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Paper Citation Record · LEDGER

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

As of 17 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 4 inbound Pith citation observations for arXiv:2605.29184.

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

pith.paper-citation-record.v1
2605.29184 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T13:03:41.124128Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:25.118902Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact5
  • verified fuzzy0
  • unresolved23
  • parse uncertain1
  • malformed identifier2
  • metadata mismatch3

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 3620cae9-045a-472f-a9db-da903cc00b45 · outbound

This paper cites Chen and C.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Chen and C

Reference 1

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arxiv_id, observed 2026-06-29T13:13:27.164670Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b17eb640-e097-42b9-a909-89c872307645 · outbound

This paper cites and Deutsch, Stuart Jay , urldate =.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback and Deutsch, Stuart Jay , urldate =

Reference 2

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arxiv_id, observed 2026-06-29T13:13:27.162323Z

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Observation 0b66464c-5546-4645-9ea3-9c27f0b4a746 · outbound

This paper cites URL https://doi.org/10.18653/v1/2023.acl-industry.4.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback URL https://doi.org/10.18653/v1/2023.acl-industry.4

Reference 3

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Observation 0e21622e-3dce-43da-9ca4-d59d4d5b8e81 · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 4

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Observation 58fb2c67-8d7c-49e7-9f6d-6cc5c1626096 · outbound

This paper cites Why should I trust you?.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Why should I trust you?

Reference 5

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arxiv_id, observed 2026-06-29T13:13:27.167081Z

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Observation 5f8ae713-fb0f-4d58-8680-7f33e604900b · outbound

This paper cites Why Should I Trust You?.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Why Should I Trust You?

Reference 6

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arxiv_id, observed 2026-06-29T13:13:27.169685Z

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Observation 82eb8b66-3474-428d-b98c-2112f8e87456 · outbound

This paper cites black boxes.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback black boxes

Reference 7

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doi, observed 2026-06-29T13:13:27.173361Z

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Observation e808a6cf-a6c0-4e7e-a7a0-978addb37ac4 · outbound

This paper cites reasoning.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback reasoning

Reference 8

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Observation 19806eeb-90ca-4163-b18f-261aa93b43c9 · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 9

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Observation ac6cb772-f35b-40ae-8f90-302397e6850f · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 10

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Observation 02125739-522f-4f23-816c-3ebc95813e88 · outbound

This paper cites change in error if term is removed.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback change in error if term is removed

Reference 11

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Observation 3515be8b-dc73-466e-b43c-e37eeb011738 · outbound

This paper cites The top K terms are retained, where K corresponds to the sparsity constraint (the keep n terms hyperparameter).

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback The top K terms are retained, where K corresponds to the sparsity constraint (the keep n terms hyperparameter)

Reference 12

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Observation 0f032382-c086-49ec-83d3-80ae150dfad4 · outbound

This paper cites prefer these kinds of terms because of scientific reason X.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback prefer these kinds of terms because of scientific reason X

Reference 13

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Observation e6ddef04-b581-42c8-bae1-a64c69ce6bfa · outbound

This paper cites Propose” agent) and another for refining the set of terms based on evaluation feedback (the “Prune.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Propose” agent) and another for refining the set of terms based on evaluation feedback (the “Prune

Reference 14

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Observation bf6cca22-7da8-49d5-8bdb-70f7683da8db · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 15

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Observation dce5cd32-3685-4636-9750-dd53d155e284 · outbound

This paper cites Propose” Agent:Below is an example response from the LLM acting as the “Propose.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Propose” Agent:Below is an example response from the LLM acting as the “Propose

Reference 16

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Observation b65b0021-e805-42ba-8827-4f6f2ff083da · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 17

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Observation 2227502d-7c0c-4f62-a517-fb99b0bbac8c · outbound

This paper cites keep" or.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback keep" or

Reference 18

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Observation 8c46f6c4-bb5f-4fe8-88ee-e0872b989685 · outbound

This paper cites DECISION.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback DECISION

Reference 19

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Observation 93d17d8b-4ecc-459f-b5d6-5aac33ef9680 · outbound

This paper cites - ‘cancer_volume * radiotherapy_dosage‘ (delta = 197.209) is highly impactful.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback - ‘cancer_volume * radiotherapy_dosage‘ (delta = 197.209) is highly impactful

Reference 20

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Observation 76456d57-660a-40db-ba86-969c807e0c79 · outbound

This paper cites - ‘chemo_dosage‘, ‘radiotherapy_dosage‘: limited delta values.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback - ‘chemo_dosage‘, ‘radiotherapy_dosage‘: limited delta values

Reference 21

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Observation f064de3c-3175-464e-bc8f-4a18ef43a4a2 · outbound

This paper cites These can be ignored safely.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback These can be ignored safely

