Paper Citation Record · LEDGER

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection

As of 14 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2605.31238.

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

pith.paper-citation-record.v1
2605.31238 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T22:26:03.073002Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-08T14:57:43.125819Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T15:05:03.555925Z

Reference resolution

49 of 49 outbound references displayed

verified exact4
verified fuzzy0
unresolved44
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad4f363e-799c-441e-aec4-ff4bdfa0f77b · outbound

This paper cites Self-RAG: Learn- ing to retrieve, generate, and critique through self-reﬂection.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Self-RAG: Learn- ing to retrieve, generate, and critique through self-reﬂection

Reference 1

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Observation d71d2720-7e70-4db7-aaa7-77db89eae3a2 · outbound

This paper cites AlpaGasus: Training a better alpaca with fewer data.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection AlpaGasus: Training a better alpaca with fewer data

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation ad926a5a-68f3-47e6-b699-4a6b9e6b1e43 · outbound

This paper cites Program of thoughts prompt- ing: Disentangling computation from reasoning for numerical reasoning tasks.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Program of thoughts prompt- ing: Disentangling computation from reasoning for numerical reasoning tasks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 0d3113c8-c10b-456f-b1d5-ddb9072d04c9 · outbound

This paper cites DoG-Instruct: Towards premium instruction-tuning data via text-grounded instruction wrapping.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection DoG-Instruct: Towards premium instruction-tuning data via text-grounded instruction wrapping

Reference 4

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Observation 026f4ea4-0978-4963-8b48-3edccffe327a · outbound

This paper cites QLoRA: Efﬁcient ﬁne- tuning of quantized LLMs.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection QLoRA: Efﬁcient ﬁne- tuning of quantized LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 847308a0-3616-4da0-bc98-c6fd886f65fa · outbound

This paper cites SciNets: Graph-constrained multi-hop reasoning for scientiﬁc literature syn- thesis.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection SciNets: Graph-constrained multi-hop reasoning for scientiﬁc literature syn- thesis

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:32:44.583981Z

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Observation 5ea04bd4-38b3-4d24-9b4a-0f0f2fd68a89 · outbound

This paper cites Faith and fate: Limits of transformers on compositionality.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Faith and fate: Limits of transformers on compositionality

Reference 7

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Observation 24f1c75a-ebc7-4ee8-8d1f-5e839e9c5f3f · outbound

This paper cites IIRC: A dataset of incomplete information reading comprehension questions.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection IIRC: A dataset of incomplete information reading comprehension questions

Reference 8

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Observation d2214417-8d7e-43a6-97af-91ab5b3eae3c · outbound

This paper cites SimCSE: Simple contrastive learning of sen- tence embeddings.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection SimCSE: Simple contrastive learning of sen- tence embeddings

Reference 9

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Source-reported events for the cited work

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Observation f302523a-4303-40c8-8e80-33172673e343 · outbound

This paper cites TrueTeacher: Learning factual consistency evaluation with large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection TrueTeacher: Learning factual consistency evaluation with large language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation d118c817-d282-4a5d-9f9f-e6c010bc64dd · outbound

This paper cites Did Aristotle use a laptop? A question answering benchmark with implicit reasoning strategies.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Did Aristotle use a laptop? A question answering benchmark with implicit reasoning strategies

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation ca43bf56-5fd5-4afc-a6a5-951ac884b92f · outbound

This paper cites LegalBench: A collaboratively built benchmark for measuring legal reasoning in large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection LegalBench: A collaboratively built benchmark for measuring legal reasoning in large language models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 7b555c43-706d-459b-97ad-bbaefd98321b · outbound

This paper cites Synthetic Data RL: Task Definition Is All You Need.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Synthetic Data RL: Task Definition Is All You Need

Reference 13

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Observation 2b9aa21c-3390-4f29-aeeb-9e2a47648f24 · outbound

This paper cites CUAD: An expert-annotated NLP dataset for legal contract review.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection CUAD: An expert-annotated NLP dataset for legal contract review

