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Paper Citation Record · LEDGER

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy

As of 10 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2607.21677.

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

pith.paper-citation-record.v1
2607.21677 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:18:33.455530Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

60 of 60 outbound references displayed

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Outbound references

Observation c7479739-f648-4380-acc0-b26e3df43081 · outbound

This paper cites Cwm: An open-weights llm for research on code generation with world models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Cwm: An open-weights llm for research on code generation with world models,

Reference 1

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source=pdf_text observed=2026-08-01T08:18:26.321470Z digest=sha256:ba272ad6262d331da7502a58822ac5a748a040889553d946489a687f87ae0a93

Observation 2e20222e-9191-48bf-9870-c0b62c0d000f · outbound

This paper cites Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks

Reference 2

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source=pdf_text observed=2026-08-01T08:18:26.387175Z digest=sha256:feb4f3553d8e5311660956b9d0140bc5b2f529cfdea192b01fbe63a729f2427f

Observation ce525bee-d77b-4a8f-9513-c3cf13ee0fdd · outbound

This paper cites Qwen3-Coder-Next Technical Report.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Qwen3-Coder-Next Technical Report

Reference 3

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Observation 55787193-6cbe-4b61-9d49-3fc484fe605d · outbound

This paper cites Mixtral of Experts.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Mixtral of Experts

Reference 4

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source=pdf_text observed=2026-08-01T08:18:26.604127Z digest=sha256:e872c9641baa68097fac0beb06b3659f406738e8c5f8bb9f036b119fc8b99484

Observation cb6a4e8e-579d-4d60-a3e8-49a78dbc040d · outbound

This paper cites How much do language models memorize?.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy How much do language models memorize?

Reference 5

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source=pdf_text observed=2026-08-01T08:18:26.746106Z digest=sha256:3c75653fc33415268202a982da05d407c5dedfebe87a06701ab20867cac3386b

Observation b2e04504-54c9-451d-a8a5-c241aba4437c · outbound

This paper cites Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

Reference 6

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source=pdf_text observed=2026-08-01T08:18:26.898553Z digest=sha256:d945b085fa06bf1483f9b6d8fbe4c4782aa2fc0bfcc69a19ad9c7736145693a8

Observation 4fe25be8-31ac-4e38-ad82-4fd2d5c1b3ae · outbound

This paper cites Scaling laws for fact memorization of large language models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Scaling laws for fact memorization of large language models,

Reference 7

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source=pdf_text observed=2026-08-01T08:18:27.033328Z digest=sha256:e4f6b1455fac7300379b0748c36195770f6dc56fcbf05b30ffe7a2b7ec969ebd

Observation 35705598-4f76-4ce6-ac1d-051775e20669 · outbound

This paper cites Scaling Laws for Neural Language Models.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Scaling Laws for Neural Language Models

Reference 8

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source=pdf_text observed=2026-08-01T08:18:27.143490Z digest=sha256:8650f6379265288e41f8eb879251699d0154a3abc25e1281ecbe05871f20974f

Observation 1a9316e9-6113-4d7c-a747-ab3feb23201d · outbound

This paper cites Towards reasoning ability of small language models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Towards reasoning ability of small language models,

Reference 9

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source=pdf_text observed=2026-08-01T08:18:27.253170Z digest=sha256:ee7223535596c40595aa22c63e6f7c3607ae32259fcd5f02bef3f6f79273fba3

Observation c0257854-b4dc-4c88-8146-db1f251a0f0f · outbound

This paper cites Why Language Models Hallucinate.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Why Language Models Hallucinate

Reference 10

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source=pdf_text observed=2026-08-01T08:18:27.340475Z digest=sha256:517ce6a4a974997b7cefa6dff4c931915484f013cc7aea0e49c95141ec09784e

Observation db84c1f4-36ce-45a6-89a1-01fc69006180 · outbound

This paper cites Human-written vs. ai- generated code: A large-scale study of defects, vulnerabilities, and complexity,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Human-written vs. ai- generated code: A large-scale study of defects, vulnerabilities, and complexity,

