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Paper Citation Record · LEDGER

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2603.19464.

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

pith.paper-citation-record.v1
2603.19464 v3

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:51:37.654940Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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Outbound references

Observation f67fd3a7-66b4-4f71-bb18-bf869565994b · outbound

This paper cites Solving the multi-objective path planning problem for mobile robot using an improved nsga-ii algorithm,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Solving the multi-objective path planning problem for mobile robot using an improved nsga-ii algorithm,

Reference 1

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Observation c5101a21-b7e8-47d1-b678-7a049417ccbd · outbound

This paper cites Approximation algorithms for some vehicle routing problems,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Approximation algorithms for some vehicle routing problems,

Reference 2

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source=pdf_text observed=2026-08-02T17:51:37.577664Z digest=sha256:52c7833a872aa6251773e7c7c4c29283eeec23aee1a12604ce8c42b2c1c98e95

Observation 5fa3ee56-9385-4d45-a36b-4826b7559221 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 3

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Observation 6318e9e4-3f54-4b50-8c8c-e77d06e7b0d1 · outbound

This paper cites Omni-MATH: A universal olympiad level mathematic benchmark for large language models,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Omni-MATH: A universal olympiad level mathematic benchmark for large language models,

Reference 4

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Observation 43ae8c0e-4d6c-4689-a3e1-2e221fc79c9c · outbound

This paper cites OlympiadBench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification OlympiadBench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems,

Reference 5

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source=pdf_text observed=2026-08-02T17:51:37.585799Z digest=sha256:6b9dccdce45b82bf0733b0c7e3662286b4e202f1b87221d5484efd041212aa8b

Observation b3c18315-f82a-4cdb-af70-23deb3dcaf28 · outbound

This paper cites Matharena: Evaluating LLMs on uncontaminated math competitions,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Matharena: Evaluating LLMs on uncontaminated math competitions,

Reference 6

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source=pdf_text observed=2026-08-02T17:51:37.588744Z digest=sha256:732993dd9b1526bca83161cfe63bb79b089a48eb8337050aeff2e703f8e3d506

Observation af06cdea-57ce-4376-b8aa-daf3d67ed0ce · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Training Verifiers to Solve Math Word Problems

Reference 7

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Observation 8931be80-85c3-4dd0-b458-2e505eb39421 · outbound

This paper cites Measuring mathematical problem solving with the MATH dataset,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Measuring mathematical problem solving with the MATH dataset,

Reference 8

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Observation 3b935bc3-3ec3-42f8-be9f-45ac6d885760 · outbound

This paper cites A log-approximation for coverage path planning with the energy constraint,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification A log-approximation for coverage path planning with the energy constraint,

Reference 9

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source=pdf_text observed=2026-08-02T17:51:37.596653Z digest=sha256:e078b53663b49dbbc3e01cf9a11b52cba58a529ce6280ba96bbdb561d4530e58

Observation c90ea3b0-e20a-4e80-8d7d-6aa77aab6509 · outbound

This paper cites An approximation algorithm for the pickup and delivery vehicle routing problem on trees,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification An approximation algorithm for the pickup and delivery vehicle routing problem on trees,

Reference 10

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source=pdf_text observed=2026-08-02T17:51:37.599121Z digest=sha256:c2fecb1155b506d289e192d9a7d2a8bdfe0916e6c4c4d4e6ffb0421dcebc3889

Observation cf4e74f3-9486-4579-9712-e92e30f718d2 · outbound

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

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Chain of thought prompting elicits reasoning in large language models,

Reference 11

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source=pdf_text observed=2026-08-02T17:51:37.601463Z digest=sha256:ccc944e73b3ab55b255ae082f24a319ef44e088f00ed5cb2ee2f5f76743814ba

Observation e50b31e7-3353-4dc9-ac36-15e2369d8e03 · outbound

This paper cites Can language models solve graph problems in natural language?.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Can language models solve graph problems in natural language?

