Paper Citation Record · LEDGER

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks

As of 19 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2509.14380.

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

pith.paper-citation-record.v1
2509.14380 v3

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:54:25.684713Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T21:55:17.414682Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

verified exact3
verified fuzzy36
unresolved14
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfd348d9-9681-42b9-b4f9-0ea1c7772c1c · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Grandmaster level in starcraft ii using multi-agent reinforcement learning,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.471129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d2de1c7-af5c-48fa-a2cb-eb56b88909f7 · outbound

This paper cites Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.451735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c984be24-13f0-41d6-b48e-e82efe451f73 · outbound

This paper cites Google research football: A novel reinforcement learning environ- ment,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Google research football: A novel reinforcement learning environ- ment,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.438678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca312782-fff8-43b2-903c-a1bca889987c · outbound

This paper cites Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.484412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5410024d-e0b3-4c6f-a16e-afeb15b95fd5 · outbound

This paper cites Marladona-towards cooperative team play using multi-agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Marladona-towards cooperative team play using multi-agent reinforcement learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.424331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04f420dd-d8b7-479f-89c6-215b3b86a852 · outbound

This paper cites Leveraging large language models for effective and explainable multi-agent credit assignment,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Leveraging large language models for effective and explainable multi-agent credit assignment,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.408781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 80e48075-02d2-459d-920d-fe6e694fbc23 · outbound

This paper cites Variational automatic curriculum learning for sparse-reward cooperative multi-agent problems,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Variational automatic curriculum learning for sparse-reward cooperative multi-agent problems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.395734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 41dd963c-17e7-487e-b7af-4edb3bcfad14 · outbound

This paper cites Au- tomatic curriculum learning for deep rl: a short survey,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Au- tomatic curriculum learning for deep rl: a short survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.382827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bec0d51-1699-44b7-a108-34f3e6946c81 · outbound

This paper cites Cooperative multi- agent control using deep reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Cooperative multi- agent control using deep reinforcement learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.369660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82e1ea03-1f27-46d0-9d72-56eb9603a1b4 · outbound

This paper cites A survey on curriculum learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks A survey on curriculum learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.357203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 53f08a60-243d-4b3d-861c-aa2ff4efb29f · outbound

This paper cites V oyager: An open-ended embodied agent with large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks V oyager: An open-ended embodied agent with large language models,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.512447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 467670c5-1e43-4355-bec0-aa6c91964437 · outbound

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

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Progprompt: Generating situated robot task plans using large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.335775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c7198cff-ef98-401e-b71d-5c8e6dedca4b · outbound

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

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Eureka: Human-level reward design via coding large language models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.323348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5999ba2b-cb4e-4228-9f0c-153f1dc8e89c · outbound

This paper cites Vision-language models are zero-shot reward models for reinforce- ment learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Vision-language models are zero-shot reward models for reinforce- ment learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.310891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3f031d1d-daf4-4e6e-82b3-412779c604f1 · outbound

This paper cites AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.527995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8930a0ac-e4dd-4b45-97a6-cc10215b69b4 · outbound

This paper cites AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.532091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43e83483-c8bc-4b55-b801-2f22a2eb5a5f · outbound

This paper cites Reverse forward curriculum learning for extreme sample and demo efficiency,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Reverse forward curriculum learning for extreme sample and demo efficiency,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.299005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eb2eed43-a4d1-4eb2-ac41-7f3bd4b63424 · outbound

This paper cites Unsupervised curricula for visual meta-reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Unsupervised curricula for visual meta-reinforcement learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.287048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bf021fa-cc3f-44cd-9588-adcb8646f87b · outbound

This paper cites Tizero: Mastering multi-agent football with curriculum learning and self-play,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Tizero: Mastering multi-agent football with curriculum learning and self-play,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.274314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f33599f5-fe9e-4b02-8a5d-e40a578b5e86 · outbound

This paper cites Curricullm: Automatic task curricula design for learning complex robot skills using large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Curricullm: Automatic task curricula design for learning complex robot skills using large language models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.260883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c41e92b-a8a1-4d06-9a96-b49deb53824d · outbound

This paper cites Environment curriculum generation via large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Environment curriculum generation via large language models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.248410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf87b1ef-6d6b-4f13-a139-328a55e62497 · outbound

This paper cites Aura: Agentic upskilling via reinforced abstractions,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Aura: Agentic upskilling via reinforced abstractions,

Reference 22

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:54:25.868764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d781468-3ebb-469d-a818-3f770a8467dd · outbound

This paper cites Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.559019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8135466-0772-4fd3-a28b-dc8030fee3fa · outbound

This paper cites Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.563421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3df44eb6-1900-476c-a3fb-9db72555d036 · outbound

This paper cites Learning multi-agent loco-manipulation for long-horizon quadrupedal pushing,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Learning multi-agent loco-manipulation for long-horizon quadrupedal pushing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.236119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47e29ed9-b3e5-461e-93e1-47661611efa2 · outbound

This paper cites Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:54:25.769117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 09df2b0d-4bc1-4c4b-8f73-53c8e2a8cc6d · outbound

This paper cites Resolving conflicting constraints in multi-agent rein- forcement learning with layered safety,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Resolving conflicting constraints in multi-agent rein- forcement learning with layered safety,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.223514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b8a5644-0d74-4b43-90d2-e2156ecce97e · outbound

