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Paper Citation Record · LEDGER

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows

As of 9 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2605.23914.

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

pith.paper-citation-record.v1
2605.23914 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T23:56:22.405561Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

63 of 63 outbound references displayed

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  • verified fuzzy0
  • unresolved63
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation d05aaf19-dc8c-46d0-85ec-4da99905510d · outbound

This paper cites Amazon Bedrock, 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Amazon Bedrock, 2026

Reference 1

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:c295d520405ad8b8d1b027a90bb60651010368e9378d03fc6984ec36f291810f

Observation d05e26fc-f60b-42df-b571-713eb724211b · outbound

This paper cites Amazon Elastic Compute Cloud (Amazon EC2), 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Amazon Elastic Compute Cloud (Amazon EC2), 2026

Reference 2

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Observation 62a2451c-6215-4b0f-b5dc-18d4e920a92b · outbound

This paper cites Strands agents — open source ai agent sdk for python & typescript.https://strandsagents.com/, 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Strands agents — open source ai agent sdk for python & typescript.https://strandsagents.com/, 2026

Reference 3

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Observation fb953e57-8bcd-470e-929f-e1c3bfd98e62 · outbound

This paper cites Claude Sonnet, 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Claude Sonnet, 2024

Reference 4

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Observation 81e50db0-0ebd-4b51-b0bf-2da9de356e5d · outbound

This paper cites Agent sdk overview - claude api docs.https://platform.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Agent sdk overview - claude api docs.https://platform

Reference 5

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Observation 700a5ccc-0ada-4ace-940b-6833324bfe7a · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language mod- els.Proceedings of the AAAI Conference on Artificial Intelligence, 38(16):17682–17690, March 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Graph of thoughts: Solving elaborate problems with large language mod- els.Proceedings of the AAAI Conference on Artificial Intelligence, 38(16):17682–17690, March 2024

Reference 6

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Observation 45a19a64-fa04-4710-971b-ec6058cb95d2 · outbound

This paper cites Prompting is programming: A query language for large language models.Proc.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Prompting is programming: A query language for large language models.Proc

Reference 7

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Observation fc1c955d-ff57-420f-ae16-b240ac80b69c · outbound

This paper cites Candès and Benjamin Recht.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Candès and Benjamin Recht

Reference 8

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Observation 7e48a810-5b13-4688-b842-180f36af95ba · outbound

This paper cites Murakkab: Resource-efficient agentic workflow orchestration in cloud platforms, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Murakkab: Resource-efficient agentic workflow orchestration in cloud platforms, 2025

Reference 9

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Observation 3b92f25a-0984-4a29-a32d-a28722511f7a · outbound

This paper cites FrugalGPT: How to use large language models while reducing cost and improving perfor- mance.Transactions on Machine Learning Research, 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows FrugalGPT: How to use large language models while reducing cost and improving perfor- mance.Transactions on Machine Learning Research, 2024

Reference 10

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Observation 54259e9c-64df-44c4-9c5e-73c68e4654f2 · outbound

This paper cites Xgboost: A scalable tree boosting system.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Xgboost: A scalable tree boosting system

Reference 11

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Observation 8cc618a9-add6-4da4-84a8-d20b7f845130 · outbound

This paper cites Do large language models need a content delivery network?, 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Do large language models need a content delivery network?, 2024

Reference 12

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Observation 8c5c4a60-1208-4bab-b33e-fce2aa6f0690 · outbound

This paper cites Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL

Reference 13

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Observation b2e58d7f-d83b-49ff-8555-5576bf10b81a · outbound

This paper cites Inferline: latency- aware provisioning and scaling for prediction serving pipelines.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Inferline: latency- aware provisioning and scaling for prediction serving pipelines

Reference 14

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Observation 50b65125-5487-4171-9a2e-237e14ee069d · outbound

This paper cites Franklin, Joseph E.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Franklin, Joseph E

Reference 15

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Observation 64fe4066-3417-465a-a0ae-38afb2001718 · outbound

This paper cites Crewai: The leading multi-agent platform.https://crewai.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Crewai: The leading multi-agent platform.https://crewai

Reference 16

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Observation d57d5824-7efa-4434-ba72-aa717306d430 · outbound

