Paper Citation Record · LEDGER

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2509.05553.

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

pith.paper-citation-record.v1
2509.05553 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:28:34.761483Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy15
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca411944-a9f3-4953-a955-bbec21300999 · outbound

This paper cites Program Synthesis with Large Language Models.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Program Synthesis with Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.250097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff6c5aa-f794-4348-b237-8c407a58a07d · outbound

This paper cites RepairAgent: An Autonomous, LLM-Based Agent for Program Repair.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study RepairAgent: An Autonomous, LLM-Based Agent for Program Repair

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.257352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be3e3c37-962b-4d2d-936a-21983e85877f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Evaluating Large Language Models Trained on Code

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.318559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f8b2369-b112-48f1-88fc-99a1cc34a166 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study A simple framework for contrastive learning of visual representations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.377963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d217938a-6aa7-4ce2-9d84-20e31052e0dd · outbound

This paper cites Addison-Wesley Profes- sional, Boston, MA, 2009.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Addison-Wesley Profes- sional, Boston, MA, 2009

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.688696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be63ed49-6859-4cfe-af3d-6516cfe88230 · outbound

This paper cites Qlora: Ef- ficient finetuning of quantized llms.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Qlora: Ef- ficient finetuning of quantized llms

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.677341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e886ac54-4464-4230-84cd-6dcf0ac7700d · outbound

This paper cites Towards Translating Real-World Code with LLMs: A Study of Translating to Rust.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Towards Translating Real-World Code with LLMs: A Study of Translating to Rust

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.494381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e9aa6a-47a6-4dc3-a644-ffdf1daf0479 · outbound

This paper cites Catastrophic forgetting in connectionist networks.Trends in Cognitive Sciences, 3(4):128–135, 1999.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Catastrophic forgetting in connectionist networks.Trends in Cognitive Sciences, 3(4):128–135, 1999

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.499053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1be9b7d6-3eac-4c42-8939-9e9e6e3ffa60 · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.502363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5abe6c25-8b23-460d-965a-37cd342d0e69 · outbound

This paper cites Dimensionality reduction by learning an invariant mapping.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Dimensionality reduction by learning an invariant mapping

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.506019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec7c2892-198f-451b-887f-7961290d6f5b · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9729– 9738, 2020.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Momentum contrast for unsupervised visual representation learning.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9729– 9738, 2020

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.585302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fb49fe78-8470-4e37-a6ee-d4c6f8e4b772 · outbound

This paper cites Unpaired image-to- image translation using cycle-consistent adversarial networks.Proceedings of the IEEE International Conference on Computer Vision, pages 2223–2232, 2017.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Unpaired image-to- image translation using cycle-consistent adversarial networks.Proceedings of the IEEE International Conference on Computer Vision, pages 2223–2232, 2017

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.469061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 960b4c44-fe35-46db-9a97-464187977bbb · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.516912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cd00f05-fd04-4d58-8354-e7c4c624b0e8 · outbound

This paper cites Husein, Hasan Aburajouh, and Cagatay Catal.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Husein, Hasan Aburajouh, and Cagatay Catal

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.452377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f27f13e-69e5-44b2-a4ff-2d24561c34a6 · outbound

This paper cites Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.441959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1710b351-ff63-450d-9e85-230065e81775 · outbound

This paper cites Condefects: A large-scale dataset of real-world concurrency bugs and fixes.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Condefects: A large-scale dataset of real-world concurrency bugs and fixes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.425698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8115a385-5ef7-4c8e-9630-0eb3f8668a11 · outbound

This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.Psychology of learning and motivation, 24:109–165, 1989.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Catastrophic interference in connectionist networks: The sequential learning problem.Psychology of learning and motivation, 24:109–165, 1989

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.529913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3175d255-1934-4bad-8312-13178dcbdc24 · outbound

This paper cites Codegen: An open large language model for code with multi-turn program synthesis.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Codegen: An open large language model for code with multi-turn program synthesis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.409543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7e13953a-f72c-48e9-9a16-6aca096ff70b · outbound

This paper cites The Code Barrier: What LLMs Actually Understand?.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study The Code Barrier: What LLMs Actually Understand?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.536630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52141707-7c39-4978-b636-1b7e01a66f05 · outbound

This paper cites Wassi, Michele Merler, Boris Sobolev, Ruchir Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Wassi, Michele Merler, Boris Sobolev, Ruchir Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.399374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f0e690e-cca5-4e97-a08b-bf4eae831a26 · outbound

This paper cites CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.631645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aedc1aba-2c8d-4f76-9458-f64e6506bcaf · outbound

This paper cites CodeBLEU: a Method for Automatic Evaluation of Code Synthesis.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.740851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b7064a3-645c-442e-aeff-23cc7c1d3385 · outbound

This paper cites A comprehensive model for code readability.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study A comprehensive model for code readability

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.292946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c19ed19-7a64-455d-952d-c8869af49bf5 · outbound

This paper cites Protecting software through obfuscation: Can it keep pace with progress in code analysis?ACM Computing Surveys, 49(1):1–37, 2016.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Protecting software through obfuscation: Can it keep pace with progress in code analysis?ACM Computing Surveys, 49(1):1–37, 2016

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.136629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 577ed829-f418-4238-bf50-6426e7967f80 · outbound

This paper cites Java obfuscator (gui).

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Java obfuscator (gui)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.125393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79df9942-9c5a-4e74-b645-57501b1123b9 · outbound

This paper cites Generating adversarial examples for holding robustness of source code processing models.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Generating adversarial examples for holding robustness of source code processing models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.115727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 319ed4bb-8d8a-4ebd-b6d1-c5fec2ebb81d · outbound

This paper cites Devign: Effective vulnerability identification by learning comprehensive program seman- tics via graph neural networks.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study Devign: Effective vulnerability identification by learning comprehensive program seman- tics via graph neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:35.016779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 93e57948-e867-4302-9087-fa9e40db1582 · outbound

This paper cites The use of large lan- guage models for program repair.Computer Standards & Interfaces, 93:103951, 2024.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study The use of large lan- guage models for program repair.Computer Standards & Interfaces, 93:103951, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:34.866777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.