Paper Citation Record · LEDGER

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback

As of 17 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2509.18384.

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

pith.paper-citation-record.v1
2509.18384 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:47:15.295884Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

44 of 44 outbound references displayed

verified exact0
verified fuzzy0
unresolved44
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c657706d-fffb-44b6-8f28-1e653e1fa672 · outbound

This paper cites Language Models are Few-Shot Learners.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Language Models are Few-Shot Learners

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.007296Z

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Unavailable: canonical work link unavailable.

Observation 5aff815c-bbca-455a-859e-451e7a47c567 · outbound

This paper cites Llm-planner: Few-shot grounded planning for embodied agents with large language models,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Llm-planner: Few-shot grounded planning for embodied agents with large language models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.097321Z

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Observation 94458386-5595-4b7e-82b7-8d1f45e9ad47 · outbound

This paper cites Llm+p: Empowering large language models with optimal planning proficiency,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Llm+p: Empowering large language models with optimal planning proficiency,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.209977Z

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Observation 4a7921e2-369a-4096-9921-402a985e3f75 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback ReAct: Synergizing Reasoning and Acting in Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.346588Z

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Observation 8e9f6e94-84d9-483b-b0af-004f709eef95 · outbound

This paper cites Fine-tuning language models using formal methods feedback: A use case in autonomous systems,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Fine-tuning language models using formal methods feedback: A use case in autonomous systems,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.442680Z

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Unavailable: canonical work link unavailable.

Observation 4e5813ae-463e-470b-b591-d806a8f6e9cb · outbound

This paper cites Deploying and evaluating llms to program service mobile robots,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Deploying and evaluating llms to program service mobile robots,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.523608Z

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Unavailable: canonical work link unavailable.

Observation 49732ebc-3da0-4f5d-beae-f7cbee698f84 · outbound

This paper cites Progprompt: Generating situated robot task plans using large language models,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Progprompt: Generating situated robot task plans using large language models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.604536Z

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Observation 5f2f83b4-940b-4fb2-ab94-7c0755724208 · outbound

This paper cites Llm- based robot task planning with exceptional handling for general purpose service robots,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Llm- based robot task planning with exceptional handling for general purpose service robots,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.697229Z

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Observation 4d101e68-9333-49b1-861b-72987712602b · outbound

This paper cites On the planning, search, and memorization capabilities of large language models,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback On the planning, search, and memorization capabilities of large language models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.793405Z

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Observation aa0b98f1-1318-4c36-8ed9-70327ea31dc2 · outbound

This paper cites Can We Rely on LLM Agents to Draft Long-Horizon Plans? Let's Take TravelPlanner as an Example.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Can We Rely on LLM Agents to Draft Long-Horizon Plans? Let's Take TravelPlanner as an Example

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.833508Z

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Observation 9e0b50f4-3480-495d-acff-482bc863f075 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Direct preference optimization: Your language model is secretly a reward model,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:10.942130Z

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Observation 666ab3fb-9999-4902-a7e8-f36fe511f41b · outbound

This paper cites GPT-4 Technical Report.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback GPT-4 Technical Report

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.017703Z

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Observation a70a0795-d401-4cac-84cd-30b53a65dbe8 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.094422Z

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Observation 15adc3bc-cc4e-489a-b0be-de7b1c154368 · outbound

This paper cites Joint verification and refinement of language models for safety-constrained planning,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Joint verification and refinement of language models for safety-constrained planning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.203493Z

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Observation 4cebab2b-d222-49a7-b78b-9fa2de4e71aa · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Large Language Models Are Human-Level Prompt Engineers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.288825Z

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Observation 96732b21-5dfb-4393-9543-a731b2d95484 · outbound

This paper cites A Survey on In-context Learning.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback A Survey on In-context Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.361709Z

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Observation dcd669a9-2607-4626-9d57-b02300fd231a · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.468854Z

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Observation 0437ca45-c610-4a12-b48c-1964e84b18b6 · outbound

This paper cites Prompt engineering in large language models,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Prompt engineering in large language models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.593552Z

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Observation dd225e30-75d8-4088-92c5-dfef1d96f6fa · outbound

This paper cites LLM-AutoDiff: Auto-Differentiate Any LLM Workflow.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback LLM-AutoDiff: Auto-Differentiate Any LLM Workflow

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.705992Z

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Observation 54180fd1-7ce2-4701-a52a-5d103d667734 · outbound

This paper cites Learning to summarize from human feedback.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Learning to summarize from human feedback

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.822384Z

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Observation 03b7123a-d432-4a21-a734-a9345e577469 · outbound

This paper cites Training language models to follow instructions with human feedback,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Training language models to follow instructions with human feedback,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:11.945493Z

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Unavailable: canonical work link unavailable.

