Paper Citation Record · LEDGER

PerfCoder: Large Language Models for Interpretable Code Performance Optimization

As of 6 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 3 inbound Pith citation observations for arXiv:2512.14018.

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

pith.paper-citation-record.v1
2512.14018 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T22:42:01.520588Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:24:35.751797Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T11:35:43.661336Z

Reference resolution

52 of 52 outbound references displayed

verified exact23
verified fuzzy27
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80315be4-afbe-4e44-afca-6040e3717bb7 · outbound

This paper cites On hardware security bug code fixes by prompting large language models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization On hardware security bug code fixes by prompting large language models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.485059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a5457a3a-8586-4d9a-8758-e21fac71daa6 · outbound

This paper cites DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.203530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a900afca-c724-4542-8520-baa88a0792f6 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Evaluating Large Language Models Trained on Code

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.210692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d0d7710e-9f98-44aa-a7da-f5030d3e8ac6 · outbound

This paper cites Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.207305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 26829e2b-c89b-4f74-a46c-5b60d00e8ad4 · outbound

This paper cites Supersonic: Learning to generate source code optimizations in c/c++.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Supersonic: Learning to generate source code optimizations in c/c++

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.512246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e345d811-cfac-4536-9ab4-a305f8409ce0 · outbound

This paper cites A performance study of llm-generated code on leetcode.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization A performance study of llm-generated code on leetcode

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.482500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1df5c136-500a-4147-90ce-c5e6767efadf · outbound

This paper cites Llm compiler: Foundation language models for compiler optimization.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Llm compiler: Foundation language models for compiler optimization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.489190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ffb331ee-e69f-4ed7-b094-01e3a14a38f6 · outbound

This paper cites Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.473335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bdd7a9c5-a38c-4dd0-b018-01e705628255 · outbound

This paper cites Large language models of code fail at completing code with potential bugs.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Large language models of code fail at completing code with potential bugs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.476182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c6d0bf55-b295-46c0-97c6-dcc321a4be00 · outbound

This paper cites Mercury: A Code Efficiency Benchmark for Code Large Language Models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Mercury: A Code Efficiency Benchmark for Code Large Language Models

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T22:43:38.162964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cdff75fe-7ce9-420a-a122-b341444edc65 · outbound

This paper cites PerfRL: A Small Language Model Framework for Efficient Code Optimization.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization PerfRL: A Small Language Model Framework for Efficient Code Optimization

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.189962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 14a34718-b779-4753-9bda-e884c88f5e07 · outbound

This paper cites Open r1: A fully open reproduction of deepseek-r1, January 2025.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Open r1: A fully open reproduction of deepseek-r1, January 2025

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.479684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6dc54260-4115-4bc7-a30e-bd007c401634 · outbound

This paper cites Search-based llms for code optimization.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Search-based llms for code optimization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.493002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e3e2085e-1231-46b7-b5c4-3accee55deb5 · outbound

This paper cites Effilearner: Enhancing efficiency of generated code via self-optimization.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Effilearner: Enhancing efficiency of generated code via self-optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.509001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7174346a-e795-48d2-b478-bcbbf8f9eb25 · outbound

This paper cites Effibench: Benchmarking the efficiency of automatically generated code.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Effibench: Benchmarking the efficiency of automatically generated code

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.514863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c6ca8d3e-39e6-4991-aabb-5a6eac1293f3 · outbound

This paper cites EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.186848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 77594dfa-7b66-4415-b445-cbb2b1607439 · outbound

This paper cites LangProp: A code optimization framework using Large Language Models applied to driving.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization LangProp: A code optimization framework using Large Language Models applied to driving

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.176406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ce8ea550-7dce-41a1-af10-1ad842db9635 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.183152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1c92ab4d-fc06-4458-9add-d00f3e4c2596 · outbound

This paper cites Inferfix: End-to-end program repair with llms.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Inferfix: End-to-end program repair with llms

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.464998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d0ffcc74-ef87-43fa-b53e-8607ed9dd43d · outbound

This paper cites Evocodebench: An evolving code generation benchmark with domain-specific evaluations.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Evocodebench: An evolving code generation benchmark with domain-specific evaluations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.447057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8fe32ae3-f13e-45f5-9c5d-2b841f7a246a · outbound

This paper cites Fastfixer: An efficient and effective approach for repairing programming assignments.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Fastfixer: An efficient and effective approach for repairing programming assignments

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.449178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cbe6525f-69fe-4905-9fb3-65f89e63b66c · outbound

This paper cites RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.166428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2e090058-3741-43fd-9551-0cdffa975a57 · outbound

This paper cites LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.156717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation efd2cfda-736e-4887-8d9f-3535fef7f597 · outbound

This paper cites Learning Performance-Improving Code Edits.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Learning Performance-Improving Code Edits