Reference 11

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source=pdf_text observed=2026-08-01T08:18:27.463620Z digest=sha256:5d8ae98651d4bea0a6718a8f1aff580381f4110f3d04f4ab8426fd4af8e9d1fb

Observation 18cc4b6a-9007-44bc-987d-c89d14803fb8 · outbound

This paper cites Speed at the cost of quality: How cursor ai increases short-term velocity and long-term complexity in open-source projects,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Speed at the cost of quality: How cursor ai increases short-term velocity and long-term complexity in open-source projects,

Reference 12

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Observation 0914197e-d1f3-4758-9c54-5c67a28cf066 · outbound

This paper cites Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

Reference 13

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source=pdf_text observed=2026-08-01T08:18:27.678402Z digest=sha256:aaaf9d569da0855c1642637c68cb1150d0462ba1e6bfe7e23016bf1e4fa69c46

Observation 5d363549-ae59-4752-9115-d574dc46c6c2 · outbound

This paper cites Supercode: Sustainability per ai-driven co-design,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Supercode: Sustainability per ai-driven co-design,

Reference 14

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source=pdf_text observed=2026-08-01T08:18:27.819186Z digest=sha256:7dc9b26741c7ab07e7d4bec43a0bc7359435cc0ce695108523e38764bd10b909

Observation 89c13c2e-c33c-4303-ac43-6adc5ca194dc · outbound

This paper cites Lofar: The low-frequency array,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Lofar: The low-frequency array,

Reference 15

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source=pdf_text observed=2026-08-01T08:18:27.943501Z digest=sha256:2e8faefd8573d66449621cca10e8800f0e03d0cde382a66ea602a31dafc57cd2

Observation 911db56a-b3ca-4aa6-966a-2780847fcdf5 · outbound

This paper cites Into the depths: Unveiling elais-n1 with LOFAR’s deepest sub-arcsecond wide- field images,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Into the depths: Unveiling elais-n1 with LOFAR’s deepest sub-arcsecond wide- field images,

Reference 16

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Observation 1aea54fc-798b-4012-9997-177fddf6119b · outbound

This paper cites The lofar correlator: Implementation and performance analysis,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy The lofar correlator: Implementation and performance analysis,

Reference 17

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source=pdf_text observed=2026-08-01T08:18:28.208099Z digest=sha256:b4637ae46dbb0734e2c8e3f02d52fb6f4860f38660f5086b7d52bc677131b0df

Observation 3b8d858f-fb15-4a36-9882-df3d8d9d2064 · outbound

This paper cites Commodity compute-and data-transport system design in modern large-scale distributed radio telescopes,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Commodity compute-and data-transport system design in modern large-scale distributed radio telescopes,

Reference 18

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source=pdf_text observed=2026-08-01T08:18:28.315230Z digest=sha256:cedf27fb0f95a9de6e8adcc2f0c3b22f6cf825e488ca28fbd6e9942dcf52a757

Observation b6b61f02-870e-4147-b7f1-a3dcf9609baa · outbound

This paper cites Lofar2.0 documentation.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Lofar2.0 documentation

Reference 19

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source=pdf_text observed=2026-08-01T08:18:28.448737Z digest=sha256:128bc7b6081949b9e52aa8b58f8340417b2c3f2cf66d5f41a7fd3a89193192ac

Observation db47622d-2253-4b34-a1e7-8aaa002e62d8 · outbound

This paper cites Analogue neuromorphic receiver signal processing for radio astronomy,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Analogue neuromorphic receiver signal processing for radio astronomy,

Reference 20

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source=pdf_text observed=2026-08-01T08:18:28.588624Z digest=sha256:ba166e29a00494e9068456298b6e4a1154ed7233ba804032e99faafa193717a2

Observation f44ba6d9-93b4-4b5c-b516-b67cb4079ed7 · outbound

This paper cites How hungry is ai? benchmarking energy, water, and carbon footprint of llm inference,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy How hungry is ai? benchmarking energy, water, and carbon footprint of llm inference,

