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Paper Citation Record · LEDGER

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning

As of 19 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2509.10946.

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

pith.paper-citation-record.v1
2509.10946 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:23:22.492228Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

18 of 18 outbound references displayed

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  • unresolved18
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f720c7bb-1771-4d9e-85ee-a0ec2ccad408 · outbound

This paper cites Tinyml: Current progress, research challenges, and future roadmap,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Tinyml: Current progress, research challenges, and future roadmap,

Reference 1

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source=pdf_text observed=2026-08-04T17:23:21.065688Z digest=sha256:078b65515790388767e79bb0ff875f9271e8122d993954474da4ffd67094e385

Observation 2d5aef0b-10f7-4fa8-bcb7-0436e33b41f8 · outbound

This paper cites Edge impulse: An mlops platform for tiny machine learning,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Edge impulse: An mlops platform for tiny machine learning,

Reference 2

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source=pdf_text observed=2026-08-04T17:23:21.128045Z digest=sha256:1bd3e8e7f4531e9349250d5e3aa33d63e1df577a3edd45a1eab35b38df409133

Observation c703c542-cfc1-4894-bfbd-916ccbf4ddec · outbound

This paper cites Llm- based test-driven interactive code generation: User study and empirical evaluation,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Llm- based test-driven interactive code generation: User study and empirical evaluation,

Reference 3

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source=pdf_text observed=2026-08-04T17:23:21.169667Z digest=sha256:b5da18f516cabf12854cd70b334bc58f63debfcf46a9a23d04e5f115d3dba2ad

Observation 4e26c96d-37b8-4296-8587-52c31d49c4c1 · outbound

This paper cites Large language models for software engineering: Sur- vey and open problems,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Large language models for software engineering: Sur- vey and open problems,

Reference 4

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source=pdf_text observed=2026-08-04T17:23:21.257421Z digest=sha256:559080d54b3f4f523c8c9df20197d09b4c3d0a581357a5d5b5c7acfb0ccef645

Observation d79f3737-8a26-4f87-a256-c5735b048f05 · outbound

This paper cites Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:23:21.346463Z digest=sha256:b125aa1002a577af8bb1799c8f0c1fe340a4bb4ad845a02a3aca4806778a45b5

Observation ea42338a-0022-4d9e-aaf1-f8a145e664f0 · outbound

This paper cites Towards risk-aware artificial intelligence and machine learning systems: An overview,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Towards risk-aware artificial intelligence and machine learning systems: An overview,

Reference 6

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source=pdf_text observed=2026-08-04T17:23:21.405766Z digest=sha256:d57503fb1ad4ca892f8e093900d2d9d02e4e9d6f991c95ee736a06a0a9533f2e

Observation 658d5516-63eb-40d5-8f06-36bb65bf298a · outbound

This paper cites What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering

Reference 7

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source=pdf_text observed=2026-08-04T17:23:21.474317Z digest=sha256:3487b7c85e090839a2e52462d5c82bc916f2790743a3f13fd0bb444ed49698b2

Observation 1cd2bc88-95f7-4a24-928c-f98710d6afa7 · outbound

This paper cites Is ChatGPT the Ultimate Programming Assistant -- How far is it?.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Is ChatGPT the Ultimate Programming Assistant -- How far is it?

Reference 8

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source=pdf_text observed=2026-08-04T17:23:21.574210Z digest=sha256:6ef3a54e54aadd22f1e51be5c9facdcef5a405d182612b79a855d77079f64bb7

Observation 4f624208-ae18-4149-938b-5a352f59127b · outbound

This paper cites Prompt engineering in consistency and reliability with the evidence- based guideline for llms,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Prompt engineering in consistency and reliability with the evidence- based guideline for llms,

Reference 9

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source=pdf_text observed=2026-08-04T17:23:21.684036Z digest=sha256:3cfbe56860f8882d95112d55794e3e7d3bba92c529be9fc671b13510db515ce9