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.159680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a4da7e4-4fef-45c4-8fbd-8b174e8e30df · outbound

This paper cites Performance-Aligned LLMs for Generating Fast Code.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Performance-Aligned LLMs for Generating Fast Code

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.170024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2e14e867-d944-435e-bfb9-2293fc6826d2 · outbound

This paper cites On evaluating the efficiency of source code generated by llms.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization On evaluating the efficiency of source code generated by llms

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.453824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 30096836-d975-4c15-ac0e-b6dd02bc71a3 · outbound

This paper cites Gpt-3.5: Openai language model.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Gpt-3.5: Openai language model

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.468991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 38fc74b8-f6ba-4fd0-83f8-586383353517 · outbound

This paper cites GPT-4 Technical Report.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization GPT-4 Technical Report

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.217226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a684a48a-a90c-4e9e-aeb7-02d0ca0d9854 · outbound

This paper cites Examining zero-shot vulnerability repair with large language models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Examining zero-shot vulnerability repair with large language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.518435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0dae03e2-bedf-42f6-afc6-959882a5d766 · outbound

This paper cites HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.220878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e6361dd4-196e-4803-a6eb-e651041f231f · outbound

This paper cites Polybench: The polyhedral benchmark suite.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Polybench: The polyhedral benchmark suite

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.499595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2d1d3670-ea98-4d5b-9875-78a33e66128d · outbound

This paper cites Polybench/c 4.2.1.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Polybench/c 4.2.1

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.460604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a18fbc48-b4b3-4956-a603-9faeb4fcec1d · outbound

This paper cites Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.193503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 556ed149-542d-4ee0-91a4-5765294a7560 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Code Llama: Open Foundation Models for Code

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.152436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation add86271-6418-4c54-9ad6-e8f910d04575 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.196481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 61a3aaf6-363c-4a60-bf35-366f3e3fd997 · outbound

This paper cites Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, and Amir Yazdanbakhsh.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, and Amir Yazdanbakhsh

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.496866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0f01f9b-0499-4eb5-8e81-e0a38c8b58d4 · outbound

This paper cites Llm-vectorizer: Llm-based verified loop vectorizer.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Llm-vectorizer: Llm-based verified loop vectorizer

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.502014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 970a3fed-b2ef-4535-aedf-fbc9843c7835 · outbound

This paper cites Deepseek-r1-distill-qwen-32b.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Deepseek-r1-distill-qwen-32b

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.504433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 90a8f5f2-8b94-4def-858b-d101a815396d · outbound

This paper cites Qwen2.5 Technical Report.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Qwen2.5 Technical Report

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.223481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a3178ce6-544b-432c-883f-fcbd1ffad063 · outbound

This paper cites Qwen2.5-Coder Technical Report.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Qwen2.5-Coder Technical Report

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.229572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5425cad-2918-4442-a1da-b7f3fe46e937 · outbound

This paper cites Llama 3: Open foundation and instruction-tuned language models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Llama 3: Open foundation and instruction-tuned language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.451363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 78a3ffab-2ee3-43bb-b412-7f8b1e6748f8 · outbound

This paper cites ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.179639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3261a210-da4d-4fe4-b1ea-ed5b59c8ebe5 · outbound

This paper cites Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.173346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 399be721-aaa7-41e0-98f2-d92e47d5b545 · outbound

This paper cites How effective are neural networks for fixing security vulnerabilities.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization How effective are neural networks for fixing security vulnerabilities

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.456155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 56474f15-1734-46f4-a12f-561122ae058e · outbound

This paper cites Automated program repair via conversation: Fixing 162 out of 337 bugs for \ 0.42 each using chatgpt.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Automated program repair via conversation: Fixing 162 out of 337 bugs for \ 0.42 each using chatgpt

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.458449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3f3f6199-49d0-423e-9f5a-549a4df398cb · outbound

This paper cites Automated program repair in the era of large pre-trained language models.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization Automated program repair in the era of large pre-trained language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.462812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 559348c9-4263-4b38-8338-597fb79876cc · outbound

This paper cites RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:43:38.199704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 908860a5-df0f-49e8-bce2-2baadf79e4a3 · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:43:38.226480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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This paper cites write newline.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization write newline

Reference 49

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This paper cites @esa (Ref.

PerfCoder: Large Language Models for Interpretable Code Performance Optimization @esa (Ref

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T22:43:38.523625Z

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PerfCoder: Large Language Models for Interpretable Code Performance Optimization Unresolved cited work

Reference 51

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Reference 52

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Pith citing papers

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Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search cites this paper.

Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search PerfCoder: Large Language Models for Interpretable Code Performance Optimization

Reference 46

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JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java cites this paper.

JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java PerfCoder: Large Language Models for Interpretable Code Performance Optimization

Reference 44

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JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java cites this paper.

JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java PerfCoder: Large Language Models for Interpretable Code Performance Optimization

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.