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Paper Citation Record · LEDGER

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2603.02792.

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

pith.paper-citation-record.v1
2603.02792 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T19:18:49.167143Z

measured 24 of 24 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T18:58:46.374476Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T19:03:08.625761Z

Reference resolution

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be4db682-9cd0-4754-9fc1-bae24fc92dd1 · outbound

This paper cites Focus only on algorithmic changes, not formatting or comments.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Focus only on algorithmic changes, not formatting or comments

Reference 1

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source=pdf_text observed=2026-08-02T19:18:48.950704Z digest=sha256:f897a9192a195ad2109c0980f89ed94e09e1a1d51dc02699cbfbe274365af2a5

Observation 867e9a9e-7e73-4dfc-9b94-6efc9dd0a31a · outbound

This paper cites Approxi- mation algorithms for bin-packing—an updated survey,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Approxi- mation algorithms for bin-packing—an updated survey,

Reference 2

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source=pdf_text observed=2026-08-02T19:18:46.648452Z digest=sha256:68903fef4afd38753e7b5b1c622bcf71b816aa17f3127662a3228adfe9ba70b7

Observation 77878530-7a94-4efc-ac97-549fe4213a47 · outbound

This paper cites AutoPBO: LLM-powered Optimization for Local Search PBO Solvers.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors AutoPBO: LLM-powered Optimization for Local Search PBO Solvers

Reference 5

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source=pdf_text observed=2026-08-02T19:18:47.014206Z digest=sha256:0ae2af86dc82b1343ecc36aec4873ee3023b657565860c57176d4fd3b6a14f0b

Observation d60d45bf-b183-4b77-ab59-7c36a9450f91 · outbound

This paper cites Understanding the importance of evolutionary search in automated heuristic design with large language models,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Understanding the importance of evolutionary search in automated heuristic design with large language models,

Reference 6

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source=pdf_text observed=2026-08-02T19:18:47.135273Z digest=sha256:cf1b9426e8ff953b09dd76bf92a2656b064186eaf1ab74a0fb996ac7b7f3d685

Observation 4da50941-ef9c-43a8-a0b1-eed7e70ddffe · outbound

This paper cites Benchmarking in Optimization: Best Practice and Open Issues.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Benchmarking in Optimization: Best Practice and Open Issues

Reference 7

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source=pdf_text observed=2026-08-02T19:18:47.283722Z digest=sha256:04e961eef64ca8667d41c53d4cc445dc3f3974f42dbbfe7d66ab4c7674dd06c1

Observation 6bf5931e-3dc6-40e1-8c92-e5d7bc1f8c8e · outbound

This paper cites Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT

Reference 9

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source=pdf_text observed=2026-08-02T19:18:47.501408Z digest=sha256:3a3c12f3e2e8e04a3de9fb55e76e5c56d0312a1a1388ec108091f7af3e8ad3ff

Observation 6a9086d0-aa77-4b01-89a4-18966c799cc2 · outbound

This paper cites Compar- ing results of 31 algorithms from the black-box optimization benchmarking bbob-2009,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Compar- ing results of 31 algorithms from the black-box optimization benchmarking bbob-2009,

Reference 14

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source=pdf_text observed=2026-08-02T19:18:48.074339Z digest=sha256:d310f08c4b8a80a47fb346e61882e1b4e64df0ad38b5aa25acb675d703d39c31

Observation a79f4274-e8ed-428d-b5cb-2603218bf760 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Bleu: a method for automatic evaluation of machine translation,

Reference 16

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source=pdf_text observed=2026-08-02T19:18:48.360844Z digest=sha256:e190d3ffe358e2b08ed494554ce205ffc018313199ca089673f49b17a8dee912

Observation e90b7f3e-adb9-4849-b79e-771cb31d4ff3 · outbound

This paper cites The CMA Evolution Strategy: A Tutorial.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors The CMA Evolution Strategy: A Tutorial

Reference 18

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source=pdf_text observed=2026-08-02T19:18:48.594822Z digest=sha256:f670d9de972e320a77196b6a27bd83c28f15efd1fbacd67a8769b9a6afd0e05b

Observation b0c01d90-aec0-44de-a17f-3471f093dead · outbound

This paper cites an unresolved cited work.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-02T19:18:49.097165Z digest=sha256:cfd89fd2802abf940aeb77e6690956a4a0f4521ce9d578d41401a656b4dadadc

Observation a863a777-6fdc-4609-83e5-518d45f18ae6 · outbound

This paper cites Thex-axis represents the cumulative number of algorithms generated by the LLM, and they-axis indicates the best-so-far AUC value.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Thex-axis represents the cumulative number of algorithms generated by the LLM, and they-axis indicates the best-so-far AUC value

Reference 23

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source=pdf_text observed=2026-08-02T19:18:49.167143Z digest=sha256:5f1ffd263686937d9b6784c837b32069e98a9043168a4832dccdf19bcb24d4f7

Observation d3ea9aa8-22d4-4d50-bbf3-f44c08e01e70 · outbound

This paper cites 23 238–23.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors 23 238–23

Reference 235

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source=pdf_text observed=2026-08-02T19:18:47.711321Z digest=sha256:15fe742dc08ecbfb91596edf4dd729cd181eca1dc7df0944b47287b4cc5f036e

