Paper Citation Record · LEDGER

Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.10881.

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

pith.paper-citation-record.v1
2406.10881 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:59:38.688865Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d4ac8346-95d8-49b3-be49-5d588b7dcc2e · inbound

GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation cites this paper.

GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T17:31:59.779480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c385bbfb-ad41-44ef-b645-6f04ce20805a · inbound

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness cites this paper.

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T04:59:38.688865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a159d84a-ae95-4e06-9cf9-a4475cddbbdc · inbound

Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation cites this paper.

Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:41:35.265426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46abb4db-fb68-4764-8923-4d8a8b8e8ec4 · inbound

Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning cites this paper.

Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:25.365761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4dfecbd-ea62-4c04-899f-f2d9dbe99164 · inbound

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions cites this paper.

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:08.098884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74d7edb8-60a3-44c8-80fd-011010052c98 · inbound

Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research cites this paper.

Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T04:40:16.553910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fcdb1449-eec9-4890-bddb-c7b6867ac1bf · inbound

Automatically Evolving Prompt Guidelines for Task-Specific Optimization cites this paper.

Automatically Evolving Prompt Guidelines for Task-Specific Optimization Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T14:46:28.313288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.