Pith. sign in

Paper Citation Record · LEDGER

MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2501.00332.

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

pith.paper-citation-record.v1
2501.00332 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:37.339141Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T00:55:12.113189Z

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 fe82640e-a209-46a9-9fba-743cecd8d6a0 · inbound

ThinkTank: A Framework for Generalizing Domain-Specific AI Agent Systems into Universal Collaborative Intelligence Platforms cites this paper.

ThinkTank: A Framework for Generalizing Domain-Specific AI Agent Systems into Universal Collaborative Intelligence Platforms MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T11:15:37.339141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:15:37.339141Z digest=sha256:94c059ebb529947eaa43bb59723b1dcc2d3d9c24edba77db857d304c4e7d5787

Observation 2b52cf93-edc2-40cb-ba31-1c42ddcb5055 · inbound

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey cites this paper.

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 117

Resolution
unresolved
no resolver link, observed 2026-08-05T20:28:53.625688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T20:28:53.625688Z digest=sha256:5f4a6bbd04deca2a72263b63261b8452bdbe0d6ac64a54e43cd050e23457c09a

Observation 33687242-0ce8-4d96-9684-2fbb2e9f30fb · inbound

MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL cites this paper.

MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T01:32:17.468249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T01:31:40.920567Z digest=sha256:45c8d738b2372177bf83a8996c07fdc61ea44ba67dc06f61ed720836cb7481eb

Observation a38d9f08-98e7-43e3-accd-b9f60bf545df · inbound

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 197

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T23:13:15.391773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-14T23:13:15.016486Z digest=sha256:4b40a9face7c0b037c05a70dc5fd32a0f0efc09ab80ff8c84955a9081bdafc82

Observation 55889af2-4be0-44c0-944a-f32e82ba5ea5 · inbound

XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception cites this paper.

XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-21T10:40:00.506256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T10:39:09.209094Z digest=sha256:8a23a9aa18b76e615782d5369bf968fbffc9cae7c6be074568fd6ceea18bd323

Observation 89af3094-0100-4edd-901c-5d976f2f503b · inbound

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data cites this paper.

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:46:06.787483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T15:08:53.731480Z digest=sha256:ef1800a2347ad3033cc958d4cd995316288476d516d8556628d703b1c6e31b27

Observation 8da6516e-4da4-4f7e-b8e0-1f9ef4c0ca59 · inbound

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data cites this paper.

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-01T00:55:12.114753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-01T00:48:54.797750Z digest=sha256:827b8583445e4221f1dad304112ee38931a4a83d82c6338dd6c1b435e47c3be1

Observation e9f80398-675c-48fa-b63a-537ecefe56ac · inbound

Pezego-HITL: A policy-grounded large language model architecture for agricultural extension in Ghana cites this paper.

Pezego-HITL: A policy-grounded large language model architecture for agricultural extension in Ghana MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-02T03:21:21.971120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:21:21.971120Z digest=sha256:ad97cfe72cddc2d77d7de82f3ffde48a3d137bc529ac8c7edfc6bcf4dd81eaa5