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Paper Citation Record · LEDGER

Bilevel Learning for Bilevel Planning

As of 9 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 4 inbound Pith citation observations for arXiv:2502.08697.

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

pith.paper-citation-record.v1
2502.08697 v3

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:02:02.712285Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T16:45:44.659577Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:27:14.845260Z

Reference resolution

66 of 66 outbound references displayed

  • verified exact0
  • verified fuzzy53
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0326365a-0a89-434e-9077-758fb111fa85 · outbound

This paper cites MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations.

Bilevel Learning for Bilevel Planning MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:01.959293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:01.959293Z digest=sha256:da9d7e1fc8e793d064a55af94526924985a3184e79b9fba2e5e57b36c7a37f44

Observation 31b0a8b4-803e-4b6b-9a18-472fadb02788 · outbound

This paper cites Dif- fusion policy: Visuomotor policy learning via action diffusion.

Bilevel Learning for Bilevel Planning Dif- fusion policy: Visuomotor policy learning via action diffusion

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:04.039975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:01.965218Z digest=sha256:6dd5914df6d0c7a7dde3940b32288127acc9d591eadb7ae1f31e2109c407c00f

Observation 7df9b183-4647-4e4f-977e-30befc5ea49d · outbound

This paper cites Learning fine-grained bimanual manipulation with low-cost hardware.

Bilevel Learning for Bilevel Planning Learning fine-grained bimanual manipulation with low-cost hardware

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:04.024618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:01.970154Z digest=sha256:f165dc40e4c2be2ebbd98adf79d0f040d550c6556ee17ec7d9e18c4becd4bdc6

Observation 4567d14a-51e1-49f5-8ae3-75f869a276fe · outbound

This paper cites Equivariant Diffusion Policy.

Bilevel Learning for Bilevel Planning Equivariant Diffusion Policy

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:01.974683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:01.974683Z digest=sha256:01eb519958a5ae70b0b78593933af98cdb2a194402b7af5adc6cbfedbe431ac5

Observation 716a7269-828e-47a6-98d8-8ed082127130 · outbound

This paper cites EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning.

Bilevel Learning for Bilevel Planning EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:01.979758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:01.979758Z digest=sha256:2b6fd93bcfd956d77747cd205e600c2089ea73320eaeb1198edcaca15cbd7e22

Observation df035090-0b92-49dd-ba8e-8073ee00bba2 · outbound

This paper cites What Planning Problems Can A Relational Neural Network Solve? In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), volume 36, 2024.

Bilevel Learning for Bilevel Planning What Planning Problems Can A Relational Neural Network Solve? In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), volume 36, 2024

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:04.009768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:01.984975Z digest=sha256:025dd4a894eed666e72bed895c931554c66e3a585486b0f8c9c1932ceed4b207

Observation 4d5e351d-b84b-45a7-88a9-36d879d980c7 · outbound

This paper cites LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation.

Bilevel Learning for Bilevel Planning LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.994572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:01.989400Z digest=sha256:85269c41133c0a901f818c77b93b1cead8fae6b370db74dbcd854af269fb2054

Observation 80f51088-a42f-4096-a682-0872851c023d · outbound

This paper cites Towards a unified theory of state abstraction for mdps.

Bilevel Learning for Bilevel Planning Towards a unified theory of state abstraction for mdps

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.979600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:01.994164Z digest=sha256:f67843a3903d818c31f5134ae2f1404b398cd0834d95fc3dd847c0b28cfd6073

Observation 5540c254-5698-4e82-aaea-1e18e3513c62 · outbound

This paper cites State abstractions for lifelong reinforce- ment learning.

Bilevel Learning for Bilevel Planning State abstractions for lifelong reinforce- ment learning

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.964999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:01.998227Z digest=sha256:b856618aaaa59d54d129d2eb133e321e0b20262d9e35a1d7b391f15ac014d450

Observation 99c1452d-d3ad-4fac-afbe-d4afbab2e437 · outbound

This paper cites From skills to symbols: Learning symbolic representations for abstract high-level planning.

