Paper Citation Record · LEDGER

TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2506.02678.

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

pith.paper-citation-record.v1
2506.02678 v3

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 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:30:15.172885Z

measured 0 of 1 external citation measurements

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 b3d37547-4a5b-497f-a0ae-c1ae225d5f7a · inbound

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review cites this paper.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:15.172885Z

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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.