Paper Citation Record · LEDGER

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 7 inbound Pith citation observations for arXiv:2606.19980.

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

pith.paper-citation-record.v1
2606.19980 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T17:25:29.469359Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:45:35.360305Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T19:17:31.658726Z

Reference resolution

56 of 56 outbound references displayed

verified exact18
verified fuzzy0
unresolved35
parse uncertain0
malformed identifier1
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f0bf1501-9ee8-4bb8-945d-8408c90fea19 · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.478832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 135f8582-5b36-4d16-bc3e-e7425b5ec6ce · outbound

This paper cites Autort: Embodied foundation models for large scale orchestration of robotic agents.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Autort: Embodied foundation models for large scale orchestration of robotic agents

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:33.496517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 118a0abe-de38-473a-96c3-ae13e75362b5 · outbound

This paper cites Introducingclaudeopus4.7.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Introducingclaudeopus4.7

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51a24066-c9eb-487f-9a81-48b72a1cef52 · outbound

This paper cites Efficient online reinforcement learning with offline data.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Efficient online reinforcement learning with offline data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation c1f10ed8-e458-40de-8db5-2f68d21727cb · outbound

This paper cites GR00T N1: An Open Foundation Model for Generalist Humanoid Robots.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.492688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7539e6a3-ced2-4031-b568-b78832064361 · outbound

This paper cites Autonomous chemical research with large language models.Nature, 624(7992):570–578, 2023.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Autonomous chemical research with large language models.Nature, 624(7992):570–578, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b623f34-5e31-4e76-ab71-aa044f62e978 · outbound

This paper cites OpenAI Gym.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World OpenAI Gym

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.482142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 59a12b41-9570-49db-9a4b-82a4be75d5f0 · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World RT-1: Robotics Transformer for Real-World Control at Scale

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.489275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2b15489-9ca2-4347-a441-37a4d34a456f · outbound

This paper cites A mobile robotic chemist.Nature, 583(7815): 237–241, 2020.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World A mobile robotic chemist.Nature, 583(7815): 237–241, 2020

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e4d8787-ac42-4cce-921b-83718ed6fd0e · outbound

This paper cites gym-pusht: A gymnasium environment for PushT.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World gym-pusht: A gymnasium environment for PushT

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation d3bce985-e21d-4c21-82c4-37973c40d05c · outbound

This paper cites Mle-bench: Evaluating machine learning agents on machine learning engineering.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Mle-bench: Evaluating machine learning agents on machine learning engineering

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 391d8d58-5f12-41de-a00c-5b6eeab90111 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of Robotics Research, 44(10-11):1684–1704, 2025.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of Robotics Research, 44(10-11):1684–1704, 2025

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation 6ed0b445-0ade-4889-89d7-2921cd523961 · outbound

This paper cites Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation 33eff707-c3b3-4b04-8072-6934b7e40251 · outbound

This paper cites Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395,.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6601c1d4-d335-4d49-a556-dd3f92584ff0 · outbound

This paper cites CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.468572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e240b296-b91f-43aa-92dc-893d412df61a · outbound

This paper cites A multi-agent system for automating scientific discovery.Nature, pages 1–3, 2026.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World A multi-agent system for automating scientific discovery.Nature, pages 1–3, 2026

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation 38565090-19aa-4db2-84a7-40bb743ca813 · outbound

This paper cites Obbtree: A hierarchical structure for rapid interference detection.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Obbtree: A hierarchical structure for rapid interference detection

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c503ae92-2b8d-4891-84ca-2cca180361d9 · outbound

This paper cites Learning to Walk via Deep Reinforcement Learning.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Learning to Walk via Deep Reinforcement Learning

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.475797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ffdcaad1-ab21-4f36-aa9d-3e008b4f0ecf · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World $\pi^{*}_{0.6}$: a VLA That Learns From Experience

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.485616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03bf4238-d8af-40d0-877e-121d14e2d94f · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.446638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0846f177-e42e-471c-a08c-66519ab02f05 · outbound