Bilevel Learning for Bilevel Planning From skills to symbols: Learning symbolic representations for abstract high-level planning

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.950414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.002165Z digest=sha256:4853d148f6b873041a53f8735f1f3c13aa3567bc4f282c4c44ab20f4fcfd492d

Observation 264217f9-0841-4f20-97f3-303dfdf38e82 · outbound

This paper cites Learning Grounded Action Abstrac- tions From Language.

Bilevel Learning for Bilevel Planning Learning Grounded Action Abstrac- tions From Language

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.935398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.006186Z digest=sha256:161b63560d53ccacd78336159ea6a9170e5b8b2a0317180fec2f8dc559b1abb0

Observation 744425cc-9f97-4f3b-8ee4-c2b27b790073 · outbound

This paper cites Discovering State And Action Abstractions For Generalized Task And Motion Planning.

Bilevel Learning for Bilevel Planning Discovering State And Action Abstractions For Generalized Task And Motion Planning

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.920498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.011497Z digest=sha256:0ec3bf3a5080a79aef1d7900abbb3ddcab1fa688e28d9d0c57a44d06b69144c0

Observation 97c8481f-b937-4ffa-a03a-86b22b2c4401 · outbound

This paper cites Guiding Long-Horizon Task and Motion Planning with Vision Language Models, 2024.

Bilevel Learning for Bilevel Planning Guiding Long-Horizon Task and Motion Planning with Vision Language Models, 2024

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.905829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.016532Z digest=sha256:db6b36bb985cb5e807e54bec11eb90dcf6aacaac76e21995c7bc98cb8039974c

Observation b5b160d9-2c7e-4cc9-b265-91b864b06b5f · outbound

This paper cites From Reals to Logic and Back: Inventing Symbolic V ocabularies, Actions and Models for Planning from Raw Data.

Bilevel Learning for Bilevel Planning From Reals to Logic and Back: Inventing Symbolic V ocabularies, Actions and Models for Planning from Raw Data

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.021533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.021533Z digest=sha256:d0fb37187e9a2caede8a379475cb6ac377650de4846bbf2b4b15f55342cd62cd

Observation dc5fbc7a-0d1e-4059-bbe0-4f0e7be9513e · outbound

This paper cites Learning Symbolic Operators for Task and Motion Planning.

Bilevel Learning for Bilevel Planning Learning Symbolic Operators for Task and Motion Planning

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.890708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.026018Z digest=sha256:912d5a342b03f710f0b6d3b4bd423452078d6ee087389f53b45d7a6aab462f1f

Observation c1da0b19-3b57-4001-a4e9-d29233096ce3 · outbound

This paper cites Tenenbaum, Tom´as Lozano-P´erez, and Leslie Pack Kaelbling.

Bilevel Learning for Bilevel Planning Tenenbaum, Tom´as Lozano-P´erez, and Leslie Pack Kaelbling

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.875999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.030446Z digest=sha256:a5d24d6960a79f97818fecacae7ec3c74797d56e0c395d75fbe416b00bec7a22

Observation 8e3bd6a3-d139-462b-9a8c-2bbafcbec695 · outbound

This paper cites Predicate Invention for Bilevel Planning.

Bilevel Learning for Bilevel Planning Predicate Invention for Bilevel Planning

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.861351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.035298Z digest=sha256:53ffb3a2e94400f7ad610860c680b11fba9dd9b45caad4d3ca1256148867e57f

Observation 541f9a5b-403b-4fbe-a2b1-7881cb7d6777 · outbound

This paper cites GLIB: Efficient Exploration for Relational Model-Based Rein- forcement Learning via Goal-Literal Babbling.

Bilevel Learning for Bilevel Planning GLIB: Efficient Exploration for Relational Model-Based Rein- forcement Learning via Goal-Literal Babbling

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.846961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.040016Z digest=sha256:19e2d71e9d6db4218f4617f8925d555f6d9407abede36d021990ab3f29189a54

Observation 70ba1a4c-6702-4eeb-9b7c-ed56d64a698a · outbound

This paper cites Practice Makes Perfect: Planning To Learn Skill Parameter Policies.

