Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.06985.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T21:03:13.289380Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T18:45:42.765422Z
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 c6fdfcdf-b3b3-4dc0-9a83-71a1ed37e48a · inbound
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering cites this paper.
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T21:03:13.289380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d96f7d36-be33-47db-9b86-ca3418007a30 · inbound
KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows cites this paper.
KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:45:42.772387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5384ef0e-70af-42ac-9a04-e2eb181aa46b · inbound
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems cites this paper.
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T18:50:10.586771Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.