Reference 21

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source=pdf_text observed=2026-08-01T08:18:28.678991Z digest=sha256:52ccba11a3ef1d56d5bb9027042e44b5025618b7eba1ddfbf2a15374b462f393

Observation 432461a2-6c1f-40c3-834b-1164e38900d3 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Carbon Emissions and Large Neural Network Training

Reference 22

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source=pdf_text observed=2026-08-01T08:18:28.779407Z digest=sha256:acd993713e50d35bfbf54096791d15e3dd2175bbf5ea49997d3f8e45ee4eda06

Observation 845f6e99-5b06-4c41-abf1-a81952eb4ddf · outbound

This paper cites Making ai less’ thirsty’,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Making ai less’ thirsty’,

Reference 23

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source=pdf_text observed=2026-08-01T08:18:28.935627Z digest=sha256:d562919f58d1d7e06f193551ef9cb951fcbc370210b482a115687410daf1390f

Observation 1ec85fbd-16ee-4954-9bea-004941790c13 · outbound

This paper cites Trends in ai inference energy consumption: Beyond the performance-vs-parameter laws of deep learning,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Trends in ai inference energy consumption: Beyond the performance-vs-parameter laws of deep learning,

Reference 24

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Observation 43dd49c6-b9fa-436a-9ddf-12cb9da08ee0 · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Quantifying the Carbon Emissions of Machine Learning

Reference 25

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Observation a79b32bd-f177-4e8a-a9e1-89818ca90b14 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 26

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Observation 2657a868-f393-4517-8149-4dc7b0a1a7d2 · outbound

This paper cites How interpretable are reasoning explanations from prompting large language models?.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy How interpretable are reasoning explanations from prompting large language models?

Reference 27

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source=pdf_text observed=2026-08-01T08:18:29.474679Z digest=sha256:4edcb83c2175e429b2985652aa56259f5ae48603ca2fa91ade641066e84e71e8

Observation 2be047fb-ddca-474b-a5d7-92afae69f94e · outbound

This paper cites Deep sub-arcsecond wide-field imaging of the Lockman Hole field at 144 MHz,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Deep sub-arcsecond wide-field imaging of the Lockman Hole field at 144 MHz,

Reference 28

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Observation f61223d4-63e2-49ec-86d3-75056f7cc3db · outbound

This paper cites Astron receives C4.6 million to widen and sharpen lofar’s cosmic vision.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Astron receives C4.6 million to widen and sharpen lofar’s cosmic vision

Reference 29

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source=pdf_text observed=2026-08-01T08:18:29.680679Z digest=sha256:2a82189d8435776888b406b1fcf53b4f6463f344df5dfd9fc9bd4ead4e1b9b95

Observation c132fb8c-8a28-4654-b162-229aceefa988 · outbound

This paper cites astrocamp: A community benchmark and co-design framework for sustainable ska-scale radio imaging,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy astrocamp: A community benchmark and co-design framework for sustainable ska-scale radio imaging,

Reference 30

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Observation 5828262b-768f-4b7c-acc4-1cd212950c57 · outbound

This paper cites Celloai: Leveraging large language models for hpc software development in high energy physics,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Celloai: Leveraging large language models for hpc software development in high energy physics,

Reference 31

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Observation eeca7fee-466b-4158-af0e-7425945247e6 · outbound

This paper cites The Larger the Better? Improved LLM Code-Generation via Budget Reallocation.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy The Larger the Better? Improved LLM Code-Generation via Budget Reallocation

Reference 32

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source=pdf_text observed=2026-08-01T08:18:29.948950Z digest=sha256:dc77c92f47211c4d791b4a331557c0110b111872e41d1cfd9dd46a69709e9830

Observation 166f0faf-3904-461e-95dc-c79d7c7742cf · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 33

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source=pdf_text observed=2026-08-01T08:18:30.099533Z digest=sha256:af5c3e72a1966584f176e9bb240c0e4209b34203cc441fff948a504e9f75a253

