Let the Target Select for Itself: Data Selection via Target-Aligned Paths | Paper Citation Record

Observation 99c18575-7263-4988-b3ed-936a246efac5 · outbound

This paper cites Intrinsic dimensionality explains the effectiveness of language model fine-tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Intrinsic dimensionality explains the effectiveness of language model fine-tuning

Reference 1

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Observation 9667eca4-8983-4d2d-89f4-c3c0d20c07fe · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.967401Z

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Observation d11a3628-97b4-4887-9026-93101beb8d51 · outbound

This paper cites Influence-preserving proxies for gradient-based data selection in llm fine-tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Influence-preserving proxies for gradient-based data selection in llm fine-tuning

Reference 3

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Observation c4365d9d-ddb0-4d45-ad1f-213a8224f701 · outbound

This paper cites Task- aware data selection via proxy-label enhanced distribution matching for LLM finetuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Task- aware data selection via proxy-label enhanced distribution matching for LLM finetuning

Reference 4

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Observation f6bd0787-87eb-46bc-b09a-d5a14b01cfa7 · outbound

This paper cites TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages.Transactions of the Association for Computational Linguistics, 8:454–470.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages.Transactions of the Association for Computational Linguistics, 8:454–470

Reference 5

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Observation 89542134-1b7a-4bec-83b7-981f164e92da · outbound

This paper cites Free Dolly: Introducing the world’s first truly open instruction-tuned LLM.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Free Dolly: Introducing the world’s first truly open instruction-tuned LLM

Reference 6

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Observation 9bbd4c7f-6f27-4cd1-838c-3a000f5797ea · outbound

This paper cites an unresolved cited work.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-12T21:41:53.968148Z

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Observation 9402e397-6a2e-405f-9bfa-6e6acf80e427 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Imagenet: A large-scale hierarchical image database

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.974687Z

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Observation 4f651c0c-df63-4051-9201-64ea74abd58f · outbound

This paper cites Influential Language Data Selection via Gradient Trajectory Pursuit.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Influential Language Data Selection via Gradient Trajectory Pursuit

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:18.000071Z

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Observation 08c9b519-ccb7-4c55-b33f-8396271edd15 · outbound

This paper cites Greedy information projection for llm data selection.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Greedy information projection for llm data selection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.981725Z

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Observation 766d605e-be55-4eac-ad40-a07c8f86bb7e · outbound

This paper cites an unresolved cited work.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Unresolved cited work

Reference 11

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Observation 52f48ce8-b29d-4d7a-b7c9-80ebee7ed1eb · outbound

This paper cites The Llama 3 Herd of Models.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths The Llama 3 Herd of Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-12T02:51:18.002707Z

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Observation cf30433b-a810-41d8-8875-6c3027892112 · outbound

This paper cites The Early Phase of Neural Network Training.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths The Early Phase of Neural Network Training

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.984752Z

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Observation 5558e69b-9189-4745-9572-a3602fad7572 · outbound

This paper cites Golub and Victor Pereyra.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Golub and Victor Pereyra

Reference 14

Resolution: verified exact; doi, observed 2026-05-12T02:51:16.878526Z

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Observation a8529f82-f5b4-4e97-aa22-9ebdbaeae50f · outbound

This paper cites BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-02T03:04:01.491891Z

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Observation 550f9f04-5c29-4760-b6be-41661a8fe1d1 · outbound

This paper cites Deep residual learning for im- age recognition.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Deep residual learning for im- age recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.958989Z

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Observation 749a6804-3aa2-40da-9b07-b2293be2f563 · outbound

This paper cites Measuring massive multitask language understanding.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Measuring massive multitask language understanding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.988181Z

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Observation 166b8a95-79de-4a90-bc23-bdf600f4d432 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T02:51:17.959198Z

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Observation ae78e9a5-df41-40ed-ab63-a8187e625038 · outbound

This paper cites Train on validation (tov): Fast data selection with applications to fine-tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Train on validation (tov): Fast data selection with applications to fine-tuning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.964485Z

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Observation 8f6daf97-d261-4711-9fc0-f670468deaad · outbound

This paper cites GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:51:18.014028Z