This paper cites Deepseek-v3.2: Pushing the frontier of open large lan- guage models, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Deepseek-v3.2: Pushing the frontier of open large lan- guage models, 2025

Reference 17

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Observation 528bd401-70e1-40b1-898f-0f0fa16f4286 · outbound

This paper cites an unresolved cited work.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Unresolved cited work

Reference 18

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Observation 1abb703c-7821-4138-a9de-2b4140c9ff71 · outbound

This paper cites Amdahl’s law for tail latency.Commun.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Amdahl’s law for tail latency.Commun

Reference 20

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Observation 7d9872a7-3394-4b20-8535-6c130888061d · outbound

This paper cites an unresolved cited work.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Unresolved cited work

Reference 21

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Observation f2d3003a-9d92-4748-9ce0-882eedd61031 · outbound

This paper cites gemma-3-27b-it Model Card, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows gemma-3-27b-it Model Card, 2025

Reference 22

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Observation 62577f41-47a4-4dcd-a306-e1d0a0fc02c6 · outbound

This paper cites Matrix completion and low-rank svd via fast alternating least squares, 2014.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Matrix completion and low-rank svd via fast alternating least squares, 2014

Reference 23

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Observation 05d6962c-9664-4874-89d9-8a5b081a212b · outbound

This paper cites MetaGPT: Meta programming for a multi- agent collaborative framework.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows MetaGPT: Meta programming for a multi- agent collaborative framework

Reference 24

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Observation 0b850d6f-e954-45d5-b78a-56ee32d0fe42 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 25

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Observation f914225f-74b4-49ba-879b-20a3932083ea · outbound

This paper cites Matrix factorization techniques for recommender systems.Computer, 42(8):30–37, 2009.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Matrix factorization techniques for recommender systems.Computer, 42(8):30–37, 2009

Reference 26

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Observation c8576efd-6eca-478b-86c4-09f28408236c · outbound

This paper cites Langgraph, 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Langgraph, 2024

Reference 27

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Observation 474cc67f-730c-4747-8d1a-c5789bd9ca8d · outbound

This paper cites Lewis, Sebastián Echeverría, Lena Pons, and Jeffrey Chrabaszcz.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Lewis, Sebastián Echeverría, Lena Pons, and Jeffrey Chrabaszcz

Reference 28

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Observation 5041074a-eb7e-4e56-b79d-697ef99042cb · outbound

This paper cites LlamaIndex, 11 2022.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows LlamaIndex, 11 2022

Reference 29

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Observation 717a872a-bb39-4bc9-8df4-51eb3547112d · outbound

This paper cites Lmcache: An efficient kv cache layer for enterprise- scale llm inference, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Lmcache: An efficient kv cache layer for enterprise- scale llm inference, 2025

Reference 30

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Observation 13ac2caa-76c0-4c32-bc27-59f1aafa76b9 · outbound

This paper cites Cachegen: Kv cache compression and streaming for fast large language model serving.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Cachegen: Kv cache compression and streaming for fast large language model serving

Reference 31

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Observation 409ffa28-115d-438d-a64d-b2929bc0dd8e · outbound

This paper cites Gonzalez, and Ion Stoica.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Gonzalez, and Ion Stoica

Reference 32

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Observation 25c18db2-d5bb-4c8d-aba1-ed4e1c740a82 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Self-refine: Iterative refinement with self-feedback, 2023

Reference 33

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Observation 7365945c-9ebe-48ad-baef-6beebb5d1704 · outbound

This paper cites Match- maker: Data drift mitigation in machine learning for large-scale sys- tems.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Match- maker: Data drift mitigation in machine learning for large-scale sys- tems

Reference 34

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Observation 5efa1ca5-1bbe-4a02-8291-2be8e9343dde · outbound

This paper cites an unresolved cited work.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Unresolved cited work

Reference 35

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Observation 7849d9d8-ff26-460a-abc3-c08f209bf061 · outbound

This paper cites Llama-3.3-70B-Instruct Model Card, 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Llama-3.3-70B-Instruct Model Card, 2024

Reference 36

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Observation 0f660452-a35f-412c-9ceb-3d3f9a22e27f · outbound