Observation 0db5017b-cda6-4ef4-9843-6e3d6c74f830 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.035968Z

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Unavailable: canonical work link unavailable.

Observation 98b8a0ec-8a35-4cba-b80c-5750919a7c77 · outbound

This paper cites Multimodal pretrained models for verifiable sequential decision-making: Planning, grounding, and perception,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Multimodal pretrained models for verifiable sequential decision-making: Planning, grounding, and perception,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.138122Z

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Observation 6c59f913-cd2d-4bbc-8ba9-0b1324450c5b · outbound

This paper cites Know where you’re uncertain when planning with multimodal foundation models: A formal framework,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Know where you’re uncertain when planning with multimodal foundation models: A formal framework,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.214594Z

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Observation 2a359f8d-a3b7-4cd9-9d1c-5fbff11c85e2 · outbound

This paper cites Joint prompt optimization of stacked llms using variational inference,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Joint prompt optimization of stacked llms using variational inference,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.286411Z

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Observation 58e06c1f-d01a-4b05-826e-929d29ffb55c · outbound

This paper cites Large language models as optimizers,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Large language models as optimizers,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.406983Z

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Observation 9be6e710-5437-43e3-8e68-4fb3831df677 · outbound

This paper cites TextGrad: Automatic "Differentiation" via Text.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback TextGrad: Automatic "Differentiation" via Text

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.521200Z

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Observation 01352ca2-aa2e-43b7-83f3-07977ef5897b · outbound

This paper cites Dspy: Compiling declarative language model calls into state-of-the- art pipelines,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Dspy: Compiling declarative language model calls into state-of-the- art pipelines,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.614889Z

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Observation a1cea746-d74a-42e2-b94b-2e6fafe7aac0 · outbound

This paper cites Promptagent: Strategic planning with language models enables expert-level prompt optimization,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Promptagent: Strategic planning with language models enables expert-level prompt optimization,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.775303Z

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Observation 2ae57cb2-65ff-4ed8-9203-d33b666837aa · outbound

This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:12.941106Z

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Observation 7b23e664-d998-4f2a-ab22-36dcb94cf60e · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:13.053845Z

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Observation 25a55611-f2b6-4f7a-867b-a7e7f150fb03 · outbound

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LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Unresolved cited work

Reference 32

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Observation e98d7765-ff47-4445-a14e-2a79a4217dad · outbound

This paper cites NuSMV 2: An opensource tool for symbolic model checking,.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback NuSMV 2: An opensource tool for symbolic model checking,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:13.395194Z

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Observation 8f43468d-072c-48d7-a9fb-8a8240cea320 · outbound

This paper cites - If true, Action: Stop.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If true, Action: Stop

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:13.847659Z

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Observation 5f784ab0-c9c9-4309-9344-49fd45689bfa · outbound

This paper cites - If true, proceed to step 3.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If true, proceed to step 3

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.042926Z

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Observation f15d4855-03fb-46bf-a337-16b5b16b2127 · outbound

This paper cites - If any are true, Action: Stop.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If any are true, Action: Stop

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.210407Z

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Observation 53a1457b-defa-4d54-ad51-2186e1299c11 · outbound

This paper cites - If any are true, Action: Stop.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If any are true, Action: Stop

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.379357Z

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Observation 8104613d-5167-45ac-8b65-92cd5fe7b608 · outbound

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LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If true, Action: Stop

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.503561Z

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Observation c0ddacf8-f210-49f8-99c4-1fdffa2c3fdc · outbound

This paper cites go straight five meters and turn left.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback go straight five meters and turn left

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.650378Z

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Observation 4d05ce06-9948-40c7-99e8-70af83b17e67 · outbound

This paper cites - If true, return previous steps.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If true, return previous steps

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.798359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d181148f-b549-46a9-84fc-445caa93a8fe · outbound

This paper cites - If true, Action: Stop.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If true, Action: Stop

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:14.942555Z

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Unavailable: canonical work link unavailable.

Observation ed78cd19-60ae-4fd3-8495-0cdccde5f709 · outbound

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LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback --Generated Plan--

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:15.089458Z

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Observation 97d33f54-ecd8-40e5-aabd-340deb7a0f70 · outbound

This paper cites If there is a pedestrian, then stop.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback If there is a pedestrian, then stop

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:15.163376Z

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Unavailable: canonical work link unavailable.

Observation 709e3f80-aac9-4abf-9f45-d0a72ceedbc4 · outbound

This paper cites - If no pedestrians, then turn left.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback - If no pedestrians, then turn left

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T15:47:15.295884Z

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Pith citing papers

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