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Paper Citation Record · LEDGER

Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.12715.

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

pith.paper-citation-record.v1
2404.12715 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:21:28.081674Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T19:29:34.446281Z

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 4811b10c-5cb9-4b9a-a43e-7fcf1024659a · inbound

Mixture-of-Agents Enhances Large Language Model Capabilities cites this paper.

Mixture-of-Agents Enhances Large Language Model Capabilities Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-16T19:29:34.448840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-16T19:29:34.379712Z digest=sha256:15a4cbfd2a8da403978c786e5f5a024bfd0b6e889b01e97ee98de82e54853e56

Observation 0854f7bf-941b-457e-9c80-7013be8bdfc7 · inbound

Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models cites this paper.

Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration

Reference 2004

Resolution
unresolved
no resolver link, observed 2026-08-12T13:21:28.081674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:21:28.081674Z digest=sha256:77263d41889b5b2bf5ba90720a27d14fbbb70eae9fdf999c664dacf2551b37cb

Observation 82fa3219-812f-452b-b444-745fad4d90ba · inbound

KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems cites this paper.

KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T13:08:22.569826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T13:08:22.569826Z digest=sha256:132bf3045f76615086466f315015dbfc230d8f4cb88c2189cb834f1d0f258260

Observation 423ccf42-dfab-45af-983c-70023554c593 · inbound

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units cites this paper.

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T16:18:45.589412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:18:45.589412Z digest=sha256:9d797d90084b5030f08b8947aa0dc298902612dd42b831cf66b85f374b10e29b