# KORA First Paper Reference Tracker

| ID | Category | Verified citation | Source URL or DOI | Status | Use in paper section | Notes |
|---|---|---|---|---|---|---|
| R01 | LLM serving and inference systems | Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. "Efficient Memory Management for Large Language Model Serving with PagedAttention." SOSP 2023; arXiv:2309.06180. | https://arxiv.org/abs/2309.06180; https://doi.org/10.48550/arXiv.2309.06180 | verified | Related work / motivation | Verified from arXiv title, authors, submission date, DOI, and SOSP 2023 comment. Relevant as LLM serving/inference-systems background. Do not use to claim KORA outperforms vLLM or any serving system. |
| R02 | LLM serving and inference systems | Vijay Janapa Reddi et al. "MLPerf Inference Benchmark." arXiv:1911.02549, 2019; revised 2020. | https://arxiv.org/abs/1911.02549 | verified | Evaluation methodology / benchmarking background | Verified from arXiv title, author list, and submission/revision dates. Useful for benchmark-methodology context and comparability discipline, not as KORA evidence. |
| R03 | LLM serving and inference systems | ONNX Runtime project. "ONNX Runtime" official documentation. Source owner: ONNX Runtime project / Microsoft. Date unavailable; accessed 2026-05-11. | https://onnxruntime.ai/docs/ | verified | Related work / future runtime context | Verified from official docs. Useful as inference-runtime background only. Do not imply KORA currently integrates ONNX Runtime. |
| R04 | Agent/tool routing and orchestration | LangChain. "LangGraph Graph API overview." Official LangGraph documentation. Date unavailable; accessed 2026-05-11. | https://docs.langchain.com/oss/python/langgraph/graph-api | verified | Related work / system positioning | Verified from official LangChain docs. Useful for graph/state-based agent orchestration context. Position KORA as deterministic-first execution control, not as a replacement for LangGraph. |
| R05 | Deterministic/programmatic AI workflows | Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts. "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines." arXiv:2310.03714, 2023. | https://arxiv.org/abs/2310.03714 | verified | Related work / programmatic LM workflows | Verified from arXiv title, authors, and 2023 submission month. Useful for programmatic LM pipeline context. Avoid presenting DSPy as deterministic-first model-call avoidance. |
| R06 | Caching and retrieval-augmented generation | Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-tau Yih, Tim Rocktaschel, Sebastian Riedel, and Douwe Kiela. "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS 2020; arXiv:2005.11401. | https://arxiv.org/abs/2005.11401; https://doi.org/10.48550/arXiv.2005.11401 | verified | Related work / RAG contrast | Verified from arXiv title, authors, NeurIPS 2020 acceptance note, and DOI. Useful to contrast retrieval-grounded generation with KORA's pre-inference execution control. |
| R07 | Workflow orchestration / DAG systems | Apache Airflow project. "Dags." Apache Airflow 3.2.1 documentation. Source owner: Apache Software Foundation. Date unavailable; accessed 2026-05-11. | https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html | verified | Related work / DAG execution background | Verified from official Apache Airflow documentation. Useful for DAG/workflow vocabulary. Do not claim KORA is a general-purpose workflow scheduler. |
| R08 | Workflow orchestration / DAG systems | Johannes Koster and Sven Rahmann. "Snakemake-a scalable bioinformatics workflow engine." Bioinformatics 28(19), 2520-2522, 2012. | https://doi.org/10.1093/bioinformatics/bts480 | verified | Related work / reproducible workflow background | Verified from DOI-backed repository metadata. Useful for workflow and reproducible pipeline context, not as direct AI execution-control evidence. |
| R09 | Local model runtime systems | ggml-org. "llama.cpp: LLM inference in C/C++." Official GitHub repository. Date unavailable; accessed 2026-05-11. | https://github.com/ggml-org/llama.cpp | verified | Future work / local runtime context | Verified from official repository title and README. Useful as local runtime context only. Do not imply current KORA paper evidence includes llama.cpp integration. |
| R10 | Systems papers with artifact evaluation practices | Association for Computing Machinery. "Artifact Review and Badging - Current." Version 1.1, 2020. | https://www.acm.org/publications/policies/artifact-review-and-badging-current | verified | Evaluation methodology / artifact plan | Verified from ACM policy page. Useful for artifact/reproducibility discipline and claim-boundary language, not as validation of KORA results. |
| R11 | Agent/tool routing and orchestration | Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. "ReAct: Synergizing Reasoning and Acting in Language Models." ICLR 2023; arXiv:2210.03629. | https://arxiv.org/abs/2210.03629; https://doi.org/10.48550/arXiv.2210.03629 | verified | Related work / agent and tool-use context | Verified from arXiv title, authors, submission/revision dates, DOI, and ICLR camera-ready comment. Useful for reasoning/action tool-use context. Do not use to claim KORA outperforms ReAct or agent baselines. |
| R12 | Agent/tool routing and orchestration | Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. "Toolformer: Language Models Can Teach Themselves to Use Tools." arXiv:2302.04761, 2023. | https://arxiv.org/abs/2302.04761; https://doi.org/10.48550/arXiv.2302.04761 | verified | Related work / tool-use context | Verified from arXiv title, authors, submission date, and DOI. Useful for model-centered API/tool-use context. Position KORA as deterministic-first execution control, not as self-supervised tool-use training. |
