Paper Citation Record · LEDGER

Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:51:09.947098Z

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 feb78bff-5130-4b6e-ae78-7903c888035d · inbound

How Knowledge Popularity Influences and Enhances LLM Knowledge Boundary Perception cites this paper.

How Knowledge Popularity Influences and Enhances LLM Knowledge Boundary Perception Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:09.947098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa198cee-3b96-4822-b4c5-75c112b9d7d1 · inbound

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs cites this paper.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:52.353944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7228dd48-639a-4945-80f9-284a311a92f5 · inbound

ISACL: Internal State Analyzer for Copyrighted Training Data Leakage cites this paper.

ISACL: Internal State Analyzer for Copyrighted Training Data Leakage Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T16:50:25.480809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0425caf5-6f61-44ad-8bf1-1a0c7d4111fe · inbound

Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking cites this paper.

Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T23:34:29.602050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-18T06:34:40.430872+00:00.

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 2026-08-18T06:34:40.430872+00:00.