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Paper Citation Record · LEDGER

Demonstration-Free Robotic Control via LLM Agents

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2601.20334.

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

pith.paper-citation-record.v1
2601.20334 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:43:52.887974Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

38 of 38 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 92d26f8b-e033-4208-b5ce-98cd9cb5f84d · outbound

This paper cites VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models.

Demonstration-Free Robotic Control via LLM Agents VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Reference 1

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source=pdf_text observed=2026-08-15T15:43:52.585263Z digest=sha256:d5fd53167bb32807857b8abddd6a1bd8baf6150deba6a8dab4230a23153610c6

Observation 866dd4f1-e3d5-4227-a681-b1b5b245e066 · outbound

This paper cites Code as policies: Language model programs for embodied control,.

Demonstration-Free Robotic Control via LLM Agents Code as policies: Language model programs for embodied control,

Reference 2

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.594237Z digest=sha256:d1ef335497434a06819d975e95f86f2a93f8a7a404d261c03e9d7652b202676a

Observation efc6c90e-8d2b-493b-85f3-68274529abab · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

Demonstration-Free Robotic Control via LLM Agents Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 3

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source=pdf_text observed=2026-08-15T15:43:52.599986Z digest=sha256:31028d3f8946ab3aec7cfcc02500001e38b316a7b482442172a8dc952baaffe8

Observation d2edf0b4-199a-4e6e-af4c-a4df151fcddd · outbound

This paper cites Inner Monologue: Embodied Reasoning through Planning with Language Models.

Demonstration-Free Robotic Control via LLM Agents Inner Monologue: Embodied Reasoning through Planning with Language Models

Reference 4

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source=pdf_text observed=2026-08-15T15:43:52.607892Z digest=sha256:b4236c5f54eaa95b173fe061079fdaa2b44c733a147e9cf21e46964720e514f4

Observation fb60cee1-563f-4d67-b4b9-366664093c82 · outbound

This paper cites ProgPrompt: Generating Situated Robot Task Plans using Large Language Models.

Demonstration-Free Robotic Control via LLM Agents ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Reference 5

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source=pdf_text observed=2026-08-15T15:43:52.618517Z digest=sha256:b646febbcbcd8a0cda76000b66fd22ac20dcb691914a11799f0c93c82160f5ad

Observation ad266f2d-41d8-4849-bf6b-48faac739841 · outbound

This paper cites MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation.

Demonstration-Free Robotic Control via LLM Agents MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation

Reference 6

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source=pdf_text observed=2026-08-15T15:43:52.624772Z digest=sha256:7637971fa510017d66946b2a8e72774d518d77ea3d4fa540a86a56df48a60ae6

Observation 17552f73-2c6a-4d42-8f2b-d2db5a1b5d1e · outbound

This paper cites Gemini robotics: Bringing AI into the physical world,.

Demonstration-Free Robotic Control via LLM Agents Gemini robotics: Bringing AI into the physical world,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.633175Z digest=sha256:1cba70e73591babae1b249aa10f6019dbf2cba61ddff8a96603d06330c8b5e58

Observation 9fa4715d-78c1-4f28-a303-774c598dd4d4 · outbound

This paper cites Qwen3-VL Technical Report.

Demonstration-Free Robotic Control via LLM Agents Qwen3-VL Technical Report

Reference 8

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source=pdf_text observed=2026-08-15T15:43:52.638702Z digest=sha256:b0e5f280566cbfc8205ee91318734f786a8b4d38fe1fb1a820cdce1543917abb

Observation 1ef7942f-540d-4e95-bd23-a9eb47490b82 · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Demonstration-Free Robotic Control via LLM Agents Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 9

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source=pdf_text observed=2026-08-15T15:43:52.645249Z digest=sha256:ea4b179c19215001bb2dc762c4e65f91ccbd8dc3e7f9d9aac39176f6a55c9da9

Observation 83843f76-28ab-4110-9292-6dd6acd53b3a · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

Demonstration-Free Robotic Control via LLM Agents RT-1: Robotics Transformer for Real-World Control at Scale

Reference 10

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source=pdf_text observed=2026-08-15T15:43:52.659532Z digest=sha256:c14a1d81f614772b6101584b3ec4d979b2ad516524ea8b27cb5aeea550178ae9

Observation b7eda1b0-de2c-4acb-84b3-770c92432491 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Demonstration-Free Robotic Control via LLM Agents RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 11

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source=pdf_text observed=2026-08-15T15:43:52.666401Z digest=sha256:7b537d1c236d2894eef368c7bb9240857b365b465394e79b67c2d77a0994d4f1

Observation 5c1262de-5433-4791-b767-107b8084c836 · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Demonstration-Free Robotic Control via LLM Agents $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 12

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source=pdf_text observed=2026-08-15T15:43:52.672536Z digest=sha256:24a64d08a5050e62d062e75dc525eb215430eb119fb4f94036d437df84322f51

Observation e0e70b23-3f56-4291-b96d-28a4e0aa46a9 · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

Demonstration-Free Robotic Control via LLM Agents $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 13

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source=pdf_text observed=2026-08-15T15:43:52.682453Z digest=sha256:6d1fa8bc14329d511cfd1b7840ae7f632c88357a73a319b905a000ef2ed276dc

Observation a3fe5f80-e547-44a5-bc4a-30bdf471146b · outbound

This paper cites $\pi^{*}_{0.6}$: a VLA That Learns From Experience.