Observation d9e77b84-f0fd-49c4-939b-6c18bc8b8ce4 · outbound

This paper cites Mindmap: constructing evidence chains for multi-step reasoning in large language models,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Mindmap: constructing evidence chains for multi-step reasoning in large language models,

Reference 10

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source=pdf_text observed=2026-08-04T17:23:21.793825Z digest=sha256:c4fe444815e66044ec52eb9c476a5c4d0b7458f258918ab2905bbc9bbbf8490a

Observation da704453-7066-4314-b607-7b9b012a48ab · outbound

This paper cites Morepair: Teaching llms to repair code via multi- objective fine-tuning,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Morepair: Teaching llms to repair code via multi- objective fine-tuning,

Reference 11

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source=pdf_text observed=2026-08-04T17:23:21.882528Z digest=sha256:4736324270580eac59f48f82d028e783920ed9f652b04e0fcd8517ddda07380a

Observation 5954333d-adbc-4857-b55e-b18c73e29984 · outbound

This paper cites The patch overfitting problem in automated program repair: Practical magnitude and a baseline for realistic benchmarking,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning The patch overfitting problem in automated program repair: Practical magnitude and a baseline for realistic benchmarking,

Reference 12

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source=pdf_text observed=2026-08-04T17:23:21.959243Z digest=sha256:d071dd9ede7aaa958cb71fba95c9edf906960ca0340ead39912403266f213f33

Observation 0c473680-8a6c-4bd9-9b6b-04dd89679433 · outbound

This paper cites Challenges and opportunities in integrating llms into con- tinuous integration/continuous deployment (ci/cd) pipelines,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Challenges and opportunities in integrating llms into con- tinuous integration/continuous deployment (ci/cd) pipelines,

Reference 13

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source=pdf_text observed=2026-08-04T17:23:22.014815Z digest=sha256:8872fe76393413f39acc60909dd66104b77d0a796c9ce26cb149b3022b694863

Observation a9b16056-dc69-4453-86c7-8c8bde8e7295 · outbound

This paper cites Bugs in large language models generated code: An empirical study,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Bugs in large language models generated code: An empirical study,

Reference 14

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source=pdf_text observed=2026-08-04T17:23:22.079098Z digest=sha256:d53a4d771fca442e2cc37926b9c73dc8c5fbf1bd88d12ce1fe590a823ee49b6c

Observation 9ab97865-288c-4d58-8138-607ace102654 · outbound

This paper cites Vecogen: Automating generation of formally verified c code with large language models,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Vecogen: Automating generation of formally verified c code with large language models,

Reference 15

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source=pdf_text observed=2026-08-04T17:23:22.193205Z digest=sha256:e05d6ea2057056995b259563b1612a995ee8b0de6754d44afda6eda29af27df7

Observation 381c8924-3661-4e80-9830-b5fbfd9fc151 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 16

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source=pdf_text observed=2026-08-04T17:23:22.285628Z digest=sha256:3562bb6e663658b722aacb69115fa822293fe6e43944140712317759cb5052b1

Observation f10feb27-682a-4b6f-88c0-0d0f5dc03bdf · outbound

This paper cites Prompt Orchestration Markup Language.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Prompt Orchestration Markup Language

Reference 17

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source=pdf_text observed=2026-08-04T17:23:22.391430Z digest=sha256:7aca0a2a8c92120ac238fc8ea0b183fabd07de4617d617d37dbb5d366ac7896f

Observation bd0f73e1-31c2-4327-9f3d-12a3423266b5 · outbound

This paper cites Lorp: Llm-based logical reasoning via prolog,.

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning Lorp: Llm-based logical reasoning via prolog,

Reference 18

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source=pdf_text observed=2026-08-04T17:23:22.492228Z digest=sha256:0f070a4252a0773a778a2838ef2b6b368b901e19b81a9f3fe7a73382aca185a6

Pith citing papers

No inbound Pith citation observations are available.