Bilevel Learning for Bilevel Planning Practice Makes Perfect: Planning To Learn Skill Parameter Policies

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.832933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.061495Z digest=sha256:726bc01f124b919dcb7f8ce55b03ab6d3734d5010005314fec9eb8f4b8e02a2e

Observation 303dba06-a92d-4d7a-8a53-eea8ed85deba · outbound

This paper cites Learning Efficient Abstract Planning Models That Choose What to Predict.

Bilevel Learning for Bilevel Planning Learning Efficient Abstract Planning Models That Choose What to Predict

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.818007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.095939Z digest=sha256:d3f57eeae4c31ae0e0b058f454074af575748dc054561fedbe3a4b8e4fdd0a77

Observation a108b778-3859-451f-a05f-3eb4141e533a · outbound

This paper cites VisualPredicator: Learning Abstract World Models With Neuro-Symbolic Predicates For Robot Planning, 2024.

Bilevel Learning for Bilevel Planning VisualPredicator: Learning Abstract World Models With Neuro-Symbolic Predicates For Robot Planning, 2024

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.803533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.130417Z digest=sha256:c9a904b92403890aa47f1b007d13eecf91a05bb789a61451f55d0420128f120d

Observation 45328fc2-1f90-45c9-b959-eb963ad11f8c · outbound

This paper cites Active learning for teaching a robot grounded relational symbols.

Bilevel Learning for Bilevel Planning Active learning for teaching a robot grounded relational symbols

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.788351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.159671Z digest=sha256:1ae3ab96dffe310f026f8076af880169564e5951c0b2f3244e38f0a6955b14f5

Observation 334e3079-9ece-4fa6-a929-20fe5b30c823 · outbound

This paper cites From skills to symbols: Learning symbolic representations for abstract high-level planning.

Bilevel Learning for Bilevel Planning From skills to symbols: Learning symbolic representations for abstract high-level planning

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.773870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.187822Z digest=sha256:ba67ed3562c4425964879f80b0bb93694b186db670c936f13c63ae8ffdd8cd7f

Observation fc4614d0-aec0-4fbb-a6fd-3fbca09455b1 · outbound

This paper cites Embodied Active Learning of Relational State Abstractions for Bilevel Planning.

Bilevel Learning for Bilevel Planning Embodied Active Learning of Relational State Abstractions for Bilevel Planning

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.759222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.218577Z digest=sha256:43ffadd3a6131de4d88bbe8bfc5a2ac4f5b3bd2bcfda37c7d0d0d9ef2a782b99

Observation a332e0f7-2085-4d34-9f74-f4dd6123d8ee · outbound

This paper cites InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning.

Bilevel Learning for Bilevel Planning InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.257233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.257233Z digest=sha256:7673ef40a531106d1d8485a911a8d7ffd28ad733b750acafe9a7c442ca9429dd

Observation 1ebb3843-8fcf-49d8-b65e-6338c81d83a0 · outbound

This paper cites Grounding Predi- cates through Actions.

Bilevel Learning for Bilevel Planning Grounding Predi- cates through Actions

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.744638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.275994Z digest=sha256:57af443331f35050dcc6def24ac1c9bb425ac9623de85d8c61d8cf6a5b2cc361

Observation 0f1ba871-3f0c-4294-8692-0278e7b569d2 · outbound

This paper cites Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary.

Bilevel Learning for Bilevel Planning Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.730233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.297787Z digest=sha256:7bec1e9162b7ea774e698245ef202fb9e3ee547552121d13618e1853fe4946b5

Observation 27a1ec22-b6c4-4ac3-a815-91d326191c2e · outbound

This paper cites Unsupervised Grounding of Plannable First-Order Logic Representation From Images.

Bilevel Learning for Bilevel Planning Unsupervised Grounding of Plannable First-Order Logic Representation From Images

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.715223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.318321Z digest=sha256:eafb0a4ef45248ef2a60ef17650fbdab38ab9c8baec6f9624d1232ab765c3a1c

Observation 85c202c9-dca0-4b79-9623-e5a81aaa8691 · outbound

This paper cites Learning Neural- Dymbolic Descriptive Planning Models via Cube-Space Priors: the V oyage Home (to STRIPS).

