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Paper Citation Record · LEDGER

Framework for On the Fly Input Refinement for Deep Learning Models

As of 11 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.05456.

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

pith.paper-citation-record.v1
2502.05456 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:18:56.089654Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

32 of 32 outbound references displayed

  • verified exact2
  • verified fuzzy24
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b84605a7-8b70-4d86-aeb3-b08e1e1d9cff · outbound

This paper cites On-the-fly Improving Performance of Deep Code Models via Input Denoising.

Framework for On the Fly Input Refinement for Deep Learning Models On-the-fly Improving Performance of Deep Code Models via Input Denoising

Reference 1

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Unavailable: canonical work link unavailable.

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Observation ebb789ae-6f58-4a43-9289-fd128de96487 · outbound

This paper cites Natural attack for pre-trained models of code,.

Framework for On the Fly Input Refinement for Deep Learning Models Natural attack for pre-trained models of code,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation ef0290c7-306d-4b97-8da7-4084c4111e4d · outbound

This paper cites Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,.

Framework for On the Fly Input Refinement for Deep Learning Models Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,

Reference 3

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 002770ff-34a0-4b7a-8466-39592bfc4b07 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Framework for On the Fly Input Refinement for Deep Learning Models CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 4

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no resolver link, observed 2026-08-08T19:18:55.836726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d2a61de5-b302-48d7-80db-fbdc893e65bd · outbound

This paper cites Intellicode compose: Code generation using transformer,.

Framework for On the Fly Input Refinement for Deep Learning Models Intellicode compose: Code generation using transformer,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 43bff370-851a-42c9-b5be-b11549c3b201 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,.

Framework for On the Fly Input Refinement for Deep Learning Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 26a275f9-5606-48ec-a987-2794497748c0 · outbound

This paper cites DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,.

Framework for On the Fly Input Refinement for Deep Learning Models DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 9d90c93f-1d6a-47ee-b863-fee8a91b9928 · outbound

This paper cites Graphcodebert: Pre- training code representations with data flow,.

Framework for On the Fly Input Refinement for Deep Learning Models Graphcodebert: Pre- training code representations with data flow,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 7556e201-7010-42ed-a475-0e94b99452dc · outbound

This paper cites Evaluating Large Language Models Trained on Code,.

Framework for On the Fly Input Refinement for Deep Learning Models Evaluating Large Language Models Trained on Code,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 379f30b2-cdfe-4734-b259-71a94a9c78ee · outbound

This paper cites Repairing failure-inducing inputs with input reflection,.

Framework for On the Fly Input Refinement for Deep Learning Models Repairing failure-inducing inputs with input reflection,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 96c21c04-e3f5-4613-916d-dd95288116ba · outbound

This paper cites Self-checking deep neural networks in deployment,.

Framework for On the Fly Input Refinement for Deep Learning Models Self-checking deep neural networks in deployment,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 20257acf-5981-4121-856d-273857a76eb3 · outbound

This paper cites Natural language processing,.

Framework for On the Fly Input Refinement for Deep Learning Models Natural language processing,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 1bc2542e-b53f-4775-96be-409018be931e · outbound

This paper cites Dissector: Input val- idation for deep learning applications by crossing-layer dissection,.

Framework for On the Fly Input Refinement for Deep Learning Models Dissector: Input val- idation for deep learning applications by crossing-layer dissection,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation be99a6fa-aa0c-4237-91ad-94f870abfc22 · outbound

This paper cites Data Augmentation by Program Trans- formation,.

Framework for On the Fly Input Refinement for Deep Learning Models Data Augmentation by Program Trans- formation,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 87806bc2-770f-434c-8979-ce58e7341d25 · outbound

This paper cites CodeImprove: Program Adaptation for Deep Code Models.

Framework for On the Fly Input Refinement for Deep Learning Models CodeImprove: Program Adaptation for Deep Code Models

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation bceb2d4c-95f1-47ea-9033-646b9a96322f · outbound

This paper cites On calibration of modern neural networks,.

