Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2506.02678.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:01:24.147834Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T05:37:40.394539Z
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 2c93907a-1052-42cc-a055-453eef601138 · inbound
SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning cites this paper.
SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:01:24.147834Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 822f149e-6e33-47c7-9794-c105730b54fa · inbound
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey cites this paper.
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
Reference 100
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:54:17.171387Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 76d009b6-bc35-4fb5-b944-391bcc13f468 · inbound
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
Reference 210
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T19:21:48.287261Z
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 36d20faa-4c6e-4ce6-ab15-25fdcd4d949d · inbound
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models cites this paper.
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T12:33:17.035460Z
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 f5be0e0f-17ca-4d89-aab4-75739fd843c2 · inbound
Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It cites this paper.
Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
Reference 80
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T05:37:40.395838Z
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.