Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T19:34:04.077230Z
Paper Citation Record · LEDGER
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
As of 19 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 5 inbound Pith citation observations for arXiv:2603.01875.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T01:53:01.113514Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T16:29:56.809337Z
Reference resolution
3 of 3 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 75daa48a-7317-48c5-8f98-5bf2e3af7ab5 · outbound
This paper cites Qwen3 Technical Report.
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models Qwen3 Technical Report
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T19:34:03.966126Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e57ddad5-c425-49e9-a18a-2ab4c4e2e32a · outbound
This paper cites A Dual-Space Framework for General Knowledge Distillation of Large Language Models.
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models A Dual-Space Framework for General Knowledge Distillation of Large Language Models
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T19:34:04.077230Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 67ec87ab-d604-4400-99f1-6aa13e6582d9 · outbound
This paper cites Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, and 1 others.
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, and 1 others
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T19:34:03.839733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 5156038b-526d-4c52-86b1-faa858c1a7d7 · inbound
SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation cites this paper.
SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Reference 59
- Resolution
- verified exact
- arxiv_id, observed 2026-07-20T02:18:26.584736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f9313a53-396e-47b7-99a6-774415001a3b · inbound
SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation cites this paper.
SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-07-20T02:18:26.584736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6eca83c9-a64b-4f7b-8aee-af52084627cf · inbound
AsyncOPD: How Stale Can On-Policy Distillation Be? cites this paper.
AsyncOPD: How Stale Can On-Policy Distillation Be? KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-07-20T02:18:26.584736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d2a62de8-f6d3-494e-93d1-7fd25d2b856c · inbound
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation cites this paper.
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T03:38:15.876016Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 78692746-b95c-49d4-804c-899f308ed6ba · inbound
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation cites this paper.
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T01:53:01.113514Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.