Paper Citation Record · LEDGER

Adapting Large Language Models to Domains via Reading Comprehension

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2309.09530.

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

pith.paper-citation-record.v1
2309.09530 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:07:46.682614Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T07:55:31.038358Z

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 5647e516-a565-4874-8267-3ee41f1a8693 · inbound

LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models cites this paper.

LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models Adapting Large Language Models to Domains via Reading Comprehension

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T18:45:47.305514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 176256cd-d2eb-45ec-b03b-7acf58dfb73f · inbound

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases cites this paper.

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases Adapting Large Language Models to Domains via Reading Comprehension

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:24.070047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ddaf70-77d4-43ce-a333-05e2b1a83d04 · inbound

TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain cites this paper.

TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain Adapting Large Language Models to Domains via Reading Comprehension

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:15.850495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e31b9722-e278-482d-afd7-82c8179bf717 · inbound

Dynamic Skill Adaptation for Large Language Models cites this paper.

Dynamic Skill Adaptation for Large Language Models Adapting Large Language Models to Domains via Reading Comprehension

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T00:46:46.063231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1f322d4-51c9-4c2e-a110-80419a8937c4 · inbound

Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis cites this paper.

Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis Adapting Large Language Models to Domains via Reading Comprehension

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T00:35:28.206198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5072ac94-3bc7-462f-9682-b929782b6944 · inbound

Continual Pre-Training is (not) What You Need in Domain Adaption cites this paper.

Continual Pre-Training is (not) What You Need in Domain Adaption Adapting Large Language Models to Domains via Reading Comprehension

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:46.682614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a768c44-459e-44ac-b5f3-ef59d77b3083 · inbound

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training cites this paper.

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training Adapting Large Language Models to Domains via Reading Comprehension

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T11:59:52.170050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1a392d9-ebe4-477f-bdee-a022b706feee · inbound

Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation cites this paper.

Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation Adapting Large Language Models to Domains via Reading Comprehension

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:42:56.552847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c5e68e6-7a81-491a-8e42-5a2c6619bb03 · inbound

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models cites this paper.

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models Adapting Large Language Models to Domains via Reading Comprehension

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:16:25.969941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 57c2eba4-fd20-4ae9-82eb-1289e22ab8a6 · inbound

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis cites this paper.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Adapting Large Language Models to Domains via Reading Comprehension

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:21:09.426500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4434a2d4-5636-45c4-ad09-7b0a92869e56 · inbound

StakeBench: Evaluating Language Understanding Grounded in Market Commitment cites this paper.

StakeBench: Evaluating Language Understanding Grounded in Market Commitment Adapting Large Language Models to Domains via Reading Comprehension

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T21:43:58.593751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7117178-3b1a-4c95-b591-54621d51d14f · inbound

How Post-Training Shapes Biological Reasoning Models cites this paper.

How Post-Training Shapes Biological Reasoning Models Adapting Large Language Models to Domains via Reading Comprehension

Reference 68

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T07:55:31.040486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1996220-e044-4a6c-adb0-b97ecf698fdf · inbound

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling cites this paper.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Adapting Large Language Models to Domains via Reading Comprehension

Reference 131

Resolution: unresolved; no resolver link, observed 2026-08-02T09:51:03.581739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 996740e3-80fa-49f1-93f8-d5bbd0c3ed5a · inbound

Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges cites this paper.

Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges Adapting Large Language Models to Domains via Reading Comprehension

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T00:32:23.201233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ae54b23-0692-49aa-8d9a-f11440d50f24 · inbound

VeriForge: Mitigating Latent Knowledge Gaps in Narrative Drafting via Mixed-Initiative Scaffolding cites this paper.

VeriForge: Mitigating Latent Knowledge Gaps in Narrative Drafting via Mixed-Initiative Scaffolding Adapting Large Language Models to Domains via Reading Comprehension

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T12:33:43.114765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.