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation f23681b9-70cf-4c09-a68b-770a720f86b7 · outbound

This paper cites Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 357048c9-3df7-4f4f-bff2-09e20efff17e · outbound

This paper cites Unnatural instructions: Tuning language models with (almost) no human labor.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Unnatural instructions: Tuning language models with (almost) no human labor

Reference 16

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Observation d7cf4cbe-c933-4bb3-b79a-68d1624c6f04 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection LoRA: Low-rank adaptation of large language models

Reference 17

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Observation f16f0574-4731-4c04-9b08-8c8371ec34aa · outbound

This paper cites Active retrieval augmented generation.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Active retrieval augmented generation

Reference 18

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Observation fa995294-a3b3-480b-8723-1cf77dc106be · outbound

This paper cites Billion-scale similarity search with GPUs.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Billion-scale similarity search with GPUs

Reference 19

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Observation fde82150-5dab-42fa-b4b2-d2d3a3d3e39f · outbound

This paper cites TriviaQA: A large scale dis- tantly supervised challenge dataset for reading comprehension.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection TriviaQA: A large scale dis- tantly supervised challenge dataset for reading comprehension

Reference 20

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Observation b67b4d34-5934-40df-b5c1-79da0768c995 · outbound

This paper cites Learning from synthetic data improves multi-hop reasoning.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Learning from synthetic data improves multi-hop reasoning

Reference 21

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Observation 4218954f-485d-494b-88c1-02fcb05a1b4c · outbound

This paper cites Dense passage retrieval for open-domain question answering.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Dense passage retrieval for open-domain question answering

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

Source-reported events for the cited work

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Observation 7e4f5fbc-b8f8-44dd-83cb-f4fc78bf79f6 · outbound

This paper cites Decomposed prompting: A modular approach for solving complex tasks.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Decomposed prompting: A modular approach for solving complex tasks

Reference 23

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Observation 39fb8e61-1f20-4576-9163-398e8d04003e · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Overcoming catastrophic forgetting in neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 962ec0d1-7ef6-4404-b4ba-e88cfe597d54 · outbound

This paper cites Automatic inter- document multi-hop scientiﬁc QA generation.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Automatic inter- document multi-hop scientiﬁc QA generation

Reference 25

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Source-reported events for the cited work

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Observation 4e5c2075-54f4-48fb-bf2e-147f6060d7df · outbound

This paper cites FactCG: Enhancing fact checkers with graph-based multi-hop data.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection FactCG: Enhancing fact checkers with graph-based multi-hop data

Reference 26

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Source-reported events for the cited work

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Observation 897727d7-4e35-4246-b693-483efa713d7c · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive NLP tasks.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Retrieval- augmented generation for knowledge-intensive NLP tasks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 166cadb0-ddb2-45e6-a64d-9b4b152b54c7 · outbound

This paper cites DESIGNER: Design-logic-guided multidisciplinary data synthesis for LLM reasoning.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection DESIGNER: Design-logic-guided multidisciplinary data synthesis for LLM reasoning

Reference 28

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Observation d0f281ba-69ea-4725-af2e-c4ed29b4ee44 · outbound

This paper cites SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models

Reference 29

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Observation b557ed47-8a01-44ba-8e24-e85011ff7bb7 · outbound

This paper cites FActScore: Fine-grained atomic evalua- tion of factual precision in long form text generation.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection FActScore: Fine-grained atomic evalua- tion of factual precision in long form text generation

Reference 30

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Observation f48e95a7-6624-41dd-b4cc-53457445d7da · outbound

This paper cites Training language models to follow instructions with human feedback.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Training language models to follow instructions with human feedback

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation f2a5289d-7a46-4871-811a-c92b9ff65230 · outbound

This paper cites Mea- suring and narrowing the compositionality gap in language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Mea- suring and narrowing the compositionality gap in language models

Reference 32

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Observation f74bea4b-7fa3-4256-8247-6ff33f26e1d2 · outbound