Bilevel Learning for Bilevel Planning Learning Neural- Dymbolic Descriptive Planning Models via Cube-Space Priors: the V oyage Home (to STRIPS)

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.700663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.322757Z digest=sha256:ebf51f3bc3babfe3db4c2011a3deeeef8810f0ed55f2d64b4a165f357ce1cffc

Observation 6b64f9f0-bc78-4e11-86f2-a6bf03d1ca08 · outbound

This paper cites Bisimulation Makes Analogies in Goal-conditioned Reinforcement Learning.

Bilevel Learning for Bilevel Planning Bisimulation Makes Analogies in Goal-conditioned Reinforcement Learning

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.685952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.327293Z digest=sha256:219b7195e0a7f6cfd952645d8bfd1973864b2ab64685f02830c684dd738efc95

Observation 926c4f14-35f1-41e8-9c48-36d556b8e4c0 · outbound

This paper cites Predicate Invention from Pixels via Pretrained Vision- Language Models.

Bilevel Learning for Bilevel Planning Predicate Invention from Pixels via Pretrained Vision- Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.331349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.331349Z digest=sha256:fb432c58bacdc6719f2587e0f5a5370d1bd8c8a4e0d6cc1eaa7433312dfac1f9

Observation 1fe75e69-dc02-4643-9bb8-6acb04dd54a3 · outbound

This paper cites Tenenbaum, Tom ´as Lozano-P´erez, and Leslie Pack Kaelbling.

Bilevel Learning for Bilevel Planning Tenenbaum, Tom ´as Lozano-P´erez, and Leslie Pack Kaelbling

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.670631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.335548Z digest=sha256:94fafc33b8d52356e6a1cddd046e3dab2a808f67625bba294099f93afeed7cb8

Observation 931b0a2a-f353-45cd-be28-fe1ec787192b · outbound

This paper cites Integrated Task and Motion Planning.

Bilevel Learning for Bilevel Planning Integrated Task and Motion Planning

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.655638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.339687Z digest=sha256:e1d2edebcf39f924260f6dbd6352590002318f90d6f5841ffdbac479611769d6

Observation e3b8946a-4a4c-49b2-b1f0-e0bd42eb1aa6 · outbound

This paper cites The Fast Downward Planning System.

Bilevel Learning for Bilevel Planning The Fast Downward Planning System

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.641328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.343807Z digest=sha256:cd272cafba3b5577c7693b39c8270e63d760e870657264840bc50695423779b4

Observation 1e44301e-8acb-4c70-8a27-11c1481fb1d7 · outbound

This paper cites Divergence Measures based on the Shannon Entropy.

Bilevel Learning for Bilevel Planning Divergence Measures based on the Shannon Entropy

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.627207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.347818Z digest=sha256:4f8ee13a7c847b6b88f9a69cfa298689d177c47e39eb0e3a13886f095de124db

Observation b1b0e948-9031-42d0-974c-4e43a648ab9f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Bilevel Learning for Bilevel Planning Adam: A Method for Stochastic Optimization

Reference 36

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unresolved
no resolver link, observed 2026-08-08T00:02:02.352118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.352118Z digest=sha256:2b1b66f2dba19f4b16967302ee40432b5b03fe05364b122a2394b7704fcf70ab

Observation e8f42a3c-6364-4135-af7f-09659ee6d0d0 · outbound

This paper cites Learning Representations by Back-propagating Errors.

Bilevel Learning for Bilevel Planning Learning Representations by Back-propagating Errors

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.613560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.356372Z digest=sha256:68437769cca5c458db443a68cf7b1b7ee65eaa5c79546fbe6c5d69e90d695854

Observation 00a61b2d-735b-48fe-b9d4-d17c62afb946 · outbound

This paper cites Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search.

Bilevel Learning for Bilevel Planning Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.599355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.361327Z digest=sha256:0788a2cc5dddbd158d5708f94fa6646390187eac6662c99d44734db8765af363

Observation 31b455eb-09bd-4380-bdae-bffc7b6a478b · outbound

This paper cites Mastering the Game of Go without Human Knowledge.

