Pith. sign in

Paper Citation Record · LEDGER

Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2403.12881.

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

pith.paper-citation-record.v1
2403.12881 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:18:48.456453Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:59:38.156324Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3a1c2c16-6736-4381-83c8-8ba563736607 · inbound

OS-ATLAS: A Foundation Action Model for Generalist GUI Agents cites this paper.

OS-ATLAS: A Foundation Action Model for Generalist GUI Agents Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 120

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T09:29:27.472265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-13T09:29:27.173784Z digest=sha256:e74bbd439295fda28316dcf2bae4f5b082227660480e231521a3ce608e33ca64

Observation 30520ba5-cb3c-458a-b185-1249ecbafd7e · inbound

Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms cites this paper.

Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-12T19:10:14.512091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:10:14.512091Z digest=sha256:0ea33187dfcfa3d6467ff1ea99d996e776ac0da22954dc271304d21a8176e9b2

Observation 6f793bbd-7632-4028-b1b5-75ba93f4de6d · inbound

Training Agents with Weakly Supervised Feedback from Large Language Models cites this paper.

Training Agents with Weakly Supervised Feedback from Large Language Models Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T10:08:13.122617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T10:08:13.122617Z digest=sha256:97359bd2ae7a69bd6ec224703373a3b2ed97da74aa2c05cec4513dfcab8959c3

Observation 1218ee6a-0a28-4055-9885-1d2a3597e456 · inbound

Towards Adaptive Mechanism Activation in Language Agent cites this paper.

Towards Adaptive Mechanism Activation in Language Agent Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T05:09:47.713097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:09:47.713097Z digest=sha256:21e8e638d004b2ae1f7af64e1e40f5efce25ac3a192cb42dfb66758864043a5d

Observation d06d3c34-de68-46c0-8e9c-2234309c1925 · inbound

Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning cites this paper.

Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T11:58:35.132157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:58:35.132157Z digest=sha256:fc2cfd00b156f344dc5f067e9dd25d6bc4581e035d4fb9cba622b1b991470b07

Observation 598e8e12-2ebd-4978-a8f3-2f2eaa86c131 · inbound

AgentRefine: Enhancing Agent Generalization through Refinement Tuning cites this paper.

AgentRefine: Enhancing Agent Generalization through Refinement Tuning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T22:24:43.043441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:24:43.043441Z digest=sha256:89a391e9937b10ff2e247e7b9703a9f2e660c8fcc99d0a8f0e64c3c3df63a559

Observation c1c5df10-83cd-45a9-bdc2-bba43442fbfc · inbound

Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments cites this paper.

Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T18:56:44.467258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T18:56:44.467258Z digest=sha256:a26a469b16ace889616ec8d2080f3477bc32b19134978c6fc56e57981e607128

Observation d0d3ecae-0ccb-4ac0-9a07-f0eaa1188143 · inbound

Exploring Expert Failures Improves LLM Agent Tuning cites this paper.

Exploring Expert Failures Improves LLM Agent Tuning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-16T12:18:48.456453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:18:48.456453Z digest=sha256:94108be194584926e8c6ab59ec9904fed9034c935710ffda98c18f076cbf8b85

Observation eaaeaa0b-3478-4bef-9c7e-9b35d6f1438b · inbound

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment cites this paper.

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 170

Resolution
unresolved
no resolver link, observed 2026-08-16T11:24:12.489752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:24:12.489752Z digest=sha256:28a50691a89be6df2dac663cc1829774732c661ef919025ca6e8845e08e48b99

Observation 1f8fa453-882f-4657-987c-a9e57eee856e · inbound

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review cites this paper.

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:57:38.200063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T02:57:37.873567Z digest=sha256:849b177953f89ae1cf8c8a4af96e832883a6d4a22c2be2c671e3684cded9d7af

Observation e28c3ea0-5df5-4bd5-a001-36e3e12aabf3 · inbound

Large Language Models for Planning: A Comprehensive and Systematic Survey cites this paper.

Large Language Models for Planning: A Comprehensive and Systematic Survey Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:52.803996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:52.803996Z digest=sha256:8cf43231e13d22877d480e1d55fe3117becf3ab26b92c159870d9485c8d885b2

Observation 78a6b0bd-7100-48fd-a47f-b6ba2fe4174f · inbound

SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attribution cites this paper.

SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attribution Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T13:53:03.263907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:53:03.263907Z digest=sha256:52fc6b5cd627d9cb1d39cd1f39af5d35a8b8cf9822c73afc5b343bfa1993e662

Observation 762efeb2-6d0c-4bec-97a0-d036d833bbeb · inbound

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning cites this paper.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T13:24:49.490774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:24:49.490774Z digest=sha256:87c55e3237d2f62730b594fb6bf7a71a05e050d5ca6e155be66b3f49c08a630b

Observation fa3b2a13-dba9-4065-a0a5-b92123ebd0cd · inbound

OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation cites this paper.

OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T12:42:32.124907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:42:32.124907Z digest=sha256:a707ae6f8c7aa5f752424ac16beb168304a206e84a243f07e8dc9f27a37fe81a

Observation 5c3b7d5c-77e2-440a-8ddc-e73840f5b7e8 · inbound

Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games cites this paper.

Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-19T12:02:16.666253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-19T12:01:42.681135Z digest=sha256:4c000095f997e56c77da63b1626907a8bad91b5ee72a6c9ae47095d2e97dc391

Observation b41b9cc9-cd38-4c58-9845-d34dd9504529 · inbound

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning cites this paper.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T12:18:32.447571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:18:32.447571Z digest=sha256:462a7f5469a92fed8ec2838454ab2aea2fdb054cb097df958fe0befb38694b4c

Observation faa91e72-c3b3-467e-ba65-26e99784678e · inbound

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation cites this paper.

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T22:51:57.967973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:51:57.967973Z digest=sha256:8af4b91b1ad43aa755bddea83b9b9c7dd55b2d98ae3225436fc57feba7dca125

Observation 27e4603f-bf47-4a24-a24e-213661cf70c5 · inbound

S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner cites this paper.

S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-05T18:12:33.676893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:12:33.676893Z digest=sha256:472366b8dac11cb520b306dd7eee43c1a080600000a525cadda02fa2889f9eb9

Observation 38fa9ab9-586a-404f-96be-187b5f97a243 · inbound

Toward Efficient Agents: Memory, Tool learning, and Planning cites this paper.

Toward Efficient Agents: Memory, Tool learning, and Planning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-03T09:21:33.649879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:21:33.649879Z digest=sha256:a2f16762f334e0797d9515f052c6cede9c4b3d045364819290e55689bb9e04eb

Observation da7b6cf1-46d5-4dd2-a49f-2de1a713de4a · inbound

Compositional Skill Routing for LLM Agents: Decompose, Retrieve, and Compose cites this paper.

Compositional Skill Routing for LLM Agents: Decompose, Retrieve, and Compose Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T21:18:59.722923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T00:37:50.570757Z digest=sha256:d9cf82464002f7543cafcff66ab2a598725c323dee3fd7c0551f59739ba707b6

Observation 99b27133-1e88-4b42-b6ae-f572b1b2267a · inbound

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents cites this paper.

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T06:59:38.157597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-26T13:56:51.914966Z digest=sha256:5f811d9477fc760ca95745c19ff1ee447e0e7723ff9b08d8809ce48abea7a5c8

Observation aa1023e0-2103-4140-8bcf-38a7d6edef7f · inbound

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories cites this paper.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:33:54.851493Z digest=sha256:07e696b68e395bcb2204e9ae838201385acfe08e0205bd88aa2fd30198c12b92