Reference 12

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source=pdf_text observed=2026-08-02T17:51:37.603889Z digest=sha256:c2c392a5c418d193cf0e74a5520530f0ff51d2a60a39ed0bf17c3a2845e3117f

Observation 2d147dc5-03cb-48c2-9342-c4b137f1b262 · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 13

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source=pdf_text observed=2026-08-02T17:51:37.606256Z digest=sha256:f46a01216188bf009c48786710a163be33a59c3a70ac6a3bf0b4a2143f7c39dd

Observation 9bf6cb82-163e-48c5-b8ad-6c89f855861a · outbound

This paper cites LLM-a*: Large language model enhanced incremental heuristic search on path planning,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification LLM-a*: Large language model enhanced incremental heuristic search on path planning,

Reference 14

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source=pdf_text observed=2026-08-02T17:51:37.609114Z digest=sha256:8b34b0e3fa9441b1a7a3d5e5b20f69f2a99591df609a2d20c217b607214a5959

Observation d473cbcf-fe15-4b13-a0cf-40cd2e7d6a71 · outbound

This paper cites Grapharena: Evaluating and exploring large language models on graph computation,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Grapharena: Evaluating and exploring large language models on graph computation,

Reference 15

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source=pdf_text observed=2026-08-02T17:51:37.611695Z digest=sha256:f1ed9fe61c886e87e5e216bc0a2160b5a49c785975d2e7e15a9fac4d74315771

Observation c290ed2e-dc36-453e-88ab-cbb9bb202513 · outbound

This paper cites Travelplanner: a benchmark for real-world planning with language agents,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Travelplanner: a benchmark for real-world planning with language agents,

Reference 16

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source=pdf_text observed=2026-08-02T17:51:37.613940Z digest=sha256:47bc2edd0e4ee5ab658521e597295f657b353fedba208c67e9f777d79853d9af

Observation 52ada5c4-73bc-4068-848f-8fb896840466 · outbound

This paper cites Personal travel solver: A preference-driven LLM-solver system for travel planning,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Personal travel solver: A preference-driven LLM-solver system for travel planning,

Reference 17

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source=pdf_text observed=2026-08-02T17:51:37.616253Z digest=sha256:38106b42c839258e920a81e647fcc4d945ba4ae2cc2817dbae0238d38a265134

Observation 107f91f6-927f-4938-9e74-9f19365582f0 · outbound

This paper cites Let’s verify step by step,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Let’s verify step by step,

Reference 18

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source=pdf_text observed=2026-08-02T17:51:37.618563Z digest=sha256:1f2476cd47b234d23aea2cb5f4a86ea91514adcf460059ff316df52e9fba76cf

Observation 0e48372a-16bf-43db-b648-05213c061f2b · outbound

This paper cites ARB: Advanced reasoning benchmark for large language models,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification ARB: Advanced reasoning benchmark for large language models,

Reference 19

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source=pdf_text observed=2026-08-02T17:51:37.620901Z digest=sha256:13e4105ceff22ccfae2b1793f7faa2d71e22a39899a12cb74730b0befc03cbe3

Observation 2922276d-b6af-4b89-bb8e-79ac2994d2b0 · outbound

This paper cites FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI

Reference 20

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source=pdf_text observed=2026-08-02T17:51:37.623160Z digest=sha256:5a4048eda038029825d0454de6a9ac4c0f7fc6448102bb9d32da70ace998d9d7

Observation 06543c3d-d94b-4ccc-8d5b-4130831084b4 · outbound

This paper cites Gold-medalist performance in solving olympiad geometry with alphageometry2,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Gold-medalist performance in solving olympiad geometry with alphageometry2,

Reference 21

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source=pdf_text observed=2026-08-02T17:51:37.625792Z digest=sha256:552dd946b6da5ea6e8472e6ceeaa4faca76acdf95620f74c35eaafa7dacc063b

Observation 7da2dfe2-9f43-410f-bb00-bc7b41f9554e · outbound

This paper cites Solving inequality proofs with large language models,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Solving inequality proofs with large language models,