Observation 91954690-8ac9-4f3a-ba7f-a79a461b9b77 · outbound

This paper cites CodeT: Code Generation with Generated Tests.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy CodeT: Code Generation with Generated Tests

Reference 34

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source=pdf_text observed=2026-08-01T08:18:30.230519Z digest=sha256:6a2790f960c1531a40b8f15291cb6f8ac140f5f1bc0630c301e93aa9485fa8d4

Observation 07b8fed7-8396-44b6-8d10-b1f49bde2f6d · outbound

This paper cites LEVER: Learning to Verify Language-to-Code Generation with Execution.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy LEVER: Learning to Verify Language-to-Code Generation with Execution

Reference 35

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source=pdf_text observed=2026-08-01T08:18:30.333650Z digest=sha256:1330e638672a8a1c658b8f4b453c30e9e085f559331f7c4237a44cf3598bd6d8

Observation 1d79c683-2206-4f18-b73c-3e4d8d06e50d · outbound

This paper cites Metis: fast quality-aware rag systems with configuration adaptation,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Metis: fast quality-aware rag systems with configuration adaptation,

Reference 36

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source=pdf_text observed=2026-08-01T08:18:30.479717Z digest=sha256:a5e6642b129fbc92721db44ee36519eec350c8752ece02890f9d206f9f2c1c83

Observation 330c41ff-490d-45a5-93ff-2e6985e59c5b · outbound

This paper cites ContextPilot: Fast Long-Context Inference via Context Reuse.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy ContextPilot: Fast Long-Context Inference via Context Reuse

Reference 37

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source=pdf_text observed=2026-08-01T08:18:30.606475Z digest=sha256:5dd427d5d95c935f6717aba7bba5f472d171727ec1f8dc38506fada75f2b2a09

Observation c49d3982-8b64-497b-bff8-789e0f74b128 · outbound

This paper cites Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches

Reference 38

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source=pdf_text observed=2026-08-01T08:18:30.702152Z digest=sha256:77a9df2919a62334e7281c69c308e953aebaa19b9218cfeaf5cadf677772599c

Observation 4db2055a-313f-4063-a6ad-50062baf2940 · outbound

This paper cites Demystifying chains, trees, and graphs of thoughts,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Demystifying chains, trees, and graphs of thoughts,

Reference 39

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source=pdf_text observed=2026-08-01T08:18:30.852325Z digest=sha256:8377a53c7224bfa0e3f39db6c6f10b6dbd85994e7b01782beb87cd7ae274b8cd

Observation e8ccf86a-3342-4971-9b15-a63f73721ceb · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Chain-of-thought prompting elicits reasoning in large language models,

Reference 40

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T08:18:30.944385Z digest=sha256:34725e8b8471b17b2195fe9efd046a4bfb3ff461865d32b6de8b1ef75ed30554

Observation ba2ec3e6-f3d1-49f2-a324-bea2a8cf95cc · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 41

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no resolver link, observed 2026-08-01T08:18:31.022929Z

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source=pdf_text observed=2026-08-01T08:18:31.022929Z digest=sha256:7ca408a0c39318757f898092457b1488b5b8357dd1f67b4675a9b09d82c83fca

Observation 1694cf9a-7615-4bb6-8a1e-978aae294c34 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Tree of thoughts: Deliberate problem solving with large language models,

Reference 42

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no resolver link, observed 2026-08-01T08:18:31.129281Z

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source=pdf_text observed=2026-08-01T08:18:31.129281Z digest=sha256:a19305d2a11f3fbcb8aba1e2c9d1ede88cd567b1ebf69515a4d0a5962f22aeb8

Observation 664bcbc4-8357-4e78-ba16-75556ad21786 · outbound

This paper cites Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning

Reference 43

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no resolver link, observed 2026-08-01T08:18:31.256413Z

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source=pdf_text observed=2026-08-01T08:18:31.256413Z digest=sha256:be75b705314f8ea6a2fbd17c79868cc7a62f006ca4caf592384373ecc65b94fd

Observation cfb2e5da-9495-4ca1-9c15-7e3e48acfe26 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Graph of thoughts: Solving elaborate problems with large language models,

