Paper Citation Record · LEDGER

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.19318.

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

pith.paper-citation-record.v1
2501.19318 v4

Coverage vector

measured 42 of 42 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-09T20:41:21.725863Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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Outbound references

Observation c7def98e-625d-47b6-9325-97ab29df2f35 · outbound

This paper cites Grounding llms for robot task planning using closed-loop state feedback, 2024.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Grounding llms for robot task planning using closed-loop state feedback, 2024

Reference 1

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Observation 87077244-902e-4e6e-9bd8-7992a08d85bb · outbound

This paper cites Language Models are Few - Shot Learners.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Language Models are Few - Shot Learners

Reference 2

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Observation 7093ca6a-75bc-4bb5-8c5a-28f2b73f7df7 · outbound

This paper cites Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process

Reference 3

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This paper cites The nature of explanation.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems The nature of explanation

Reference 4

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This paper cites PaLM-E: An Embodied Multimodal Language Model.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems PaLM-E: An Embodied Multimodal Language Model

Reference 5

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Observation a4597e2a-e7f5-4a37-8877-2b4ba3283106 · outbound

This paper cites Dulac-Arnold, N.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Dulac-Arnold, N

Reference 6

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Observation 8a68cb0b-0929-4e69-95bc-c4ce41c09f94 · outbound

This paper cites MineDojo : Building Open - Ended Embodied Agents with Internet - Scale Knowledge.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems MineDojo : Building Open - Ended Embodied Agents with Internet - Scale Knowledge

Reference 7

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Unresolved cited work

Reference 8

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This paper cites PDDLStream : Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems PDDLStream : Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

Reference 9

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Observation 8c6d7a27-72e1-44f9-8a5a-4c2ba66126f0 · outbound

This paper cites World Models.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems World Models

Reference 10

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Observation d4f6e1b9-3e44-4c1f-914c-a6978ed2c412 · outbound

This paper cites Mastering Diverse Domains through World Models.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Mastering Diverse Domains through World Models

Reference 11

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Observation 27c9ed00-76d5-48c7-9f9b-d70961b1208b · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Reasoning with Language Model is Planning with World Model

Reference 12

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Observation 9f29846a-6a65-4256-8f61-17cd82775088 · outbound

This paper cites Towards Reasoning in Large Language Models : A Survey.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Towards Reasoning in Large Language Models : A Survey

Reference 13

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This paper cites Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Reference 14

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Observation 5de0def5-fc8d-4d03-ae3e-da8f409403c6 · outbound

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

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Inner Monologue: Embodied Reasoning through Planning with Language Models

Reference 15

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Observation aa0db18f-df25-4bf8-90ae-99d5a310ef88 · outbound

This paper cites Making Large Language Models Better Planners with Reasoning-Decision Alignment.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Making Large Language Models Better Planners with Reasoning-Decision Alignment

Reference 16

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Observation c5f7a0a9-6982-4bba-a659-d9bf1f1a1796 · outbound

This paper cites Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents

Reference 17

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This paper cites Hierarchical task and motion planning in the now.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Hierarchical task and motion planning in the now

Reference 18

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Rusu, Joel Veness, Marc G

Reference 19

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Reasoning with Large Language Models , a Survey , July 2024

Reference 20

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Unresolved cited work

Reference 21

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Proximal Policy Optimization Algorithms

Reference 22

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems LLMs Can Plan Only If We Tell Them

Reference 23

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This paper cites From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control.

MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control

Reference 24

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Reflexion: language agents with verbal reinforcement learning

Reference 25

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Observation f6857f35-b96e-4106-9d52-9749dca3aa94 · outbound

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Progprompt: Generating situated robot task plans using large language models

Reference 26

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems AdaPlanner : Adaptive Planning from Feedback with Language Models

Reference 27

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Sutton, Doina Precup, and Satinder Singh

Reference 28

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 29

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems E 2CL : Exploration -based Error Correction Learning for Embodied Agents

Reference 30

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Large language models for robotics: Opportunities, challenges, and perspectives

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems ReAct: Synergizing Reasoning and Acting in Language Models

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Robotic Control via Embodied Chain-of-Thought Reasoning

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems PIGLeT : Language Grounding Through Neuro - Symbolic Interaction in a 3D World

Reference 35

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems ExpeL : LLM Agents Are Experiential Learners

Reference 36

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Reference 37

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems write newline

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems @esa (Ref

Reference 40

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MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems s 2vp ^7믿D+ cȊx Αǣ )(L4@ 4 h MMM3 HI_Ÿ &a!1* & > b` z :r&! A Mq1 t: ؊ @@oqK & v Lq `x &a7΂n Sb N-1@ l 6 7 : z \ [ h Jʏ r0ARbc oRub h ϟg yI !Ђ < #VNo WV: N '*ČJJ^ HvRK xM

Reference 42

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Pith citing papers

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