08-01T06:04:46.242170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd9c1064-b1a0-4c6e-9b91-35c2c1a21733 · inbound

MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination cites this paper.

MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination Generative to Agentic AI: Survey, Conceptualization, and Challenges

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T10:11:28.505199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.