Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:11:52.803996Z

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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.

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.

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.

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.

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 2026-08-10T06:31:04.303077+00:00.

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.

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.

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.

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.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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.