| R13 | Deterministic/programmatic AI workflows | Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. "PAL: Program-aided Language Models." arXiv:2211.10435, 2022; revised 2023. | https://arxiv.org/abs/2211.10435; https://doi.org/10.48550/arXiv.2211.10435 | verified | Related work / programmatic AI workflows | Verified from arXiv title, authors, submission/revision dates, and DOI. Useful for program-aided reasoning context. Do not present PAL as model-call avoidance evidence for KORA. |
| R14 | Caching and retrieval-augmented generation | Fu Bang. "GPTCache: An Open-Source Semantic Cache for LLM Applications Enabling Faster Answers and Cost Savings." Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 212-218, Singapore. Association for Computational Linguistics, 2023. | https://aclanthology.org/2023.nlposs-1.24/; https://doi.org/10.18653/v1/2023.nlposs-1.24 | verified | Related work / semantic caching contrast | Verified from ACL Anthology title, author, venue, pages, year, URL, and DOI. Useful to distinguish semantic cache reuse from KORA execution-control routing. Do not use to support KORA production cost or API-cost claims. |
| R15 | Local model runtime systems | Ollama. "Ollama's documentation." Official documentation. Source owner: Ollama. Date unavailable; accessed 2026-05-11. | https://docs.ollama.com/; https://github.com/ollama/ollama | verified | Future work / local runtime context | Verified from official docs and official GitHub repository. Useful as future local-runtime context only. Do not imply current KORA paper evidence includes Ollama integration. |
| R16 | Benchmarking and reproducibility | Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alche-Buc, Emily Fox, and Hugo Larochelle. "Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)." arXiv:2003.12206, 2020. | https://arxiv.org/abs/2003.12206; https://doi.org/10.48550/arXiv.2003.12206 | verified | Evaluation methodology / reproducibility context | Verified from arXiv title, authors, submission/revision dates, DOI, and JMLR comment. Useful for reproducibility/checklist context, not as KORA evidence. |
| R17 | Systems papers with artifact evaluation practices | USENIX Association. "NSDI '26 Call for Artifacts." Official USENIX guidance, 2025-2026. | https://www.usenix.org/conference/nsdi26/call-for-artifacts | verified | Submission readiness / artifact plan | Verified from official USENIX page. Useful for venue-specific artifact-evaluation expectations. Does not imply KORA has passed artifact evaluation. |
| R18 | Systems papers with artifact evaluation practices | MLSys. "Call for Artifact Evaluations." Official MLSys 2023 guidance. | https://mlsys.org/Conferences/2023/CallForAE | verified | Submission readiness / artifact plan | Verified from official MLSys page. Useful for ML/systems artifact appendix and reproducibility expectations. Does not imply KORA has artifact approval or validation. |
| R19 | Benchmarking and reproducibility | Aarohi Srivastava et al. "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models." Transactions on Machine Learning Research, 2022; arXiv:2206.04615. | https://arxiv.org/abs/2206.04615; https://doi.org/10.48550/arXiv.2206.04615 | verified | Future evaluation methodology / benchmark-construction context | Verified from arXiv title, author lead, submission/revision dates, TMLR journal reference, DOI, and BIG-bench description. Useful as broad benchmark-construction context. Do not use as evidence for KORA production benchmark performance. |
| R20 | Benchmarking and reproducibility | Percy Liang et al. "Holistic Evaluation of Language Models." Transactions on Machine Learning Research, 2023; arXiv:2211.09110. | https://arxiv.org/abs/2211.09110; https://doi.org/10.48550/arXiv.2211.09110 | verified | Future evaluation methodology / multi-metric evaluation context | Verified from arXiv title, author lead, submission/revision dates, TMLR journal reference, DOI, and HELM description. Useful for holistic evaluation framing and metric coverage. Do not use to imply KORA has broad benchmark coverage. |
| R21 | Benchmarking and reproducibility | Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" ICLR 2024; arXiv:2310.06770. | https://arxiv.org/abs/2310.06770; https://doi.org/10.48550/arXiv.2310.06770 | verified | Future evaluation methodology / task benchmark construction context | Verified from arXiv title, authors, submission/revision dates, ICLR 2024 comment, and DOI. Useful as a benchmark-construction example for software-engineering tasks. Do not compare KORA results against SWE-bench. |
| R22 | Benchmarking and reproducibility | Xiao Liu et al. "AgentBench: Evaluating LLMs as Agents." ICLR 2024; arXiv:2308.03688. | https://arxiv.org/abs/2308.03688; https://doi.org/10.48550/arXiv.2308.03688 | verified | Future evaluation methodology / agent benchmark context | Verified from arXiv title, author lead, submission/revision dates, ICLR 2024 publication note, and DOI. Useful as agent-benchmark background only. Do not use to claim KORA outperforms agent systems. |
| R23 | Benchmarking and reproducibility | Douwe Kiela et al. "Dynabench: Rethinking Benchmarking in NLP." NAACL 2021; arXiv:2104.14337. | https://arxiv.org/abs/2104.14337; https://doi.org/10.48550/arXiv.2104.14337 | verified | Future evaluation methodology / dynamic benchmark context | Verified from arXiv title, author lead, submission date, NAACL 2021 comment, and DOI. Useful for benchmark design and dynamic evaluation context. Do not use as KORA production evidence. |
| R24 | Benchmarking and reproducibility | Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. "Beyond Accuracy: Behavioral Testing of NLP Models with CheckList." Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4902-4912, 2020. | https://aclanthology.org/2020.acl-main.442/; https://doi.org/10.18653/v1/2020.acl-main.442 | verified | Future evaluation methodology / behavioral test design context | Verified from ACL Anthology title, authors, venue, pages, year, URL, and DOI. Useful for behavior-oriented evaluation design. Do not use to imply KORA has completed broad behavioral testing. |