This paper cites Gonzalez, M Waleed Kadous, and Ion Stoica.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Gonzalez, M Waleed Kadous, and Ion Stoica

Reference 37

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Observation cc4d6f38-2668-4842-b8c9-f8a217191643 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows gpt-oss-120b & gpt-oss-20b Model Card, 2025

Reference 38

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:fa77542736e3bc069897e6919ad834490ed5947866ca708d4a761395022bc100

Observation 7b455e93-3b79-43b5-aa64-045d43a384ee · outbound

This paper cites Openai agents sdk.https://openai.github.io/openai-agents- python/, 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Openai agents sdk.https://openai.github.io/openai-agents- python/, 2026

Reference 39

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Observation 061ca800-78f4-4b34-b6dd-832b565c2b15 · outbound

This paper cites KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows

Reference 40

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Observation d3513de9-fe66-4fff-82c8-d46c06309a6e · outbound

This paper cites Patil, Tianjun Zhang, Xin Wang, and Joseph E.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Patil, Tianjun Zhang, Xin Wang, and Joseph E

Reference 41

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Observation 8928e0eb-8f79-4ffc-9883-9650201fd3fa · outbound

This paper cites Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023

Reference 42

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:81ed76b272a75a32a8bc9e4e32d1941bcd01a97e6e3f48f1a2ab408df284a1d0

Observation 83923d7c-41fd-4590-b240-59d719847213 · outbound

This paper cites Self-Reflection in LLM Agents: Effects on Problem-Solving Performance.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Self-Reflection in LLM Agents: Effects on Problem-Solving Performance

Reference 43

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Observation 2cc03554-ac86-44a6-9711-94a26a2fb371 · outbound

This paper cites Yadwadkar, and Christos Kozyrakis.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Yadwadkar, and Christos Kozyrakis

Reference 44

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:576c0ebb6da55522061822d66202007a5bc069b8580e67e4b75401b5dc614291

Observation d160c234-f15c-4a44-b173-8ae60260f162 · outbound

This paper cites Yadwadkar, and Christos Kozyrakis.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Yadwadkar, and Christos Kozyrakis

Reference 45

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:2be15ef014c1d470935cffc4c6ea27064b8ccad1b459c0ff4fb9e23046de63bd

Observation 2f8c7362-1d98-4e9f-a6c7-6a388e14faf8 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Toolformer: Language models can teach themselves to use tools

Reference 46

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:3ffdfd75e935aa30e7e116e322937e6b7f22f23662e7187e69dcb14755625dc3

Observation 0dfc850d-8d61-4414-aefb-c2f703cc819a · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023

Reference 47

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:ebf187890f56541bbb1f0f1f4dccb4672b7d0bafeb0f24ac002df2231023113a

Observation d8b3e523-e50f-4cb1-809c-f3a60758ca64 · outbound

This paper cites Agentxray: White-boxing agentic systems via workflow reconstruction, 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Agentxray: White-boxing agentic systems via workflow reconstruction, 2026

Reference 48

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:2682bfe89bac67f70eb9a63cce9d7f2f74df03295993584c5bbb3840da828211

Observation 5787fde3-6357-4ffd-8cb8-81c5035d614b · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Reflexion: Language agents with verbal reinforcement learning, 2023

Reference 49

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:0ad74e12991ddd68f7babb83b347c8213561109d50d87166d7644557b04f8d89

Observation 32cade63-2b32-4683-8b43-9579472391dd · outbound

This paper cites Irt-router: Effective and inter- pretable multi-llm routing via item response theory, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Irt-router: Effective and inter- pretable multi-llm routing via item response theory, 2025

Reference 50

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:d6b084d543543496be271ffd3d6a6ff3aaabb723e8831e710e0ea4b2a9fb9ae1

Observation 584b4c93-88bd-45d3-ade4-0faf0aedd1f8 · outbound

This paper cites Kimi k2.5: Visual agentic intelligence, 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Kimi k2.5: Visual agentic intelligence, 2026

Reference 51

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:c9fa3a79dab810766f6dfe951d9b422d807db08ea11c01965fd5164eafa24dbd

Observation 255b2f1c-9f0c-4a97-9326-826911fe47b2 · outbound

This paper cites Causal LLM routing: End-to-end regret minimization from observational data.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Causal LLM routing: End-to-end regret minimization from observational data

