Paper Citation Record · LEDGER

SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.03284.

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

pith.paper-citation-record.v1
2411.03284 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:23:58.917129Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:35:44.199771Z

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 fb75e794-6fa8-4052-9827-b26625e5061e · inbound

A Survey on LLM-as-a-Judge cites this paper.

A Survey on LLM-as-a-Judge SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:35:44.204299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f67615ef-5366-4947-a05b-f5a7327e10e0 · inbound

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs cites this paper.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.917129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 065edb2c-6337-4c27-b62e-6ffbc0d040f6 · inbound

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs cites this paper.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.768620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed5823d3-acbb-425b-a2bc-49ac35cae20f · inbound

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation cites this paper.

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T12:15:05.309996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aee4ce5-d632-4ebb-aad9-0aaaf1399aae · inbound

S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning cites this paper.

S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:45:11.190127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cfb6be75-6a08-4b86-8c6d-d723df06f34e · inbound

Chained Recursive Language Models for Multi-Iteration Reasoning cites this paper.

Chained Recursive Language Models for Multi-Iteration Reasoning SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T04:47:19.356870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.