Reference 44

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no resolver link, observed 2026-08-01T08:18:31.442713Z

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source=pdf_text observed=2026-08-01T08:18:31.442713Z digest=sha256:25155beb3d1248c40416ceed875d6189dba36647ab668078ca61b1312f5303c7

Observation 87caf9c3-d9ba-43ce-9224-b503eb6294e2 · outbound

This paper cites Everything of thoughts: Defying the law of penrose triangle for thought generation,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Everything of thoughts: Defying the law of penrose triangle for thought generation,

Reference 45

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no resolver link, observed 2026-08-01T08:18:31.550711Z

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source=pdf_text observed=2026-08-01T08:18:31.550711Z digest=sha256:3ff26395561c4c1d614f8d6e9451ef5325e49c47616c6569a10ffe6ccdae5d78

Observation bd68f257-c7fc-4c36-8b30-58731acccfed · outbound

This paper cites Buffer of thoughts: Thought-augmented reasoning with large language models,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Buffer of thoughts: Thought-augmented reasoning with large language models,

Reference 46

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no resolver link, observed 2026-08-01T08:18:31.662996Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T08:18:31.662996Z digest=sha256:7a197a8ee01f174b1680afe2c496a9f8a47db3b8f17fc377185440807e0f2dc8

Observation 6923d45f-42e9-411a-afc9-83a38ee1cd94 · outbound

This paper cites LightRAG: Simple and Fast Retrieval-Augmented Generation.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy LightRAG: Simple and Fast Retrieval-Augmented Generation

Reference 47

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no resolver link, observed 2026-08-01T08:18:31.802220Z

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source=pdf_text observed=2026-08-01T08:18:31.802220Z digest=sha256:6cf7a113fdec23d0cc51d04800d60eb029c76275637bb5d8f80b90ec70793a45

Observation 519bb0fe-4c33-46a7-bece-9bb072b55da4 · outbound

This paper cites Reversal of thought: Enhancing large language models with preference-guided reverse rea- soning warm-up,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Reversal of thought: Enhancing large language models with preference-guided reverse rea- soning warm-up,

Reference 48

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no resolver link, observed 2026-08-01T08:18:31.918812Z

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source=pdf_text observed=2026-08-01T08:18:31.918812Z digest=sha256:675f2b2e72f7681f0c4cbbccfd2c7af4533b2153a37a43ebc35f10d82c954e3b

Observation 8c572676-27b6-4429-8d09-0cc59d649cd4 · outbound

This paper cites Combining large language models with static analyzers for code review generation,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Combining large language models with static analyzers for code review generation,

Reference 49

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no resolver link, observed 2026-08-01T08:18:32.080311Z

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source=pdf_text observed=2026-08-01T08:18:32.080311Z digest=sha256:7ac42834687368aa5f1361e626480700dc104b0d57c649c2b57efeae3d6d9c70

Observation 013c11a7-48d4-4508-89fc-bc02909f9d2f · outbound

This paper cites Static analysis as a feedback loop: Enhancing llm-generated code beyond correctness,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Static analysis as a feedback loop: Enhancing llm-generated code beyond correctness,

Reference 50

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no resolver link, observed 2026-08-01T08:18:32.207133Z

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source=pdf_text observed=2026-08-01T08:18:32.207133Z digest=sha256:49a8db7de16eee877c420a22203f448d79cacfda5a990e5d3656e5be41402363

Observation 17a70aec-1cd1-4bd1-bb12-882778a4f502 · outbound

This paper cites Producing wrong data without doing anything obviously wrong!.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Producing wrong data without doing anything obviously wrong!