Demonstration-Free Robotic Control via LLM Agents $\pi^{*}_{0.6}$: a VLA That Learns From Experience

Reference 14

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source=pdf_text observed=2026-08-15T15:43:52.693204Z digest=sha256:9615d1c4fc03d87435ac2969729c6fe004268b0fe24a00e6953d6af41d8efe37

Observation 14702b19-9596-4af3-a6c6-e966112b609a · outbound

This paper cites GR00T N1: An open foundation model for generalist humanoid robots,.

Demonstration-Free Robotic Control via LLM Agents GR00T N1: An open foundation model for generalist humanoid robots,

Reference 15

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.702667Z digest=sha256:2adf20c9f71c3a5359dd0947caf883d231531672a0857ce35d88d64de11e8846

Observation 36804093-be1d-443e-8553-238096f0ba0a · outbound

This paper cites Openvla: An open-source vision-language-action model,.

Demonstration-Free Robotic Control via LLM Agents Openvla: An open-source vision-language-action model,

Reference 16

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.711020Z digest=sha256:1b4cc3a5fd89503ed4891a85064d663a164cb626dc5fa08387c57d19c50af856

Observation 171ba812-bc0a-448d-a379-11e0cf256684 · outbound

This paper cites SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics.

Demonstration-Free Robotic Control via LLM Agents SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Reference 17

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source=pdf_text observed=2026-08-15T15:43:52.720880Z digest=sha256:bc0fda370739f5a23aa7894cce80faf6974282738b6c72cfc482bc09c61a654f

Observation e000f1ed-6d51-4d0b-b747-b3e40e7a3c89 · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

Demonstration-Free Robotic Control via LLM Agents LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 18

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source=pdf_text observed=2026-08-15T15:43:52.727828Z digest=sha256:9a2d506a6b1c70b9491f6a7a365f8f98e02ae4331b27f17a100ad54ac59dfe07

Observation 38af911f-a994-48f3-93d2-7d8700e234cf · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

Demonstration-Free Robotic Control via LLM Agents Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 19

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source=pdf_text observed=2026-08-15T15:43:52.735890Z digest=sha256:9e1ec40185df521e723aff90d20b787b16cae0134083bf6691e3c3dba2b24312

Observation ab131a4a-e03e-475f-b634-4e38da53dc97 · outbound

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

Demonstration-Free Robotic Control via LLM Agents ReAct: Synergizing Reasoning and Acting in Language Models

Reference 20

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source=pdf_text observed=2026-08-15T15:43:52.750086Z digest=sha256:59195f9d2aa51235b3633acf3cf78266d612e3183641f1342aca78a88ef47951

Observation f7594241-955c-457a-b44c-1c8c3488689b · outbound

This paper cites Claude agent sdk,.

Demonstration-Free Robotic Control via LLM Agents Claude agent sdk,

Reference 21

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.758181Z digest=sha256:c1d32a1eb43da9f18301464bc63e4577e85911a262def9d25a2ac72d97ac6c3e

Observation 27194adc-eb65-45ee-addd-63e6a6b0e694 · outbound

This paper cites Assistants api,.

Demonstration-Free Robotic Control via LLM Agents Assistants api,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.765068Z digest=sha256:3e118d7a8d00c4c59d469aaac7fa6ca0c4ea7da7a0b918497563c2622fccfb52

Observation 9c52face-97e7-4d14-8e95-f67fe3af8b25 · outbound

This paper cites Gemini agent development kit (adk),.

Demonstration-Free Robotic Control via LLM Agents Gemini agent development kit (adk),

Reference 23

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.774760Z digest=sha256:dd73afd12f8159dee0c56152e6d7beb5bf5392d8228a5dd3f317b672d7125259

Observation c485fcf6-761c-4ee6-bd46-3af625a1bbfc · outbound

This paper cites Langchain: Building applications with llms,.

Demonstration-Free Robotic Control via LLM Agents Langchain: Building applications with llms,

Reference 24

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.779922Z digest=sha256:a8170db1627e9e71121f6c65e6f343bed368a734bdadc2075da8b5bd4edd9b17

Observation 3f6885aa-d304-426e-9b2a-228f52788da6 · outbound

This paper cites EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents.

Demonstration-Free Robotic Control via LLM Agents EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

Reference 25

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source=pdf_text observed=2026-08-15T15:43:52.790465Z digest=sha256:2a2c0954d866a8f6775be846414e0b23713b850275fac35d7edfe33f16d8bfd3

Observation 4a3d17ad-22d5-44fd-ad49-a4ecceaf5c05 · outbound

This paper cites Project fetch: Can claude train a robot dog?.

