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Paper Citation Record · LEDGER

Global Optimization and Inference-Time Region Grafting for Agentic Workflows

As of 13 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.02353.

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

pith.paper-citation-record.v1
2608.02353 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:52:24.632934Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

21 of 21 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved21
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 569e0ff6-d78a-4d62-b290-4072918247d6 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Evaluating Large Language Models Trained on Code

Reference 3

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no resolver link, observed 2026-08-04T08:52:21.634519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:21.634519Z digest=sha256:1d51512701f48599243fa66dbb630fe153846d6399022f96152a95bac28f7ab3

Observation 9da1ec30-dc35-43fe-ae53-cc052cdd7033 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Training Verifiers to Solve Math Word Problems

Reference 5

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no resolver link, observed 2026-08-04T08:52:21.880653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:21.880653Z digest=sha256:d7fbbd937024be572965e7655028d0d91f677e760b7dbac96c6c25b02d3b3f5b

Observation 746d44c9-e417-4695-abdd-0794370ff34b · outbound

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

Global Optimization and Inference-Time Region Grafting for Agentic Workflows MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 7

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no resolver link, observed 2026-08-04T08:52:22.058758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:22.058758Z digest=sha256:49e902b420d0cb3fdb1466434ff6416f4b50bf7af0f787c806dc6cef4ce4e599

Observation fe9df4b3-0278-448c-942d-e2a15f5666b3 · outbound

This paper cites CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

Reference 10

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no resolver link, observed 2026-08-04T08:52:22.517554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:22.517554Z digest=sha256:064ca2b45ee47b0cb29fda30c652be99580b12de031765fb8da3159ac55b236d

Observation 30c7ed1b-d96a-45b1-8cbe-4e79ba477902 · outbound

This paper cites arXiv:2506.06017.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows arXiv:2506.06017

Reference 11

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no resolver link, observed 2026-08-04T08:52:22.654776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:22.654776Z digest=sha256:5079a0b3cfbf33786753002bb9184530728b330258b9c0a0cebfbdbe21e9b012

Observation e535defa-42a1-4ad4-84c3-245379a10425 · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 12

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no resolver link, observed 2026-08-04T08:52:22.773150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:22.773150Z digest=sha256:75b26fab87532a557fbb22101c49de53f07ce8dc5fb1855ea338d3ab99361df7

Observation 3ca042bf-0a89-4c78-86cf-3a76a0c91882 · outbound

This paper cites A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration

Reference 13

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no resolver link, observed 2026-08-04T08:52:22.942819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:22.942819Z digest=sha256:fda5dd6b00c83ccc1e4b7c0496ab67bbc5c4da029925ed2de966f811dd26a644

Observation 49385e71-aeb2-465c-b170-8ef4c70b5d6d · outbound

This paper cites Large Language Models: A Survey.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Large Language Models: A Survey

Reference 14

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no resolver link, observed 2026-08-04T08:52:23.152337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:23.152337Z digest=sha256:9cec59b939ad6f9a382d9d63edb9d9136b507b0399f786eb6b0911540d46d1e7

Observation 8b24dafd-6844-4bae-a4fd-0b780ab5b5fd · outbound

This paper cites Flow: Modularized Agentic Workflow Automation.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Flow: Modularized Agentic Workflow Automation

Reference 15

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no resolver link, observed 2026-08-04T08:52:23.399703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:23.399703Z digest=sha256:731d3958fd288469994f280e5afeb4b1462a21a680512396871de6728bfabb66

Observation 569120ef-8cd5-4a0a-bfb9-f355b732e2c1 · outbound

This paper cites AgentSquare: Automatic LLM Agent Search in Modular Design Space.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows AgentSquare: Automatic LLM Agent Search in Modular Design Space

Reference 16

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no resolver link, observed 2026-08-04T08:52:23.570960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:23.570960Z digest=sha256:8083bcc46e0e120e1979c6a8d829d180063650741c39a4f97c26b7ff1e4475dd

Observation a90bbd73-fb12-43ce-a7fe-ab71ee2794cd · outbound

This paper cites InInternational Conference on Learning Representations (ICLR).