Observation 346b3bfc-7f44-4f74-949f-18bd2744c72a · outbound

This paper cites JoPA: Explaining large language model’s generation via joint prompt attribution,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors JoPA: Explaining large language model’s generation via joint prompt attribution,

Reference 255

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source=pdf_text observed=2026-08-02T19:18:47.849885Z digest=sha256:c0600e81b844be049997c488d7505422f7623d3af3cb4a12de7704a8a2b436db

Observation abe9fbf1-c332-471c-9023-726604b9e9c1 · outbound

This paper cites CodeBLEU: a Method for Automatic Evaluation of Code Synthesis.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 1696

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source=pdf_text observed=2026-08-02T19:18:48.198228Z digest=sha256:73bc172a60017a6310356b7289c1e589947d72656f9846fbacf0e59de0335302

Observation 8979149d-0923-48cc-82b3-6351ea98c9c7 · outbound

This paper cites Stochastic local search,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Stochastic local search,

Reference 2002

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source=pdf_text observed=2026-08-02T19:18:48.497281Z digest=sha256:305cff67ac963c92dcdca6d7d5b26176dfc2db331ef2b5380be684a787d2f04e

Observation 797c4b13-4da9-4ec2-ab2b-1c9b415f0a87 · outbound

This paper cites Cumulative step-size adaptation on linear functions,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Cumulative step-size adaptation on linear functions,

Reference 2016

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source=pdf_text observed=2026-08-02T19:18:48.738888Z digest=sha256:61805aebc3933e4e6f314aa01d12befbd80f39be800146476dfd5af33b82d8e9

Observation 8474e880-555c-41ed-9c5c-557cbad12698 · outbound

This paper cites Transformers: State-of-the-art natural language processing,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Transformers: State-of-the-art natural language processing,

Reference 2018

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source=pdf_text observed=2026-08-02T19:18:48.821059Z digest=sha256:fa56d1d9c4c228910fc1fc71ea9e0ad4435392edde097c391b9c4edaeddb7b7b

Observation 3f8e7318-4b5e-4ae2-8198-87eb5e915d41 · outbound

This paper cites Automated design of metaheuristic algorithms,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Automated design of metaheuristic algorithms,

Reference 2019

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source=pdf_text observed=2026-08-02T19:18:46.537203Z digest=sha256:b5eca4948112f5790d5694b713318e3c3504a08a468aacd8a8aa01a1490cd8be

Observation 81b7f4e8-3323-4fea-805c-c0f26054e55f · outbound

This paper cites Sequential Integrated Gradients: a simple but effective method for explaining language models.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Sequential Integrated Gradients: a simple but effective method for explaining language models

Reference 2020

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source=pdf_text observed=2026-08-02T19:18:47.616762Z digest=sha256:24a7aebf840004653a27da1357f071c20396c5395b7e6aa79e3948c10da4cc3b

Observation ff4d9a4a-075c-44a4-a0af-acfa733b20a5 · outbound

This paper cites Autonomous Code Evolution Meets NP-Completeness.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Autonomous Code Evolution Meets NP-Completeness

Reference 2021

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source=pdf_text observed=2026-08-02T19:18:47.408187Z digest=sha256:69d021d251c17ad9c2616155cf85e7925910243d7a284706c517dfb7e115cfd6

Observation 6feb46a3-d20e-476a-943e-6798f027c15a · outbound

This paper cites Pretrained optimization model for zero-shot black box optimization,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Pretrained optimization model for zero-shot black box optimization,

Reference 2022

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source=pdf_text observed=2026-08-02T19:18:47.972121Z digest=sha256:4a0febc717ae9c5b5a6a07cb47d2dec8a8621e9710299989e72e28497b238687

Observation 4f819069-c449-4fa5-b4df-9bfde7732097 · outbound

This paper cites Discovering heuristics in a complex SAT solver with large language models.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Discovering heuristics in a complex SAT solver with large language models

Reference 2024

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source=pdf_text observed=2026-08-02T19:18:46.786771Z digest=sha256:2b06e0f5e8a8dc603bfb7a42c0ef1e5277c45383e469935fded227d72071b21a

Observation aca24648-b1bc-481c-b6c8-a83178b7f96f · outbound

This paper cites Multi- objective evolution of heuristic using large language model,.

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors Multi- objective evolution of heuristic using large language model,

Reference 2025

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source=pdf_text observed=2026-08-02T19:18:46.906643Z digest=sha256:acec1ca5223f93c830175f08e82901100c534a4e2fedc55ca0c415d92541ca6f

Pith citing papers

Observation 41289d95-6691-4004-a2f6-583854e7d5ff · inbound

Breaking Validity-Induced Boundaries to Expand Algorithm Search Space: A Two-Stage AST-Based Operator for LLM-Driven Automated Heuristic Evolution cites this paper.

Breaking Validity-Induced Boundaries to Expand Algorithm Search Space: A Two-Stage AST-Based Operator for LLM-Driven Automated Heuristic Evolution From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors

Reference 12

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arxiv_id, observed 2026-07-21T02:20:35.628763Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T18:58:46.374476Z digest=sha256:25b0016af8834cbb9b85d8c2df582824f4791cd7f5ecfb543c3fc8fd44c50187