Reference 22

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Observation 8a8ee4b4-484f-4b9e-8dea-b4e030ad2a54 · outbound

This paper cites The open proof corpus: A large-scale study of LLM-generated mathematical proofs,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification The open proof corpus: A large-scale study of LLM-generated mathematical proofs,

Reference 23

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Observation 0cb8e05a-0623-4b13-b13f-9a3bed864446 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 24

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source=pdf_text observed=2026-08-02T17:51:37.633360Z digest=sha256:e1f75847bd4db76a7a225aa58bea62c4291953ed5c0dc76eed819111037c11a0

Observation 989e30cc-f4a0-429e-ab59-d90ee6c4b1fa · outbound

This paper cites A survey on llm-as-a-judge,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification A survey on llm-as-a-judge,

Reference 25

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source=pdf_text observed=2026-08-02T17:51:37.635691Z digest=sha256:d2c37f6853592743335ed3c4c5e160a7714c735e17076cd603cb7568f9e89d63

Observation 82fc1b1e-6b08-42b5-863c-0c15106c43ce · outbound

This paper cites Language Models as Science Tutors.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Language Models as Science Tutors

Reference 26

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source=pdf_text observed=2026-08-02T17:51:37.638006Z digest=sha256:6d363a367259668b87adab02af249df6c4750480018d6c5236355b31f6b0bce5

Observation c6ecebfa-f01b-40dd-b81d-7ded37d73f80 · outbound

This paper cites Autograding mathematical induc- tion proofs with natural language processing,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Autograding mathematical induc- tion proofs with natural language processing,

Reference 27

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source=pdf_text observed=2026-08-02T17:51:37.640594Z digest=sha256:4a9ec4a2b837bf5c984d348fe4114fd0c04dbd407cdcf7d8902fcec1a1ef0c38

Observation a7f2ef38-618c-4c01-b9ab-608f83feac79 · outbound

This paper cites Language models are few-shot graders,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Language models are few-shot graders,

Reference 28

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source=pdf_text observed=2026-08-02T17:51:37.642971Z digest=sha256:66c622cac858c4b860733b187b49c9ad6f541af67e138915672ce5ceca550b1a

Observation 6cec8363-00fb-4dc8-8549-104a58eff354 · outbound

This paper cites Approximation algorithms for distance constrained vehicle routing problems,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Approximation algorithms for distance constrained vehicle routing problems,

Reference 29

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source=pdf_text observed=2026-08-02T17:51:37.645179Z digest=sha256:6889f84f6f758cd3e1af3e44d0d33390cd8d6d1f9a9027c4adbf32b0817f8855

Observation 26c3aba6-6cc5-4103-ae72-fcb0d02bac3f · outbound

This paper cites A new approximation algorithm for the capacitated vehicle routing problem on a tree,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification A new approximation algorithm for the capacitated vehicle routing problem on a tree,

Reference 30

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Observation 25c04e1b-c571-4ae4-84ed-cfc77c32cfad · outbound

This paper cites Approximation algorithms for some routing problems,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Approximation algorithms for some routing problems,

Reference 31

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source=pdf_text observed=2026-08-02T17:51:37.650280Z digest=sha256:784d4ad32dd1bd622e1bd5751b83759a08622ff0ede485a62622822b759dc17f

Observation c420ef5a-10ab-4f1e-a186-c757e7af10a8 · outbound

This paper cites Approximation algorithms for tours of height-varying view cones,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Approximation algorithms for tours of height-varying view cones,

Reference 32

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source=pdf_text observed=2026-08-02T17:51:37.652617Z digest=sha256:42a5e4fd5f6e23b6af456983683cf0a24afc45ddf38bd52363caa19ac84e5f7b

Observation f1975f7f-39cb-4491-9dcf-53b048f68f94 · outbound

This paper cites Coverage path planning under the energy constraint,.

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification Coverage path planning under the energy constraint,

Reference 33

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