Bilevel Learning for Bilevel Planning Mastering the Game of Go without Human Knowledge

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.584454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.366182Z digest=sha256:5d5a2b5969c94b655031d5560fb05da4c82d7492074ffb1238961ee99ec0a87c

Observation dd7120fd-3e4e-4f23-9ebb-22c0ad794732 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Bilevel Learning for Bilevel Planning Relational inductive biases, deep learning, and graph networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.370621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.370621Z digest=sha256:a00ab02368b67b8773b3476f1e29d9c8a3a05e79cff0456b1027bd25c2efa66e

Observation bcb2ec7f-4e2e-4bcc-8bdf-8de31168ef10 · outbound

This paper cites Attention is All You Need.

Bilevel Learning for Bilevel Planning Attention is All You Need

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.570113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.375708Z digest=sha256:aa117ff9b05510afadb24489d226dd457e4262722e054856563695a8685d3ea2

Observation 84da41bf-2548-410c-8a30-61fc3c31f0b3 · outbound

This paper cites Language Segment-Anything.

Bilevel Learning for Bilevel Planning Language Segment-Anything

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.555057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.380666Z digest=sha256:032675695f91e52d7ff9e89f304ab913906c51dea069f779340dab5456c17b8e

Observation f9f3b928-b772-40e5-8316-fe629386fddd · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Bilevel Learning for Bilevel Planning SAM 2: Segment Anything in Images and Videos

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.385466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.385466Z digest=sha256:827aa51978eecd09d1a89c342d9ba41bbf0d875a3ccf4b19d4be4bbad2a659e4

Observation b0730161-90a8-4444-8f8e-a9ac27596195 · outbound

This paper cites Hi- erarchical Task and Motion Planning in the Now.

Bilevel Learning for Bilevel Planning Hi- erarchical Task and Motion Planning in the Now

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.540368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.390730Z digest=sha256:f89e42e56d800e8e73e1f6e0514f132528f34102450d37dd3425352d0c16165c

Observation 08709afb-bbfd-4c04-b0d2-71b94608cef7 · outbound

This paper cites Relay Policy Learning: Solv- ing Long-Horizon Tasks via Imitation and Reinforcement Learning.

Bilevel Learning for Bilevel Planning Relay Policy Learning: Solv- ing Long-Horizon Tasks via Imitation and Reinforcement Learning

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.524580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.395333Z digest=sha256:a2fae4f2b40047c1b178d27d2a78998619a24eea2af5b8974ba89156c3207076

Observation af89e26f-eed6-4db9-b055-9049d1b771d4 · outbound

This paper cites Augment- ing Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks.

Bilevel Learning for Bilevel Planning Augment- ing Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.509365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.410207Z digest=sha256:e92aafd109849ef4f7b662ba3e872a889a784110ffdef9fed3a083bf9bd968fe

Observation 22e74c44-d1fc-449d-b250-19e5d666ed63 · outbound

This paper cites Neural Logic Machines.

Bilevel Learning for Bilevel Planning Neural Logic Machines

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.494687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.418370Z digest=sha256:4fdb9658ee3f6064884007e5683a33f3ddba2393eb5a104e38ea5e3207f9cad7

Observation e3239d39-70fe-4543-b9d2-cd750de34acd · outbound

This paper cites Directed-Info GAIL: Learning Hierar- chical Policies from Unsegmented Demonstrations using Directed Information.

Bilevel Learning for Bilevel Planning Directed-Info GAIL: Learning Hierar- chical Policies from Unsegmented Demonstrations using Directed Information

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.480811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.423259Z digest=sha256:b09ed728158d50bcf6f8b70cc0fde1baa5e0474381d9cd455168f4720b6476e0

Observation 6b67e16e-219f-4432-879a-af83de231bc7 · outbound

This paper cites Compile: Compositional Imitation Learning and Execution.