This paper cites Swe-bench: Canlanguagemodelsresolvereal-worldgithubissues? InInternationalConferenceonLearning Representations, volume 2024, pages 54107–54157, 2024.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Swe-bench: Canlanguagemodelsresolvereal-worldgithubissues? InInternationalConferenceonLearning Representations, volume 2024, pages 54107–54157, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation 11ba1cbf-7e1e-4fad-9a75-25633103dd0d · outbound

This paper cites autoresearch: AI agents running research on single-GPU nanochat training automatically.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World autoresearch: AI agents running research on single-GPU nanochat training automatically

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 77b435e2-cdc7-41bb-a169-7e49b375b0ea · outbound

This paper cites Functional genomic hypothesis generation and experimentation by a robot scientist.Nature, 427(6971):247–252, 2004.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Functional genomic hypothesis generation and experimentation by a robot scientist.Nature, 427(6971):247–252, 2004

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 19cd3272-bd95-49ac-9403-ae0283fb5474 · outbound

This paper cites Rl-100: Performant robotic manipulation with real-world reinforcement learning.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Rl-100: Performant robotic manipulation with real-world reinforcement learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:33.435901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 730ce238-b301-413e-b441-9401862d1bb5 · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Code as policies: Language model programs for embodied control

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation fb37be23-03bd-49e5-ac36-93883de244fc · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.441450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 200dc568-bb96-43b8-9264-c9e15a69b39e · outbound

This paper cites Serl: A software suite for sample-efficient robotic reinforcement learning.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Serl: A software suite for sample-efficient robotic reinforcement learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation a884bd5e-cc91-4973-ac70-3db8d796932d · outbound

This paper cites Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning.Science Robotics, 10(105):eads5033, 2025.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning.Science Robotics, 10(105):eads5033, 2025

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation ad87cbd5-5f62-4847-bfc6-7078a3326de3 · outbound

This paper cites Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

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Observation 99b74566-ea5e-4a0f-bc09-a6e84eddd842 · outbound

This paper cites Dreureka: Language model guided sim-to-real transfer.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Dreureka: Language model guided sim-to-real transfer

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Unavailable: canonical work link unavailable.

Observation d665a83a-3f97-4e3a-bfc3-2c872d30a4f7 · outbound

This paper cites Eureka: Human-level reward design via coding large language models.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Eureka: Human-level reward design via coding large language models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 42f42b78-6c96-449a-9592-43f492159c70 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Self-refine: Iterative refinement with self-feedback

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 28406922-45ae-4eaf-b82d-32b8ff7129a0 · outbound

This paper cites Kimi code.https://www.kimi.com/code/en, 2026.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Kimi code.https://www.kimi.com/code/en, 2026

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 35e04338-710f-4cbf-baeb-2d98cba64637 · outbound

This paper cites Robocasa365: A large-scale simulation framework for training and benchmarking generalist robots.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Robocasa365: A large-scale simulation framework for training and benchmarking generalist robots

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:33.430608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f203aef-d25f-4c9a-bbb7-a889201b6985 · outbound

This paper cites Openai codex.https://developers.openai.com/codex/, 2026.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Openai codex.https://developers.openai.com/codex/, 2026

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation c22bdab9-90bf-4c8a-8343-8f5d8b71b582 · outbound

This paper cites Alvinn: An autonomous land vehicle in a neural network.Advances in neural information processing systems, 1, 1988.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Alvinn: An autonomous land vehicle in a neural network.Advances in neural information processing systems, 1, 1988

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Unavailable: canonical work link unavailable.

Observation 41791279-65d4-40a3-8147-2e3d1dd1017f · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.Advances in neural information processing systems, 36:68539–68551, 2023.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Toolformer: Language models can teach themselves to use tools.Advances in neural information processing systems, 36:68539–68551, 2023

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation ab96f770-db8e-43c8-9c91-50a6c1dadac7 · outbound

This paper cites AgentRxiv: Towards Collaborative Autonomous Research.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World AgentRxiv: Towards Collaborative Autonomous Research

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:33.463797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6648ab18-c1bb-4d46-b764-8b8c903c8abb · outbound

This paper cites Agent laboratory: Using llm agents as research assistants.Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5977–6043, 2025.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Agent laboratory: Using llm agents as research assistants.Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5977–6043, 2025

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 4d16b13f-3b70-49c1-99d5-9ff3728a7f2e · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Unavailable: canonical work link unavailable.

