Paper Citation Record · LEDGER

Aligner: Efficient Alignment by Learning to Correct

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2402.02416.

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

pith.paper-citation-record.v1
2402.02416 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:24:54.804235Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T07:12:28.734663Z

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 444ac8cd-d294-49ee-a8be-5b21bf515177 · inbound

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss cites this paper.

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss Aligner: Efficient Alignment by Learning to Correct

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T21:24:54.804235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15d03d4c-14e1-499b-8722-cfa0c39ccf76 · inbound

On Almost Surely Safe Alignment of Large Language Models at Inference-Time cites this paper.

On Almost Surely Safe Alignment of Large Language Models at Inference-Time Aligner: Efficient Alignment by Learning to Correct

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:40.984315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70a126d3-96d7-4fd9-9b44-91aec349b42b · inbound

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation cites this paper.

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation Aligner: Efficient Alignment by Learning to Correct

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:45.487622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0854d417-b344-4473-a723-5b8fa1bcce57 · inbound

A Survey on Training-free Alignment of Large Language Models cites this paper.

A Survey on Training-free Alignment of Large Language Models Aligner: Efficient Alignment by Learning to Correct

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T21:18:41.955175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 863bd64f-3983-4473-a368-962bb21d5915 · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization Aligner: Efficient Alignment by Learning to Correct

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:56:05.419222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b8e9802-1b8e-448a-ab06-e791cd561117 · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization Aligner: Efficient Alignment by Learning to Correct

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:26.385783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4bc87aac-e863-4484-b04b-3a765d724650 · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization Aligner: Efficient Alignment by Learning to Correct

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:12:28.737468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e115bf7-aa03-4c57-9704-95ed4123ad11 · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization Aligner: Efficient Alignment by Learning to Correct

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T14:53:24.080687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.