Reference 52

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:ef54c0fb525a229f1c5ab5c97812906cf28d63c27f743bba800e061fff79f6fc

Observation e394e9ba-dc1f-424d-8787-fdc21c2829a6 · outbound

This paper cites Efficient llm serving for agentic workflows: A data systems perspective, 2026.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Efficient llm serving for agentic workflows: A data systems perspective, 2026

Reference 53

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:9b23cfe6dacc23f3db323bab96b5fac4e3b93d3c04ac4f1d2d700884b059686e

Observation f5f1b0c1-aaa4-44f6-ad87-8df293909c1b · outbound

This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6), March 2024.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6), March 2024

Reference 54

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:ddc6f0c8993a837098e82d787b1c021a921df9b0728eebf21f68ca81762cb8f4

Observation 62671617-07ca-46c5-83fd-a41ae4fa412f · outbound

This paper cites Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models, 2023

Reference 55

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:d2fb5e1d831a4218f531e3d3ee29b20a9b9fd356a85b3d507c5a65eeec91bb1f

Observation 2877db8e-accb-471c-a40f-8bf417114c49 · outbound

This paper cites White, Doug Burger, and Chi Wang.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows White, Doug Burger, and Chi Wang

Reference 56

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Observation 2193eec4-d152-47ce-8ce8-ce547c8687b2 · outbound

This paper cites Rewoo: Decoupling reasoning from observa- tions for efficient augmented language models, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Rewoo: Decoupling reasoning from observa- tions for efficient augmented language models, 2023

Reference 57

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:5a7f52adfdc37cdf56679a2fb1c13e37ebf32ee0757d4bf32f73f445753f5f72

Observation ebaffb37-a542-4703-8eb4-9d9042871e1d · outbound

This paper cites Qwen3 technical report, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Qwen3 technical report, 2025

Reference 58

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:b023ab8a8306baedd97af1d08ffef674f082ae1b3e1467c26a7f1a71e90391e6

Observation 5623b48e-2f7b-4ef7-a11e-48b4d6f044ea · outbound

This paper cites Cacheblend: Fast large language model serving for rag with cached knowledge fusion.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Cacheblend: Fast large language model serving for rag with cached knowledge fusion

Reference 59

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:faa5eb08abc91ddda32b1cb3178f89c9f672303fcae6612e0ad908bbd21604df

Observation 778ed2a7-2a74-4715-9b81-5ec1c4bc08d0 · outbound

This paper cites Griffiths, Yuan Cao, and Karthik Narasimhan.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Griffiths, Yuan Cao, and Karthik Narasimhan

Reference 60

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Observation d9316494-a571-49c9-bbc7-5f3c93ecfac2 · outbound

This paper cites React: Synergizing reasoning and acting in language models, 2023.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows React: Synergizing reasoning and acting in language models, 2023

Reference 61

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:7c566a53a5604716cf23f07c4c88e2931e60a0b1d690f5856f35802c06dfd0d1

Observation 5cc93b1f-13cd-46a5-bfbf-f3378c64a23e · outbound

This paper cites AFlow: Automating agentic workflow generation.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows AFlow: Automating agentic workflow generation

Reference 62

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:d25678c553ea0bf497c8eee4e539ee4af03592e965c81a6d5c05a659b1182d7b

Observation 28de1150-d6a0-4762-abf0-b848653c818a · outbound

This paper cites Gonzalez, Clark Barrett, and Ying Sheng.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows Gonzalez, Clark Barrett, and Ying Sheng

Reference 63

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:0e6b7bcbec6021ab5b63b039740ee643d96b2123cdf64c6cef40d751d972ba9e

Observation dbd84928-8ce8-425c-afc9-fee16429cb3f · outbound

This paper cites GLM-4.7 Technical Blog, 2025.

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows GLM-4.7 Technical Blog, 2025

Reference 64

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source=pdf_text observed=2026-07-12T23:56:22.405561Z digest=sha256:3817a1bf3ae344b8617cc497482b252168f8fa68aac5a0249e2ab24b3d21e716

Pith citing papers

No inbound Pith citation observations are available.