Observation 6a050f12-2fae-4f50-87c2-3ca4de399037 · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.483677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78955043-362d-4831-94de-5a9294293cfe · outbound

This paper cites Vipergpt: Visual inference via python execution for reasoning.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Vipergpt: Visual inference via python execution for reasoning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation efd88d93-cda4-4e08-b089-379366bddde5 · outbound

This paper cites 1038/381520a0.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World 1038/381520a0

Reference 43

Resolution: metadata mismatch; doi, observed 2026-06-26T17:29:37.684582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 646bdc70-1bea-4768-b1a8-d6b2799184b5 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.490907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f983e08-4501-4692-adb6-de7d337a2f6b · outbound

This paper cites Executable code actions elicit better llm agents.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Executable code actions elicit better llm agents

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 52222bea-986e-4cac-921a-80e9ce8a4b30 · outbound

This paper cites RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:33.480323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a0d997ff-998c-4c2a-9680-4dd3d6976464 · outbound

This paper cites Learning beyond gradients.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Learning beyond gradients

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation 897b5f38-4572-4219-8521-5ea86224c643 · outbound

This paper cites arXiv preprint arXiv:2511.00091 , year=.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World arXiv preprint arXiv:2511.00091 , year=

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:59:33.487223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ca83d7e-6bf6-436d-adc3-f8aafbdd8179 · outbound

This paper cites Text2reward: Reward shaping with language models for reinforcement learning.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Text2reward: Reward shaping with language models for reinforcement learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

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Observation ea3333cf-5d30-4ed0-9264-af781835f34d · outbound

This paper cites The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.458710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc7b5f34-6706-498e-aeb1-f9f8304ee6f1 · outbound

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

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World ReAct: Synergizing Reasoning and Acting in Language Models

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.450108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d35a57ae-efdf-4992-b111-9b612c57acd9 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edd7a058-da00-4aae-b0cd-11dd3f2f9eaa · outbound

This paper cites Language to Rewards for Robotic Skill Synthesis.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Language to Rewards for Robotic Skill Synthesis

Reference 53

Resolution: malformed identifier; arxiv_id, observed 2026-07-04T03:59:33.466912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d53bb8c-051e-4b0e-8a39-2fe9303cfbb8 · outbound

This paper cites an unresolved cited work.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4276ed23-c28c-4be9-9e37-d6aac8deedb1 · outbound

This paper cites In this setting, Codex can only analyze text-based information or write code to extract information from images.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World In this setting, Codex can only analyze text-based information or write code to extract information from images

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9818d31-08ea-408f-90fe-6e9a44b521c5 · outbound

This paper cites Figure 18: Qualitative SAM3 mask outputs for 20 RoboCasa counter-to-cabinet target objects at480×640 resolution.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Figure 18: Qualitative SAM3 mask outputs for 20 RoboCasa counter-to-cabinet target objects at480×640 resolution

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:29.469359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 3c7945fc-0816-4c9e-bb0a-a4f14de5f0db · inbound

Sequential Planning via Anchored Robotic Keypoints cites this paper.

Sequential Planning via Anchored Robotic Keypoints ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-06-30T05:04:20.352491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 19d9e74e-8174-4a20-a9ea-2a9d9fe60996 · inbound

Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report cites this paper.

Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T13:06:15.329891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ae27f1f8-3d0e-46c3-bc8f-94b6d501a914 · inbound

Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report cites this paper.

Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T19:17:31.660672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3dd1a66f-d1bb-4bf8-b3c5-da8e749bcd61 · inbound

Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment cites this paper.

Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T02:59:27.462267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88c8f65c-5bb6-4bad-a27d-9229206d5fb7 · inbound

RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning cites this paper.

RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-01T16:19:09.510548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23e5d1c5-f29f-46ff-86c7-9410ac479e08 · inbound

Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations cites this paper.

Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-30T20:27:07.996170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02f63cd2-873b-4cf7-9ed7-e704ddb64efd · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Reference 272

Resolution: unresolved; no resolver link, observed 2026-08-04T19:45:35.360305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.