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Paper Citation Record · LEDGER

CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2404.01663.

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

pith.paper-citation-record.v1
2404.01663 v6

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 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:32:58.874081Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:05:30.670213Z

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 a70badb6-48e0-4f63-bd32-8cddb9aca1a6 · 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 CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T19:10:14.453120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:10:14.453120Z digest=sha256:8690fe6f0bc7b6c074195ddae24d6ab1bc3891f087da87869bf9368c1f1fe23f

Observation bd10c118-04e0-4971-95ff-5655458199aa · inbound

AI PERSONA: Towards Life-long Personalization of LLMs cites this paper.

AI PERSONA: Towards Life-long Personalization of LLMs CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T13:31:41.274410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:31:41.274410Z digest=sha256:fc5cb6a4b119a7d5e97046f567948f116232b17872388b4293a53e4b60b0ce5e

Observation d4617ee2-bb5b-464f-b408-ca907c96e644 · inbound

ForgetMe: Evaluating Selective Forgetting in Generative Models cites this paper.

ForgetMe: Evaluating Selective Forgetting in Generative Models CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-16T12:32:58.874081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:32:58.874081Z digest=sha256:36b2d0e9d652cd53d09c9046d7379398dfdcda47780993c3f06bed0aabf344e1

Observation c3036e81-923d-41ae-a7e2-271da506d254 · inbound

Training-Free Multimodal Large Language Model Orchestration cites this paper.

Training-Free Multimodal Large Language Model Orchestration CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-19T00:12:54.112232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-19T00:12:39.834892Z digest=sha256:9a4d4ffe7908019987b6a88f164c43f41cbb8e66a8523e8fd470b0a7f3a05726

Observation ddaacb95-eb59-4eda-97c3-3e38f1a42de9 · inbound

Training-Free Multimodal Large Language Model Orchestration cites this paper.

Training-Free Multimodal Large Language Model Orchestration CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:05:30.673078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-25T08:02:15.950975Z digest=sha256:4e261635ba32b3fd7b74c367d0437446f89408e1c12c5663f99da3920ef1fe9b

Observation 353e85dd-3312-4a49-adf5-274929a3d896 · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:04.364500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:53:04.364500Z digest=sha256:41ae9779e61b11e4f38af13361815070cde024166c881444fed27a1f514530e2

Observation abc734bf-11d4-4969-b349-88615310ec8d · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:15.035778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:53:15.035778Z digest=sha256:6e0262b85d47bfc73d6c28050fb96c51f3c0d9dc5464e3ce2cd90238d1ba3980

Observation d9c6956c-ddfb-4946-ac4d-a48a06b1a062 · inbound

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting cites this paper.

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-18T12:36:22.638795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-18T12:33:46.039899Z digest=sha256:ff65f62ce8c1b6d235d730ddc5003b22e6ce3927499249e9560d6b2a31d26209