This paper cites Sentence-BERT: Sentence embeddings using Siamese BERT-networks.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Sentence-BERT: Sentence embeddings using Siamese BERT-networks

Reference 33

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Observation bb5d6d51-a483-4882-a3fc-cff37953a72c · outbound

This paper cites The web as a knowledge-base for answering complex questions.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection The web as a knowledge-base for answering complex questions

Reference 34

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Observation ab987f10-e25f-4487-ac1b-838beef63164 · outbound

This paper cites MuSiQue: Multihop questions via single-hop question generation.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection MuSiQue: Multihop questions via single-hop question generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 2ba47d8a-91bb-4ad0-a952-9b02c33d2357 · outbound

This paper cites MAUD: An expert- annotated legal NLP dataset for merger agreement understanding.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection MAUD: An expert- annotated legal NLP dataset for merger agreement understanding

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation c99cbd08-9d63-4f73-8764-4f90914b31be · outbound

This paper cites Self-consistency improves chain of thought reasoning in lan- guage models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Self-consistency improves chain of thought reasoning in lan- guage models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation c566d5c1-69c3-46b0-bee8-393a3bf4ab94 · outbound

This paper cites Self-Instruct: Aligning language models with self-generated instruc- tions.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Self-Instruct: Aligning language models with self-generated instruc- tions

Reference 38

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Observation 0cc9934d-bfb0-498c-9fa9-0e78cb5e298a · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Chain-of-thought prompting elicits reasoning in large language models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 425a3c37-710e-44a9-83e5-a90e4932c1fa · outbound

This paper cites WizardLM: Empowering large language models to follow complex instructions.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection WizardLM: Empowering large language models to follow complex instructions

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 6ac6ba00-f3b6-4129-bee1-d430c6e81236 · outbound

This paper cites HotpotQA: A dataset for diverse, explainable multi-hop question answering.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection HotpotQA: A dataset for diverse, explainable multi-hop question answering

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 2ad0d4c0-7070-428c-b9cc-5fbd617bc2d7 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Tree of thoughts: Deliberate problem solving with large language models

Reference 42

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Source-reported events for the cited work

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Observation 677caf56-849b-4c0b-aac3-a041a8569d1b · outbound

This paper cites QA- GNN: Reasoning with language models and knowledge graphs for question answering.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection QA- GNN: Reasoning with language models and knowledge graphs for question answering

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation 1d16e7c8-28ea-485f-83a8-c254bcaeafc8 · outbound

This paper cites AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:17:09.666261Z

Source-reported events for the cited work

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Observation e94c4fe0-20f3-4022-9a82-3e88a4a98fd5 · outbound

This paper cites GreaseLM: Graph reasoning enhanced language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection GreaseLM: Graph reasoning enhanced language models

Reference 45

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Observation 0fb937e2-81d4-4bf2-8738-ffda7958039c · outbound

This paper cites LIMA: Less is more for alignment.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection LIMA: Less is more for alignment

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

Source-reported events for the cited work

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Observation 488ff751-54d2-4a59-97db-29e14cc2aaa3 · outbound

This paper cites Least-to-most prompting enables complex reasoning in large language models.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection Least-to-most prompting enables complex reasoning in large language models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-28T22:26:03.073002Z

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Observation f07b4bc8-bf48-466c-bb42-d43e8e9fb6f9 · outbound

This paper cites ﬁnd candidate evidence paths that the teacher comfortably verbalizes.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection ﬁnd candidate evidence paths that the teacher comfortably verbalizes

Reference 48

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Observation 3416c61b-bed0-4cb3-bec4-a4ad737c3822 · outbound

This paper cites no transfer.

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection no transfer

Reference 49

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Pith citing papers

Observation bc5857e0-eaf8-46ff-8f94-0def8d3ac347 · inbound

LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis cites this paper.

LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis Scaling Multi-Hop Training Data via Graph-Constrained Path Selection

Reference 1

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