Paper Citation Record · LEDGER

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2504.15989.

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

pith.paper-citation-record.v1
2504.15989 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:35.402808Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:19:18.967869Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:19:21.080091Z

Reference resolution

36 of 36 outbound references displayed

verified exact4
verified fuzzy7
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27b7ff6d-ec9c-4713-9395-29393b83d3b2 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.239370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4b89ee3-9a8d-4f13-8836-262033abbbbf · outbound

This paper cites A study on prompt design, advantages and limitations of chatgpt for deep learning program repair,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency A study on prompt design, advantages and limitations of chatgpt for deep learning program repair,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.356496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 298e4df5-19b8-447c-96bf-5ce8ac996869 · outbound

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

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency The Code Barrier: What LLMs Actually Understand?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.254425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0f47d91-cb12-42d2-9876-523c7129287c · outbound

This paper cites Large Language Models for Software Engineering: A Systematic Literature Review.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Large Language Models for Software Engineering: A Systematic Literature Review

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.259506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2b8203e-997c-4932-8476-79e491942d65 · outbound

This paper cites Distilling llm agent into small models with retrieval and code tools,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Distilling llm agent into small models with retrieval and code tools,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.264448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 649bacc0-45d2-40de-a7dd-43d7f3f71149 · outbound

This paper cites When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:17:36.121959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a091e787-cc5e-4432-8d57-9ccede89f1b3 · outbound

This paper cites Development in times of hype: How freelancers explore generative ai?.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Development in times of hype: How freelancers explore generative ai?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.274220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57405296-4a11-4f94-924b-8d5c1f5069e0 · outbound

This paper cites Autol2s: Auto long-short reasoning for efficient large language models,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Autol2s: Auto long-short reasoning for efficient large language models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.279296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 558b12a3-d4be-4c2a-a95c-4442cf74b7ff · outbound

This paper cites Optimizing token usage on large language model conversations using the design structure matrix,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Optimizing token usage on large language model conversations using the design structure matrix,

Reference 9

Resolution: verified exact; doi, observed 2026-08-16T11:17:35.438221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 72e02900-5d66-4d73-bccf-2c7fe0aa8acb · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.288849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8332e0b1-9320-465e-891e-385ad1edc15c · outbound

This paper cites Polymetric views - a lightweight visual approach to reverse engineering,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Polymetric views - a lightweight visual approach to reverse engineering,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.341151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 318caf37-c0c9-45d1-8b05-31a5c9f3f45b · outbound

This paper cites USA: Addison- Wesley Longman Publishing Co., Inc., 1999.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency USA: Addison- Wesley Longman Publishing Co., Inc., 1999

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.324968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b605227a-3d34-4273-89c8-a5679f31be5d · outbound

This paper cites Bad smells - humans as code critics,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Bad smells - humans as code critics,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.310959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 89a0ddf0-4db8-4a02-a929-1cd8420dd055 · outbound

This paper cites A comprehensive evaluation of parameter-efficient fine-tuning on method-level code smell detection,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency A comprehensive evaluation of parameter-efficient fine-tuning on method-level code smell detection,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.307028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ddd14cd-5771-4305-8f98-097283e0d6ba · outbound

This paper cites AI in Software Engineering: Perceived Roles and Their Impact on Adoption.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency AI in Software Engineering: Perceived Roles and Their Impact on Adoption

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:17:35.908490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2819293b-24b4-457c-b9c3-609652acd30c · outbound

This paper cites Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.315802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 625a04ea-740a-4c5e-9482-e4a4bf9d877f · outbound

This paper cites A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.320463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9db1e884-7ee5-4f36-9c79-d8a48dfee4e1 · outbound

This paper cites Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:17:35.855358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation da2305ed-404b-44e8-8f40-a77124e40367 · outbound

This paper cites An Empirical Study on the Code Refactoring Capability of Large Language Models.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency An Empirical Study on the Code Refactoring Capability of Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.329537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0222698b-2fe5-410c-9d10-6ea7c0cebbca · outbound

This paper cites Uncertainty-aware molecular dynamics from bayesian active learning for phase transformations and thermal transport in sic,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Uncertainty-aware molecular dynamics from bayesian active learning for phase transformations and thermal transport in sic,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.296937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 13f7a06a-8e88-4ea4-96e6-db94cde462b7 · outbound

This paper cites Open-Source AI-Powered Optimization in Scalene: Advancing Python Performance Profiling with DeepSeek-R1 and LLaMA 3.2.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Open-Source AI-Powered Optimization in Scalene: Advancing Python Performance Profiling with DeepSeek-R1 and LLaMA 3.2

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.342626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e5c9055-17e1-4037-bf0f-8e05c2444b83 · outbound

This paper cites Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.338140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cbbc12d-24b0-41ef-add0-b514eadaa068 · outbound

This paper cites Prompt learning for multi-label code smell detection: A promising approach,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Prompt learning for multi-label code smell detection: A promising approach,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.282170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c44b65c5-3ff9-458e-9c9c-e06c8f99db12 · outbound

This paper cites The Impact of Prompt Programming on Function-Level Code Generation.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency The Impact of Prompt Programming on Function-Level Code Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.346845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6de13f40-477b-473a-a112-7c1e504aa678 · outbound

This paper cites Research on compressed input sequences based on compiler tokenization,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Research on compressed input sequences based on compiler tokenization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:36.267323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 502a3f9e-a66f-40d7-9d9e-30f2c3399d00 · outbound

This paper cites Cothink: Token-efficient reasoning via instruct models guiding reasoning models,.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Cothink: Token-efficient reasoning via instruct models guiding reasoning models,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.370387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6dd3fe7-e70c-4eae-b975-302dff133eaa · outbound

This paper cites Chain-of-Thought Tokens are Computer Program Variables.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Chain-of-Thought Tokens are Computer Program Variables

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.361165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3844bb80-e516-4625-8f39-a1e66652be8a · outbound

This paper cites An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.378807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acc4f8c0-7e86-4c0c-a329-a7f487dc51a7 · outbound

This paper cites Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.383393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e665133-d85c-4869-ae5b-c4ded1d91d73 · outbound

This paper cites How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.374595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a1d484-0972-40ff-a43f-4bcd111b49b1 · outbound

This paper cites Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.393128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1dd11a5-4cd9-4d00-9ee2-b2e5e5d69cbc · outbound

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

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.398042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8097a88-ee63-4d89-8550-bb0f791874c5 · outbound

This paper cites Code Smells for Machine Learning Applications.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Code Smells for Machine Learning Applications

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.387925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6a5ba17-7286-434a-9a7c-51f282c3f255 · outbound

This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.402808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1508db60-e69f-47ea-aec8-987177d2a6d5 · outbound

This paper cites A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.249499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb7e18b3-44b6-493d-ba92-d64f2de6393a · outbound

This paper cites Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.355796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f5e23077-5f98-4968-b45e-50c21baca477 · inbound

CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs cites this paper.

CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:19:21.147626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.