Reference 22

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Observation 92626d31-8c39-4aae-8c67-717a2773ca4c · outbound

This paper cites - ‘chemo_dosage‘ (delta = 18.8115): essential for modeling ‘dc_dt‘.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback - ‘chemo_dosage‘ (delta = 18.8115): essential for modeling ‘dc_dt‘

Reference 23

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Observation 6d09523f-7584-44c3-b0cd-c69d7dcc0c35 · outbound

This paper cites - ‘cancer_volume‘: relevant, but has relatively small delta compared to other terms.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback - ‘cancer_volume‘: relevant, but has relatively small delta compared to other terms

Reference 24

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This paper cites keep": [.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback keep": [

Reference 25

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arxiv_id, observed 2026-06-29T13:13:27.619070Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 4881788d-77ce-4cbb-9f8f-70c7ab79e345 · outbound

This paper cites These are combined with surviving terms from the previous iteration.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback These are combined with surviving terms from the previous iteration

Reference 26

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Observation d5de6070-b3a8-4c32-817d-67b6e95414b8 · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 27

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Observation 7434eae7-a873-4fca-8f9f-71bbf1a20d60 · outbound

This paper cites 70 Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback 70 Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Reference 28

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Observation 8cb6349b-f8c0-4676-a302-c0067a9bdfca · outbound

This paper cites The Prune agent receives the terms (with their optimized c(value) markers), their fitted outer weightsw j, and their per-term influence scores∆ j.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback The Prune agent receives the terms (with their optimized c(value) markers), their fitted outer weightsw j, and their per-term influence scores∆ j

Reference 29

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Observation 3c5493ef-f291-4403-8188-5d4acd2dcb20 · outbound

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Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Surviving terms, retaining theirc()markers, are passed to the next iteration

Reference 30

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arxiv_id, observed 2026-06-29T13:13:27.613782Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5720603e-a086-49cf-8b64-abd7fb223b1b · outbound

This paper cites This tests whether a non-linear predictor can extract more value from the discovered symbolic features.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback This tests whether a non-linear predictor can extract more value from the discovered symbolic features

Reference 31

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Observation c0d336ca-08c6-48b9-b4aa-d662db6f82b5 · outbound

This paper cites an unresolved cited work.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback Unresolved cited work

Reference 32

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Observation 785e278a-bd09-4635-bdc6-f2dbe1f71e72 · outbound

This paper cites sweet spot.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback sweet spot

Reference 33

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arxiv_id, observed 2026-06-29T13:13:27.616509Z

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Observation 3db75192-a112-4cdf-890a-d324233aaf2d · outbound

This paper cites •depth limit:10.

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback •depth limit:10

Reference 34

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source=pdf_text observed=2026-06-29T13:03:41.124128Z digest=sha256:9f6e7955cbf10066471fa835c5d0267f24a7709a10e13000307275f0e7a8509f

Pith citing papers

Observation c327c815-33c6-4256-a0dd-e089b8c60bba · inbound

Dictionaries, Not Darwin: Set-Level Selection Beats LLM Evolution in Scientific Equation Discovery cites this paper.

Dictionaries, Not Darwin: Set-Level Selection Beats LLM Evolution in Scientific Equation Discovery Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Reference 9

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source=pdf_text observed=2026-07-11T21:38:37.234781Z digest=sha256:2ab5e15b0c728fadc1e2fef1319be60504014dd17ce83ba3990070df219b1dfe

Observation a6fa53da-ee89-49a8-9b05-a7252756b6ec · inbound

On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement cites this paper.

On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Reference 29

Resolution
unresolved
no resolver link, observed 2026-07-30T13:41:01.948972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T13:41:01.948972Z digest=sha256:ba79e2e47b1495b1738aa4d6e51a57974eb976d209f3012eb9cc4a295a33ec46

Observation c07cfc7e-3a6b-4a22-a6ca-b69cb439f826 · inbound

Library Reachability in LSR-Synth: How Anti-Memorization Design Changes the Measurement of Symbolic Discovery cites this paper.

Library Reachability in LSR-Synth: How Anti-Memorization Design Changes the Measurement of Symbolic Discovery Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Reference 21

Resolution
metadata mismatch
local_arxiv, observed 2026-08-03T00:48:28.462864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-03T00:47:39.500855Z digest=sha256:a7c5e6bbef6e60c267133aff86d3d993c89d42c5e548b5249cd8cafe5fd4ef84

Observation 5d35f2ce-0fa3-41f3-a9b2-387ca04690fa · inbound

DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery cites this paper.

DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T14:39:25.118902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:39:25.118902Z digest=sha256:e1c351a956f78a5b62b5e9e5fedeabbefc694498cebebd496d1572245a2498f7