This paper cites Learning differentiable and safe multi-robot control for generalization to novel environments using control barrier functions,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Learning differentiable and safe multi-robot control for generalization to novel environments using control barrier functions,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.210537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c514f9f-1992-4b6f-8bd3-baa8a1f17b11 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.583370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8db63cab-e014-44c2-8443-0c13689dc937 · outbound

This paper cites Monotonic value function factorisation for deep multi- agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Monotonic value function factorisation for deep multi- agent reinforcement learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.587142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 019d8fa8-9d48-4e34-bc1a-e8bce33e1a33 · outbound

This paper cites Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.181631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5068b772-177c-4a64-a7ec-04bf3561d021 · outbound

This paper cites Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.168023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d084231-cbd8-4df1-bb3a-8e5b54bdfa92 · outbound

This paper cites 3d-vla: A 3d vision-language-action generative world model,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks 3d-vla: A 3d vision-language-action generative world model,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.155306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 99229b5a-4310-498b-9ab3-92911e9b221d · outbound

This paper cites Synthesizing interpretable control poli- cies through large language model guided search,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Synthesizing interpretable control poli- cies through large language model guided search,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.141819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ccee268-ff84-41cd-bb3e-076658b02488 · outbound

This paper cites Large language model based multi-agents: a survey of progress and challenges,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Large language model based multi-agents: a survey of progress and challenges,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.129500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05d1178c-76e3-4b09-b877-93bf046888ee · outbound

This paper cites Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.610221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd43f7cc-ca22-4f14-857f-caf601c5f708 · outbound

This paper cites Loss of plasticity in continual deep reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Loss of plasticity in continual deep reinforcement learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.116258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57b26db8-69d4-4b03-99f3-d0cab00a88bc · outbound

This paper cites Loss of plasticity in deep continual learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Loss of plasticity in deep continual learning,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.617971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e21494a3-4e0f-41b5-b917-fa0cb5f1d680 · outbound

This paper cites Mqe: Unleashing the power of interaction with multi-agent quadruped envi- ronment,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Mqe: Unleashing the power of interaction with multi-agent quadruped envi- ronment,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.095292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 48403b71-ffb5-4bb2-bed3-115817d620c1 · outbound

This paper cites robosuite: A Modular Simulation Framework and Benchmark for Robot Learning.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:25.625446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85e141b3-116a-4bc3-bcab-54e252768353 · outbound

This paper cites OpenRL: A Unified Reinforcement Learning Framework.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks OpenRL: A Unified Reinforcement Learning Framework

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:54:25.725192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63c55479-751d-4232-beda-e983aa341a1d · outbound

This paper cites skrl: Modular and flexible library for reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks skrl: Modular and flexible library for reinforcement learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.082715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 412a2675-b44c-49d7-abe5-718e7e9af546 · outbound

This paper cites One for generating candidate curricula, and another for refining the final curriculum from the candidates.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks One for generating candidate curricula, and another for refining the final curriculum from the candidates

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.070532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b12f8a9-2420-4080-bf92-41fbe8babed6 · outbound

This paper cites Prompt 3: LLM prompt for generating base reward function.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Prompt 3: LLM prompt for generating base reward function

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.058085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de92ccf1-7880-4fab-b003-8e2ee1cccf2f · outbound

This paper cites Prompt 4: VLM prompt for policy evaluation.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Prompt 4: VLM prompt for policy evaluation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.045528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 17994f35-dc9a-43c3-92d5-3ceaef5f8513 · outbound

This paper cites an unresolved cited work.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:54:26.033122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39872ea0-03f2-44a9-95f7-dab277970512 · outbound

This paper cites very close.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks very close

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.021042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7cb458cd-b881-466d-b5f9-04b9130e812f · outbound

This paper cites One for generating advice on how to refine the reward (VLM), and one for refining the reward function given the advice (LLM).

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks One for generating advice on how to refine the reward (VLM), and one for refining the reward function given the advice (LLM)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.009147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27888c29-f4c7-44de-ad69-98b7fa1811cc · outbound

This paper cites reach_reward.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks reach_reward

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:54:25.997016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db39c5b9-9b97-4560-b0b6-11375773a7d0 · outbound

This paper cites Because the agents rarely reach that threshold early on, they get almost zero signal to lift beyond ~0.016 m.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Because the agents rarely reach that threshold early on, they get almost zero signal to lift beyond ~0.016 m

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.984465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9acaca1d-9d2b-4513-9d3a-165aa5292ea5 · outbound

This paper cites an unresolved cited work.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:54:25.970937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7aea6525-0f93-46ff-95fc-5475e0ba5245 · outbound

This paper cites Once the handles are touched or grasped, there is little extra push to actually raise the pot.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Once the handles are touched or grasped, there is little extra push to actually raise the pot

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.958144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bd2ebe99-9204-4922-a3b8-b0b6caf39e59 · outbound

This paper cites This gives gradient toward any increase in height, not just surpassing 0.1 m.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks This gives gradient toward any increase in height, not just surpassing 0.1 m

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.945421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8015b13-809b-4d46-924a-ccf9300627fd · outbound

This paper cites This still rewards low tilt but provides a gradient that gently pushes the pot back toward upright whenever it begins to tilt.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks This still rewards low tilt but provides a gradient that gently pushes the pot back toward upright whenever it begins to tilt

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.932738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 70989a4b-1106-4453-bd86-8174867af71d · outbound

This paper cites touch and release.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks touch and release

Reference 55

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:54:25.919759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation e3b92c80-6ea9-4bc6-855f-3eaddf39fdb3 · inbound

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation cites this paper.

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-31T21:55:17.414682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.