Bilevel Learning for Bilevel Planning Compile: Compositional Imitation Learning and Execution

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.465799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.428102Z digest=sha256:d8c4fd610dfd8042ead27a3eaec7e24776da8eec2784fe6bc3b6bed560229030

Observation d8e6f83c-2ae8-4e7d-bf34-74856bca85f4 · outbound

This paper cites PDSketch: Integrated Domain Programming, Learning, and Planning.

Bilevel Learning for Bilevel Planning PDSketch: Integrated Domain Programming, Learning, and Planning

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.450762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.433007Z digest=sha256:daf8b913adcc12094f117dd103a4b0aadb3f5d63e1278976ad2625ea59bf63d2

Observation fbbe30fd-8da4-4eba-80e7-9fd3ac01f6fa · outbound

This paper cites BLADE: Learning Compositional Behaviors from Demonstration and Language.

Bilevel Learning for Bilevel Planning BLADE: Learning Compositional Behaviors from Demonstration and Language

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.435543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.437043Z digest=sha256:17a34da3f5d3d7c50e56e91c2ae7a29d3dc4d9de05b93a1558bb0ba525c74d06

Observation 38a74271-8fa9-47a3-96a3-284f4c9ab682 · outbound

This paper cites Keypoint Abstraction using Large Models for Object-Relative Imitation Learning.

Bilevel Learning for Bilevel Planning Keypoint Abstraction using Large Models for Object-Relative Imitation Learning

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.441073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.441073Z digest=sha256:0208bd5ce7a563df1dfd174b1eb53552144fdfba2e5d51f438cba61f5a2124a5

Observation e7ef2d09-9bc0-4442-b7a1-cfbe2676eb1b · outbound

This paper cites Gener- alized Planning in PDDL Domains with Pretrained Large Language Models.

Bilevel Learning for Bilevel Planning Gener- alized Planning in PDDL Domains with Pretrained Large Language Models

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.420376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.445426Z digest=sha256:47ad0b7a25dcfeb7c1d1c34ceb27fcadd13745711399de63a319c98484847d67

Observation e190f1f7-7668-4561-90db-6eff55621c47 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Bilevel Learning for Bilevel Planning Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.403843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.449667Z digest=sha256:9a81e0f6b8c53b5becac1df24c81cb02afe4796eaf397e90068d49328b0aab83

Observation 6302c8cb-a72e-4e4e-9a39-eaab1e11a65b · outbound

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

Bilevel Learning for Bilevel Planning V oxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.345882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.476422Z digest=sha256:edb98e8b0d7be98298a052fdbed112b24d7c27bd55b9ffd0997d8fe85909bf1b

Observation 5406b303-0436-4e37-9c41-6f9c14b71bb2 · outbound

This paper cites Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning.

Bilevel Learning for Bilevel Planning Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.506842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.506842Z digest=sha256:dfc25531b00422ebfd3668b34a458e54c02c095d8ad59cfff256b479b0b0d8a8

Observation 9130dc0e-c63e-46d2-89b6-2cce8b41c63a · outbound

This paper cites Open-World Task and Motion Planning via Vision-Language Model Inferred Constraints.

Bilevel Learning for Bilevel Planning Open-World Task and Motion Planning via Vision-Language Model Inferred Constraints

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.299133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.541415Z digest=sha256:b224e11b244948756f5b457e2e830469291313d95194089900085e742008593f

Observation fbdadb50-cc9d-45e3-ae10-d27d95dae3df · outbound

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

Bilevel Learning for Bilevel Planning Pddlstream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.283063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.569168Z digest=sha256:05969b1b5eb220dd7665d7a2433a6360f03fb6e26dab2e698f6668369b32c9f6

Observation 86c4d678-dc42-4bf9-b633-e65007eac2fc · outbound

This paper cites Howe, Craig A.

Bilevel Learning for Bilevel Planning Howe, Craig A

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.269228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.593129Z digest=sha256:91fe37ec544abe891ffa185fee9d13cb4c5c5c5c7cb950c182ca698a287220bc

Observation 30457b50-e26d-44b6-81d4-cd215f6cbf1a · outbound

This paper cites Anytime Motion Plan- ning using the RRT.