## Metadata Audit Status

[KORA First Paper Reference Metadata Audit v0.1](kora-first-paper-reference-metadata-audit-v0-1.md) audits `[R01]` through `[R24]` for metadata completeness and source traceability before normalized bibliography work.

Status:

- Stable `[Rxx]` labels remain unchanged.
- No new references were added in the metadata audit.
- No BibTeX was generated.
- Next step: create a normalized bibliography table for `[R01]` through `[R24]` from verified tracker and audit fields.
- BibTeX remains pending until normalized fields are verified.

## Normalized Bibliography Status

[KORA First Paper Normalized Bibliography v0.1](kora-first-paper-normalized-bibliography-v0-1.md) represents `[R01]` through `[R24]` in a structured bibliography table.

Status:

- Stable `[Rxx]` labels remain unchanged.
- No new references were added.
- No BibTeX was generated.
- High-risk metadata gap resolution remains pending.
- BibTeX remains pending until normalized fields and high-risk gaps are resolved.

## Metadata Gap Resolution Status

[KORA First Paper Metadata Gap Resolution v0.1](kora-first-paper-metadata-gap-resolution-v0-1.md) classifies high-risk entries before BibTeX preparation.

Status:

- High-risk entries are classified by remaining blocker: author-list normalization, venue/status normalization, docs/repo style, artifact guidance style, or target-venue deferral.
- No new references were added.
- No BibTeX was generated.
- BibTeX remains pending until ready records are selected from verified normalized fields.

## Final Bibliography and Claim Audit Status

[KORA First Paper Final Bibliography and Claim Audit v0.1](kora-first-paper-final-bibliography-claim-audit-v0-1.md) records bibliography readiness and claim-safety status after the expanded preliminary BibTeX candidate subset.

Status:

- Preliminary BibTeX includes `[R01]`, `[R03]`, `[R04]`, `[R05]`, `[R06]`, `[R07]`, `[R08]`, `[R09]`, `[R10]`, `[R11]`, `[R12]`, `[R13]`, `[R14]`, `[R16]`, `[R21]`, and `[R24]`.
- Deferred records `[R17]` and `[R18]` remain excluded.
- Still-not-eligible records `[R02]`, `[R15]`, `[R19]`, `[R20]`, `[R22]`, and `[R23]` remain excluded.
- Claim support remains limited to documented benchmark and local/no-network validation evidence.
- Full BibTeX remains incomplete, and the paper remains not submission-ready.
