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Paper Citation Record · LEDGER

Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

As of 1 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.11677.

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

pith.paper-citation-record.v1
2502.11677 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-01T06:32:01.292127+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T08:19:25.544186Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T05:16:39.603541Z

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 9b5dec6c-b790-439e-a25b-241061558cf9 · inbound

Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards cites this paper.

Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-15T13:50:02.353340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

source=pdf_text observed=2026-05-15T13:49:11.758336Z digest=sha256:c45afe43b1e8967f46f48822087ef4e5055be80a2e21013b7d1048348ebb9643

Observation 177ecf8a-02a1-4912-bcdc-162ebf51aa30 · inbound

Can LLM Rerankers Predict Their Own Ranking Performance? cites this paper.

Can LLM Rerankers Predict Their Own Ranking Performance? Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-07-02T05:16:39.604903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

source=arxiv_source observed=2026-06-28T08:19:25.544186Z digest=sha256:b67277ad5c37e7411cdd29bbd0536d66141654c941c37822504357e45ca4e287