Bilevel Learning for Bilevel Planning Anytime Motion Plan- ning using the RRT

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.254039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.619291Z digest=sha256:32ab6eaeb57a8e441c61911e60a6275b2b05777cb1bf5677e709c653442a99e4

Observation 98133825-cf77-4f0c-8f51-da39b9721b0d · outbound

This paper cites Skill-based curiosity for intrinsically motivated reinforcement learning.

Bilevel Learning for Bilevel Planning Skill-based curiosity for intrinsically motivated reinforcement learning

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.237553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.639776Z digest=sha256:a10048d33d5df2767c33e8c8e1d8c16f5a2abeedc4977da9f8fc277f9fd223ec

Observation 1b0df965-515b-4cde-ac18-31cec39199a5 · outbound

This paper cites an unresolved cited work.

Bilevel Learning for Bilevel Planning Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-08T00:02:03.222191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.658648Z digest=sha256:0e18caa22ee4b75da867d36692df4bec97e3224a4b56140462a6fa2fb691dcaf

Observation eae53c3a-5bc0-4270-b721-8bec9c026028 · outbound

This paper cites Hybrid Declarative-Imperative Representations for Hybrid Discrete-Continuous Decision- Making.

Bilevel Learning for Bilevel Planning Hybrid Declarative-Imperative Representations for Hybrid Discrete-Continuous Decision- Making

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.207468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.679284Z digest=sha256:de1614850c112a8aa7334f204f3d21e5e17b0420aaf99a1a1b0d3691978fe432

Observation 0a4183f1-ae7a-4bbc-b0dd-ea14d8dba546 · outbound

This paper cites Accelerating 3D Deep Learning with PyTorch3D.

Bilevel Learning for Bilevel Planning Accelerating 3D Deep Learning with PyTorch3D

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-08T00:02:02.701278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:02:02.701278Z digest=sha256:b02dcdc2ce29a854fc0c1f04b8512ded5ee18327ad1d14f6715e3e30b51435c8

Observation 23e4bab2-569f-4fd2-a3b6-677c68b2bfbf · outbound

This paper cites Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation.

Bilevel Learning for Bilevel Planning Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.191507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.707026Z digest=sha256:9b6a85952133ad2fd2ddee5cf776c1de09c2a74548334659ad2f0407443a29e8

Observation d8d51992-c589-4f38-aab1-d642afbf875e · outbound

This paper cites grounded on.

Bilevel Learning for Bilevel Planning grounded on

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:02:03.174874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T00:02:02.712285Z digest=sha256:24ec5025ce3ca479db307e9b55f12c82d2dce021bf18491879b1475fe70b57f2

Pith citing papers

Observation 85f9aea1-a2d2-4b68-a08e-1230a856a198 · inbound

Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories cites this paper.

Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories Bilevel Learning for Bilevel Planning

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-15T00:08:21.427216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T00:08:16.186576Z digest=sha256:b2fc46816d153fbbc43e961c6cb23161b1ce36c47f56d45ec9d6d8b97ee2e58f

Observation 268a69ea-c732-49c7-a9d4-d401610b4903 · inbound

Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs cites this paper.

Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs Bilevel Learning for Bilevel Planning

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:35:57.417210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T18:04:05.157103Z digest=sha256:6e37e70f23a680acab03df8e803e9f9d6a98ba0e7ee0c802b61d28c5977dfcf6

Observation ed6e7735-852e-4f3e-a9d4-68d2d292925e · inbound

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming cites this paper.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Bilevel Learning for Bilevel Planning

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-29T16:53:41.325972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T16:45:44.659577Z digest=sha256:888d330bfae845736d7decf3d4365f1a54f3adfe5d7c714b55493d9bad339bf5

Observation 9f2311c5-76f7-49f2-92e2-cf25cb7c8283 · inbound

Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints cites this paper.

Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints Bilevel Learning for Bilevel Planning

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-07-02T17:27:14.847930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T22:04:01.280390Z digest=sha256:3f0b2b36f79511042276cf89bc1470fac0b20f6bc6220a15a06ff610512d1938