Global Optimization and Inference-Time Region Grafting for Agentic Workflows InInternational Conference on Learning Representations (ICLR)

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:23.741422Z digest=sha256:a151798fc20f7a0cae1cf7d0b36d9bd33eb073bb1317e11a914e89cb83ab95bd

Observation c530f464-9e02-4f9f-8422-1b62789c73e6 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 19

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no resolver link, observed 2026-08-04T08:52:24.170202Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:24.170202Z digest=sha256:e160566ec1f8398fddd4d2bd08e09e7b0d712c12417d25aacf0e6b938812bfe1

Observation e7e3fbb0-2131-45f0-8f88-c6911019cb21 · outbound

This paper cites A Survey of Large Language Models.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows A Survey of Large Language Models

Reference 21

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no resolver link, observed 2026-08-04T08:52:24.632934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:24.632934Z digest=sha256:c91c28495ad277583796392fcddcbd0db87c66797f9e10fe1b74f9d1c4536811

Observation f1c06da2-4a10-41fc-bb93-7d34d01ab07a · outbound

This paper cites In Sarkar, V.; Ryder, B.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows In Sarkar, V.; Ryder, B

Reference 1994

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no resolver link, observed 2026-08-04T08:52:22.391046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:22.391046Z digest=sha256:21ced51137c80d7dbd4b6411b0d253c691b46af7d5c350153afe8bb5023ebf82

Observation ca8a4d47-fa27-4cad-85fb-05c9398795aa · outbound

This paper cites Why Do Multi-Agent LLM Systems Fail?.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Why Do Multi-Agent LLM Systems Fail?

Reference 2012

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:21.501302Z digest=sha256:36a0e0b75a2467548a273015d3daa79af9e63681c9943ca0d2b490499d65691d

Observation 1eed0e35-3bac-4ac3-bdce-9067b5658b55 · outbound

This paper cites Program Synthesis with Large Language Models.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Program Synthesis with Large Language Models

Reference 2021

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:21.436132Z digest=sha256:f3f40a478af08f7c9877b2593eabce0dbb6e187f73845e8e50c5429e790a8bad

Observation 300a8ff8-08ee-45fa-b3c4-a1ca464b12be · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 2022

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no resolver link, observed 2026-08-04T08:52:23.938212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:23.938212Z digest=sha256:722d206140b680bd5be9aa052b45ab7507806eddb51f85ed0395e39ab0683c9e

Observation de3cb4bd-b037-45a1-93c8-ceef14dd87e3 · outbound

This paper cites AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

Reference 2023

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no resolver link, observed 2026-08-04T08:52:21.758006Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:21.758006Z digest=sha256:89b0d6722f26b628d64e9af42951eb930dc86a8c3b0f1a2b147031812ce40f15

Observation 9ba2c58f-db2f-48c2-8fcb-6994d5a26098 · outbound

This paper cites JMLR.org.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows JMLR.org

Reference 2024

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no resolver link, observed 2026-08-04T08:52:21.963495Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:21.963495Z digest=sha256:aed2aaa2ccc27ef23528072d3cfd71254ebac674181762ca9fb1041f6ce6ec35

Observation 62a38b24-ba37-4287-ac10-73c49b47349a · outbound

This paper cites Automated Design of Agentic Systems.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows Automated Design of Agentic Systems

Reference 2025

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source=pdf_text observed=2026-08-04T08:52:22.238228Z digest=sha256:2c31b42622a7592ce7733d9a7dfe7aee6d843dd511ef7c4ceb64d2e2119b8bd1

Observation 07575100-017f-476e-9808-013630192ddd · outbound

This paper cites EvoFlow: Evolving Diverse Agentic Workflows On The Fly.

Global Optimization and Inference-Time Region Grafting for Agentic Workflows EvoFlow: Evolving Diverse Agentic Workflows On The Fly

Reference 2026

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no resolver link, observed 2026-08-04T08:52:24.430006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:52:24.430006Z digest=sha256:9c8c1790c0e54d594b81a157cc2a4876f56378297f28f6f50f4ba222656cf623

Pith citing papers

No inbound Pith citation observations are available.