Reference 51

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no resolver link, observed 2026-08-01T08:18:32.299481Z

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source=pdf_text observed=2026-08-01T08:18:32.299481Z digest=sha256:ea584daa1dbae5286475fc265e1bca1b3e1399b1898a68a014f93d2189bb3bbe

Observation 90c7169c-fedc-47ed-b04d-e2ec2d964bc9 · outbound

This paper cites an unresolved cited work.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Unresolved cited work

Reference 52

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no resolver link, observed 2026-08-01T08:18:32.447425Z

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source=pdf_text observed=2026-08-01T08:18:32.447425Z digest=sha256:df786afe4ecad05437c5e9ae4511ece65c0576f68257e20f6dba75a517145760

Observation 6b74d4de-326e-4fdb-8444-e6625f134e10 · outbound

This paper cites Rapidly selecting good compiler optimizations using per- formance counters,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Rapidly selecting good compiler optimizations using per- formance counters,

Reference 53

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no resolver link, observed 2026-08-01T08:18:32.564003Z

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source=pdf_text observed=2026-08-01T08:18:32.564003Z digest=sha256:e719aa59ee7c762f70721777b6bbaabc35217fa8c11772ad884f8a6e4cac14e2

Observation 2c3b17ee-8f79-47dd-928f-88fcb2d8082c · outbound

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

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion,

Reference 54

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no resolver link, observed 2026-08-01T08:18:32.671402Z

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source=pdf_text observed=2026-08-01T08:18:32.671402Z digest=sha256:873c6e5e8908205ec7531e8a2819b369886471d21b5d9bfecef4407834f5a0d7

Observation 6e1e6c20-e381-4c96-ad04-263782f24ac8 · outbound

This paper cites Langchain community.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Langchain community

Reference 55

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no resolver link, observed 2026-08-01T08:18:32.820630Z

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source=pdf_text observed=2026-08-01T08:18:32.820630Z digest=sha256:2b8cdf7792beddb3e7185ee3666bbd4e795a9428496491c0cedc8e67f7358533

Observation 90745d2b-2720-4804-9301-5ce8ea0ec1da · outbound

This paper cites The Faiss library.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy The Faiss library

Reference 56

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no resolver link, observed 2026-08-01T08:18:32.956691Z

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source=pdf_text observed=2026-08-01T08:18:32.956691Z digest=sha256:7652353920d841ca1b3764b428186840df9a18b6b4e4af18d8e7edc6dbca75cc

Observation 8e6318a1-4d47-4a14-b803-503c4f6d6824 · outbound

This paper cites Qwen2. 5 technical report,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Qwen2. 5 technical report,

Reference 57

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no resolver link, observed 2026-08-01T08:18:33.079901Z

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source=pdf_text observed=2026-08-01T08:18:33.079901Z digest=sha256:e59da4def187c9fe117b2823913bb49f90503e83c95fa7bd1b8a89a4d3e8e852

Observation a47ef160-bc25-4441-9f4e-7735f12aad71 · outbound

This paper cites Coz: Finding code that counts with causal profiling,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Coz: Finding code that counts with causal profiling,

Reference 58

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no resolver link, observed 2026-08-01T08:18:33.219433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T08:18:33.219433Z digest=sha256:f096207d984f0be20b09a5d21992f2167b3b8445774acb828bfaa0efaf3fd3d1

Observation 7db8256f-8501-4e2f-a506-02d8d051f8f4 · outbound

This paper cites Job-level Carbon and Water Footprint Estimation for HPC: Bias Assessment from Runtime to Full Life Cycle.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Job-level Carbon and Water Footprint Estimation for HPC: Bias Assessment from Runtime to Full Life Cycle

Reference 59

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no resolver link, observed 2026-08-01T08:18:33.336229Z

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source=pdf_text observed=2026-08-01T08:18:33.336229Z digest=sha256:014a2b8681590be4631389ff88b5cd09e35ace79b94a84a01f5393b579431174

Observation d2ac86a6-b9a9-4135-b461-2f49aee5ff24 · outbound

This paper cites Evaluating large language models trained on code,.

Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy Evaluating large language models trained on code,

Reference 60

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source=pdf_text observed=2026-08-01T08:18:33.455530Z digest=sha256:dfe56566a707da1a0bccb60884d70793d42c5baa34bfc175205a5c522d5d9c91

Pith citing papers

No inbound Pith citation observations are available.