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Observation e4ebf131-45a7-4e03-b70f-1002d968f752 · outbound

This paper cites GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:18.011275Z

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Observation 40ccfff8-707f-4637-ad11-559185623a90 · outbound

This paper cites Understanding black-box predictions via influence functions.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Understanding black-box predictions via influence functions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.965388Z

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Observation b1ff6c8d-8fa1-4afc-8ec6-6b69572c4beb · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths A study of cross-validation and bootstrap for accuracy estimation and model selection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.971641Z

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Observation e3e69cf7-3e3c-42f0-bcdf-b44927020ebc · outbound

This paper cites Openassistant conversations–democratizing large language model alignment.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Openassistant conversations–democratizing large language model alignment

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.978103Z

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Observation 44ade208-d3bd-4715-a233-5a5682ae47a1 · outbound

This paper cites Learning multiple layers of features from tiny images.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Learning multiple layers of features from tiny images

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.951292Z

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Observation 7c00a638-c554-4ed2-ab2d-0166f0acf9bb · outbound

This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.975709Z

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Observation f8f3bf5e-b900-4bde-8fa8-cd38d5e891e8 · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths The flan collection: Designing data and methods for effective instruction tuning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.955335Z

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Observation a905dfe8-60ad-4f01-b0b8-9e2672c4bc32 · outbound

This paper cites GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:02:09.973489Z

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Observation f6c7d58d-6019-43a6-b846-6efb17866f9e · outbound

This paper cites Prioritized training on points that are learnable, worth learning, and not yet learnt.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Prioritized training on points that are learnable, worth learning, and not yet learnt

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.944323Z

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Observation 0a5d0405-beb9-41cb-b4d1-f5a917ca9427 · outbound

This paper cites Coresets for Data-efficient Training of Machine Learning Models.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Coresets for Data-efficient Training of Machine Learning Models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.970250Z

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Observation 8b205f3b-d216-42c8-a6db-ef3ac152a9df · outbound

This paper cites Token cleaning: Fine-grained data selection for llm supervised fine-tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Token cleaning: Fine-grained data selection for llm supervised fine-tuning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:18.016819Z

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Observation 10ae2e7a-0f0a-454a-ba25-4ae2b7a90be4 · outbound

This paper cites Trak: Attributing model behavior at scale.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Trak: Attributing model behavior at scale

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.947558Z

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Observation 7f7d7e2a-27b9-4665-afb5-401665cc1611 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Deep learning on a data diet: Finding important examples early in training

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.999110Z

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Observation 18e8a8f3-7825-4142-8ae1-6c78297437ac · outbound

This paper cites Elenberg, and Kilian Q.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Elenberg, and Kilian Q

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.991347Z

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Observation 7b219f61-860f-454c-9ac9-fd13d30be752 · outbound

This paper cites Estimating Training Data Influence by Tracing Gradient Descent.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Estimating Training Data Influence by Tracing Gradient Descent

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:18.005919Z

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Observation 412450fc-715a-42b9-ab7e-ca94b188c2d7 · outbound

This paper cites A unified convergence analysis of block successive minimization methods for nonsmooth optimization.SIAM Journal on Optimization, 23(2):1126–1153.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths A unified convergence analysis of block successive minimization methods for nonsmooth optimization.SIAM Journal on Optimization, 23(2):1126–1153

Reference 36

Resolution: verified exact; doi, observed 2026-05-12T02:51:16.881103Z

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Observation cfc41f1b-e8b6-4813-b4a9-cfdaad71fc93 · outbound

This paper cites Cross-validatory choice and assessment of statistical predictions.Journal of the Royal Statistical Society: Series B, 36(2):111–133.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Cross-validatory choice and assessment of statistical predictions.Journal of the Royal Statistical Society: Series B, 36(2):111–133

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.995743Z

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Observation 7ac239b7-7a80-48ce-a147-1664a63d74e3 · outbound

This paper cites Challenging BIG- bench tasks and whether chain-of-thought can solve them.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Challenging BIG- bench tasks and whether chain-of-thought can solve them

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.934482Z

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Observation f7a34262-8719-469a-b5f8-53118da239f6 · outbound