Framework for On the Fly Input Refinement for Deep Learning Models On calibration of modern neural networks,

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 18127be4-dfa4-43ac-9ba0-0658d88a957a · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks,.

Framework for On the Fly Input Refinement for Deep Learning Models A baseline for detecting misclassified and out-of-distribution examples in neural networks,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e6ac2b5c-cf2b-4905-8ecc-8328963f06f8 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

Framework for On the Fly Input Refinement for Deep Learning Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 18

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e76889df-1d33-447e-b78d-4b1b1c1746f2 · outbound

This paper cites code2vec: Learn- ing distributed representations of code,.

Framework for On the Fly Input Refinement for Deep Learning Models code2vec: Learn- ing distributed representations of code,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c40459c7-e1bf-4a30-aefe-8645006a5b84 · outbound

This paper cites Quantifying uncertainties in natural language processing tasks,.

Framework for On the Fly Input Refinement for Deep Learning Models Quantifying uncertainties in natural language processing tasks,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 128c9503-114d-42cd-a48c-2f013e69e0d0 · outbound

This paper cites Towards better confidence estimation for neural models,.

Framework for On the Fly Input Refinement for Deep Learning Models Towards better confidence estimation for neural models,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 6ace0f68-7dfa-4ede-876b-b0b580b28301 · outbound

This paper cites Addressing failure prediction by learning model confidence,.

Framework for On the Fly Input Refinement for Deep Learning Models Addressing failure prediction by learning model confidence,

Reference 22

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Source-reported events for the cited work

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Observation dd30b6bb-19ec-4218-aab5-c0f6a2084b7b · outbound

This paper cites an unresolved cited work.

Framework for On the Fly Input Refinement for Deep Learning Models Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 819f6353-1f64-4bad-b2d4-9702312f2b63 · outbound

This paper cites Unsupervised risk estimation using only conditional independence structure,.

Framework for On the Fly Input Refinement for Deep Learning Models Unsupervised risk estimation using only conditional independence structure,

Reference 24

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation a70ab58f-81bf-41c4-8fb5-a6075fe7935c · outbound

This paper cites A mathematical theory of communication,.

Framework for On the Fly Input Refinement for Deep Learning Models A mathematical theory of communication,

Reference 25

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5d1af431-c953-4263-ac7b-8fe0bd0306a0 · outbound

This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages,.

Framework for On the Fly Input Refinement for Deep Learning Models CodeBERT: A Pre-Trained Model for Programming and Natural Languages,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 86fd46c0-e8ce-4c21-8ebc-fd18c554d30c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach,.

Framework for On the Fly Input Refinement for Deep Learning Models RoBERTa: A Robustly Optimized BERT Pretraining Approach,

Reference 27

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raw_fallback, observed 2026-08-08T19:18:56.355813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation a272ebe1-9477-41aa-a9e7-9d00eb2877f8 · outbound

This paper cites Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,.

Framework for On the Fly Input Refinement for Deep Learning Models Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 78a03ded-3495-4bf3-9c1b-3d1e743db9ef · outbound

This paper cites Convolutional neural networks on assembly code for predicting software defects,.

Framework for On the Fly Input Refinement for Deep Learning Models Convolutional neural networks on assembly code for predicting software defects,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 3fa680db-d78c-4db6-b996-9e79af64dfdd · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Framework for On the Fly Input Refinement for Deep Learning Models Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 30

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no resolver link, observed 2026-08-08T19:18:56.073762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 142d00eb-3acf-4d8f-a2f8-1740ad2bddbd · outbound

This paper cites Random search algorithms,.

Framework for On the Fly Input Refinement for Deep Learning Models Random search algorithms,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 74d5fb0b-cbb0-4c20-92a9-3f00fb914fae · outbound

This paper cites Hill-climbing search,.

Framework for On the Fly Input Refinement for Deep Learning Models Hill-climbing search,

Reference 32

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raw_fallback, observed 2026-08-08T19:18:56.315433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Pith citing papers

No inbound Pith citation observations are available.