Paper Citation Record · LEDGER

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI

As of 8 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2606.23913.

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

pith.paper-citation-record.v1
2606.23913 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:39:23.985069Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

9 of 9 outbound references displayed

verified exact2
verified fuzzy0
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2a6cefd7-f986-43e2-b431-8b69f1fa1a72 · outbound

This paper cites LLM proposes, verifier disposes.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI LLM proposes, verifier disposes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T08:39:23.985069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d86c28e6-703c-4336-9572-292c131aad04 · outbound

This paper cites DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T10:39:45.204324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd823b22-7933-4a01-84ac-0532c5ce442b · outbound

This paper cites core of settled meaning.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI core of settled meaning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T08:39:23.985069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f7d9bd-4543-4450-86c9-901005fd685e · outbound

This paper cites The amount of tax that is owed is greater than or equal to zero.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI The amount of tax that is owed is greater than or equal to zero

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T08:39:23.985069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80462bf1-69b8-4661-b5bb-91e9c89498dc · outbound

This paper cites A Neurosymbolic Approach to Natural Language Formalization and Verification.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI A Neurosymbolic Approach to Natural Language Formalization and Verification

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-15T01:20:49.585649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5ea2b763-1675-42dc-b5ac-76676cd54fe3 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-04T10:39:45.202488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 00835e06-b6a5-4681-8938-6470b83b3258 · outbound

This paper cites Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:39:45.207554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f00f99d-2296-4aeb-aeff-82987fe82b18 · outbound

This paper cites deadline > trigger.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI deadline > trigger

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T08:39:23.985069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65906318-2c31-4a41-a66a-b1b2d28e9dcd · outbound

This paper cites an unresolved cited work.

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T08:39:23.985069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.