Demonstration-Free Robotic Control via LLM Agents Project fetch: Can claude train a robot dog?

Reference 26

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.800075Z digest=sha256:e504e9c12c4078b160e5080f31d459ce6fb396010a2fec664880411439c65929

Observation 8870b2ba-1ad3-4dd6-93fa-889729a58151 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Demonstration-Free Robotic Control via LLM Agents Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 27

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source=pdf_text observed=2026-08-15T15:43:52.806386Z digest=sha256:535ca23d3056a6bd987a924c81886a3e7cebb410b95944acbc4d985765e8b96b

Observation 8a76b6c4-e012-4b26-be01-11e844756f8c · outbound

This paper cites Best of sim and real: Decoupled visuomotor manipulation via learning control in simulation and perception in real,.

Demonstration-Free Robotic Control via LLM Agents Best of sim and real: Decoupled visuomotor manipulation via learning control in simulation and perception in real,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.815357Z digest=sha256:27982610623363eaa5d0483ebb2f76a9bed4b60db9f48d2f2c3f8d822d342bcb

Observation 96f7fd2c-efe0-431e-bb1b-201916c8b6eb · outbound

This paper cites Nvidia kicks off the next generation of ai with rubin,.

Demonstration-Free Robotic Control via LLM Agents Nvidia kicks off the next generation of ai with rubin,

Reference 29

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raw_fallback, observed 2026-08-15T15:43:53.801433Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.822093Z digest=sha256:3e0d862aed5e735abdcaa025bf237bd9e5ba9d1693400082fb6e24ecd18c9464

Observation e97bf34e-38e9-4297-8795-b0fce6bdbf99 · outbound

This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learning,.

Demonstration-Free Robotic Control via LLM Agents Libero: Benchmarking knowledge transfer for lifelong robot learning,

Reference 30

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raw_fallback, observed 2026-08-15T15:43:53.783366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.828448Z digest=sha256:afe05d87ffa84ed9c872e2dd86649194472e0fe2b4230c86fba94a8bf37946d5

Observation a6bfa5ee-bfc5-44c1-a374-3f37762f4618 · outbound

This paper cites ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI.

Demonstration-Free Robotic Control via LLM Agents ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

Reference 31

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source=pdf_text observed=2026-08-15T15:43:52.835428Z digest=sha256:1b9a024d34c19bde2bec8d5d39ada8aee04fe8507f9771c3a8f6fbd072a8b0a8

Observation 416e7541-3876-425d-b080-74a557a9ada4 · outbound

This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,.

Demonstration-Free Robotic Control via LLM Agents Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,

Reference 32

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raw_fallback, observed 2026-08-15T15:43:53.753812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T15:43:52.841151Z digest=sha256:42311d393daaa240d6b48bf95455d75d4a2aeda17350381c53fbe3dbc6161880

Observation f5821bf1-9d12-4b7a-a558-966270b3e0c6 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Demonstration-Free Robotic Control via LLM Agents Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 33

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source=pdf_text observed=2026-08-15T15:43:52.849008Z digest=sha256:37a9cb5fe861e5be4f41084f227fdab4e5eed3a81426baf8f11768cbc7b10cf4

Observation cc30e65d-a41b-4e45-9e05-ec5e6533cb3e · outbound

This paper cites Efficient training of artificial neural networks for autonomous navigation,.

Demonstration-Free Robotic Control via LLM Agents Efficient training of artificial neural networks for autonomous navigation,

Reference 34

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source=pdf_text observed=2026-08-15T15:43:52.855966Z digest=sha256:be4f5c59b473de08ebf185e5d5731b74ba0fc6bce10eb910590d0e0f1e1d4317

Observation e2c052fb-ceb4-420f-9995-88755c24e188 · outbound

This paper cites Video models are zero-shot learners and reasoners.

Demonstration-Free Robotic Control via LLM Agents Video models are zero-shot learners and reasoners

Reference 35

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source=pdf_text observed=2026-08-15T15:43:52.863595Z digest=sha256:7a18d9be8570455ef917cab56e3114211e4a595a4d26f347895c6d816d033cc6

Observation 2b61c6da-54f0-4dd4-94d1-50b20c11b517 · outbound

This paper cites Evaluating gemini robotics policies in a veo world simulator,.

Demonstration-Free Robotic Control via LLM Agents Evaluating gemini robotics policies in a veo world simulator,

Reference 36

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Unavailable: canonical work link unavailable.

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Observation ba933e7b-153a-4e84-8a9f-c04fcd3081ea · outbound

This paper cites Embodied ai: From llms to world models,.

Demonstration-Free Robotic Control via LLM Agents Embodied ai: From llms to world models,

Reference 37

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Observation 3ddeee84-12a4-4834-a1cd-e3211a733c22 · outbound

This paper cites Autonomous chemical research with large language models,.

Demonstration-Free Robotic Control via LLM Agents Autonomous chemical research with large language models,

Reference 38

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Pith citing papers

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