Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.05406.
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 2026-08-09T13:58:45.302879Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T06:02:25.531295Z
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 084a8d86-641c-4891-a9b3-f1f1c1fa9670 · inbound
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing cites this paper.
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T13:58:45.302879Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2fce1a50-a268-4f6f-8304-2af8e58b9266 · inbound
When One LLM Drools, Multi-LLM Collaboration Rules cites this paper.
When One LLM Drools, Multi-LLM Collaboration Rules Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T22:33:09.187389Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 04e8d916-8630-450c-bf4c-64573bec7b1f · inbound
AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM cites this paper.
AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T06:02:25.533881Z
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.