This paper cites Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:51:17.953969Z

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Observation aa237632-0a1f-40c9-888e-477518565ef0 · outbound

This paper cites Pereira, and William Bialek.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Pereira, and William Bialek

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.937784Z

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Observation 98ba71c5-21ef-4514-8d0f-a5a744976fb4 · outbound

This paper cites The information bottleneck method.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths The information bottleneck method

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-12T02:51:17.961792Z

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Observation 2a787acd-ce57-41ff-a65a-2d021b9411b7 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.942694Z

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Observation ef2b1305-bf50-4b7f-a80e-f6648b1d58b9 · outbound

This paper cites Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:53:30.082707Z

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Observation 99f83dce-fa0c-4e6f-b50f-591883c86ea1 · outbound

This paper cites Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.937391Z

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Observation b80e5662-ede8-48f9-b1c2-85b4748776de · outbound

This paper cites NICE data selection for instruction tuning in LLMs with non-differentiable evaluation metric.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths NICE data selection for instruction tuning in LLMs with non-differentiable evaluation metric

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.941250Z

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Observation 93e81444-66fd-497f-9b4b-a6ce3377e04d · outbound

This paper cites Opus: Towards efficient and principled data selection in large language model pre-training in every iteration.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Opus: Towards efficient and principled data selection in large language model pre-training in every iteration

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.922890Z

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Observation 386f51ec-97b6-4204-a38b-d35b8b58c7dd · outbound

This paper cites Opus: Towards efficient and principled data selection in large language model pre-training in every iteration.arXiv preprint arXiv:2602.05400,.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Opus: Towards efficient and principled data selection in large language model pre-training in every iteration.arXiv preprint arXiv:2602.05400,

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.972849Z

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Observation 50061784-a9f4-4f8a-8302-aa1267f7a6a3 · outbound

This paper cites Smith, Iz Beltagy, and Hannaneh Ha- jishirzi.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Smith, Iz Beltagy, and Hannaneh Ha- jishirzi

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.927701Z

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Observation ea83bfc2-937b-4abe-898d-68f7f85fbc0d · outbound

This paper cites How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.945367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6dec4c3-cddf-40d1-b8b5-e67e8a9dd5d9 · outbound

This paper cites Target-Oriented Pretraining Data Selection via Neuron-Activated Graph.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Target-Oriented Pretraining Data Selection via Neuron-Activated Graph

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-12T02:51:17.948436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 534b4af5-bbbc-45c1-8c5a-676039adec16 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-12T02:51:18.008422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50e43d54-8a0d-4742-8df3-5a3e9045f193 · outbound

This paper cites ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.982139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0c16eca-6102-4996-a681-820d1221266b · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.987635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd11cc83-5644-444b-8e23-e9e73db2f544 · outbound

This paper cites GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:19:50.278133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 33b79aed-cb0e-4fa3-b8f7-9abd423bfbb7 · outbound

This paper cites A survey on data selection for llm instruction tuning.Journal of Artificial Intelligence Research, 83, August 2025.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths A survey on data selection for llm instruction tuning.Journal of Artificial Intelligence Research, 83, August 2025

Reference 55

Resolution: verified exact; doi, observed 2026-05-12T02:51:16.883470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce182efa-e63e-4d71-8e0c-fe0328ed797b · outbound

This paper cites The best instruction-tuning data are those that fit.arXiv preprint arXiv:2502.04194.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths The best instruction-tuning data are those that fit.arXiv preprint arXiv:2502.04194

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.979199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 52f451f4-764b-466b-8809-82ec785e932a · outbound

This paper cites Towards understanding valuable preference data for large language model alignment.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Towards understanding valuable preference data for large language model alignment

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:41:53.918387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 710c3456-92e4-4ece-afc8-34f9efceb7b3 · outbound

This paper cites an unresolved cited work.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths Unresolved cited work

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.956490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be7c49e5-d3fe-44a6-adc1-bb2135d9cc02 · outbound

This paper cites LIMA: Less Is More for Alignment.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths LIMA: Less Is More for Alignment

Reference 59

Resolution: malformed identifier; arxiv_id, observed 2026-05-17T11:34:13.088019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers