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Paper Citation Record · LEDGER

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System

As of 8 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2506.18448.

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

pith.paper-citation-record.v1
2506.18448 v2

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:20:43.532697Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

73 of 73 outbound references displayed

  • verified exact4
  • verified fuzzy56
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfee9e70-49c4-4cf5-8a9d-122acf332be3 · outbound

This paper cites A survey on learning-based robotic grasping,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System A survey on learning-based robotic grasping,

Reference 1

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raw_fallback, observed 2026-08-06T23:20:58.478787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e7a68425-69ff-4b33-9d53-0eeba8527e39 · outbound

This paper cites Review of deep learning methods in robotic grasp detection,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Review of deep learning methods in robotic grasp detection,

Reference 2

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raw_fallback, observed 2026-08-06T23:20:58.301325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.125884Z digest=sha256:15e24755ea479ca8be6262f1647fa1c467c4e000ac2eaa4c8660db427b787c46

Observation 33f42aac-41b3-4cef-8846-55df79a7399f · outbound

This paper cites Real-time grasp detection using convo- lutional neural networks,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Real-time grasp detection using convo- lutional neural networks,

Reference 3

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raw_fallback, observed 2026-08-06T23:20:58.216234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.204067Z digest=sha256:18551d63b751e702a095025eb4ceb0e9b02adc982833a576d2696378ba53e710

Observation a351e099-f783-46e0-be85-0bb5ffcedc0b · outbound

This paper cites Preparatory object reorientation for task-oriented grasping,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Preparatory object reorientation for task-oriented grasping,

Reference 4

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raw_fallback, observed 2026-08-06T23:20:57.990560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.253346Z digest=sha256:f9ecb1a4b8ac6f327f6d4b98941be8cb51895372e7e83e54a588f52392bf2947

Observation d5401055-9c73-407c-9713-d23281855aa3 · outbound

This paper cites Jacquard: A large scale dataset for robotic grasp detection,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Jacquard: A large scale dataset for robotic grasp detection,

Reference 5

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raw_fallback, observed 2026-08-06T23:20:57.736623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.401600Z digest=sha256:fd49706e1dbb99e9bfde0be22ba8cc20cb923257611fc8ba10151182528be967

Observation 758ec55c-540e-481f-af07-e340f97a72a0 · outbound

This paper cites Graspnet-1billion: A large- scale benchmark for general object grasping,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Graspnet-1billion: A large- scale benchmark for general object grasping,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.448889Z digest=sha256:a45f5ebf4d2b2e4789b88889d4ab911026bd957df2fb57f24f73c4322c72267a

Observation 2c8a2e67-60ef-4fed-b429-42222feadcc5 · outbound

This paper cites When transformer meets robotic grasping: Exploits context for efficient grasp detection,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System When transformer meets robotic grasping: Exploits context for efficient grasp detection,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.516034Z digest=sha256:210c15ac9b9467be465997e8b89051bd65465011e6a3c1ac4cf2ae498c51ef73

Observation 9ecc8bbb-b95d-4bf1-8501-a4206a9e5c67 · outbound

This paper cites Language-driven grasp detection,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Language-driven grasp detection,

Reference 8

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raw_fallback, observed 2026-08-06T23:20:57.049981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.579080Z digest=sha256:cd3128d2673645670639464ff71bd271b9d2f489510a055718828894bc683f30

Observation 673999b0-da49-4f87-b56f-e4f97dc41a30 · outbound

This paper cites Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter

Reference 9

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local_arxiv, observed 2026-08-06T23:20:45.178010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.609767Z digest=sha256:108a66a35790c54ae28c5bdf071c65dfc86b6e4831fd7e772dbde7dee14a3f15

Observation af492a5c-f929-475a-91a5-026a5773ef3d · outbound

This paper cites Language models are few-shot learners,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Language models are few-shot learners,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:38.674760Z digest=sha256:f632935de59d047c229f5f0eff6ba72ac529c66e8e195f3e6eeac0c7eb4066a8

Observation 58cb3bed-a02d-4fae-b018-f2fc2c7406a3 · outbound

This paper cites Gpt-4 technical report,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Gpt-4 technical report,

Reference 11

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no resolver link, observed 2026-08-06T23:20:38.778580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:38.778580Z digest=sha256:a794a77d9e593b0bd3b8f6cea1f93686fe398746158f28429b90d41537919608

Observation a6f4ae32-61e4-4be4-be6d-7806892cf27f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 12

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no resolver link, observed 2026-08-06T23:20:38.843155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:38.843155Z digest=sha256:7751310425833a145ea0dc25860f15491829410afc9ac2f80f465ac408cce864

Observation 432299de-d598-41c0-a191-6184e0cb596e · outbound

This paper cites DeepSeek-V3 Technical Report.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System DeepSeek-V3 Technical Report

Reference 13

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no resolver link, observed 2026-08-06T23:20:38.927491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:38.927491Z digest=sha256:c48b8a84bfd554451913699fb47c0e222cf9428a81e7724dc05e9b8377540848

Observation ff4ec0e8-b75c-4d34-bb41-065499fcb4fe · outbound

This paper cites Do as i can, not as i say: Grounding language in robotic affordances,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Do as i can, not as i say: Grounding language in robotic affordances,

Reference 14

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raw_fallback, observed 2026-08-06T23:20:56.519220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.042509Z digest=sha256:6dba7a76aad5b28c97f974044903a79413206bc6457f6b4bd4f8178354f5e031

Observation a80f6429-bad9-4015-bf96-dc37fb953111 · outbound

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

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Progprompt: Generating situated robot task plans using large language models,

Reference 15

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raw_fallback, observed 2026-08-06T23:20:56.214734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.117593Z digest=sha256:3ad2152c259150d2f20eb6121c7f1ad8f19bbd284c329552d3bfbff92f534870

Observation 204cacfc-e23c-453c-86b9-88e9190dcc2b · outbound

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

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Code as policies: Language model programs for embodied control,

Reference 16

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raw_fallback, observed 2026-08-06T23:20:55.944894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.187018Z digest=sha256:f236ffaac3a4a1f29ea9f747cdcca036af3c7d2b19a7de45e4b5c347a4013981

Observation 1dd424ce-a6d8-46ed-8272-3fe0e0ef052c · outbound

This paper cites Socratic models: Composing zero-shot multimodal reasoning with language,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Socratic models: Composing zero-shot multimodal reasoning with language,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.380956Z digest=sha256:c1e943ee5fd9d85fb7cd38848021f112594287386b19afc77fb1c4ac4290afba

Observation 77296287-c655-4e8f-ae5f-b6580c84e3a8 · outbound

This paper cites L3mvn: Leveraging large language models for visual target navigation,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System L3mvn: Leveraging large language models for visual target navigation,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.438424Z digest=sha256:35035fe804324c5390e4fd16fc18c7a38288c0132752b9cdd7b83d230647d2a6

Observation 6cbfbcb9-892a-4ab2-b578-ab491770c62f · outbound

This paper cites Adapt: Vision-language navigation with modality-aligned action prompts,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Adapt: Vision-language navigation with modality-aligned action prompts,

Reference 19

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raw_fallback, observed 2026-08-06T23:20:55.204823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.490045Z digest=sha256:b8063972906cd67553f6cadd1b8989be64c29924ed563565699aca6663fcbe1b

Observation 714b6050-5789-4027-817d-89664a20e4e5 · outbound

This paper cites Language-driven grasp detection with mask-guided attention,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Language-driven grasp detection with mask-guided attention,

Reference 20

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raw_fallback, observed 2026-08-06T23:20:54.933331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.577677Z digest=sha256:981d6255733b76cb19a0951b12f1d8a98d83445dbaeffc26ac997e18f192aa21

Observation e3b1d6ff-87d8-4504-9ed0-8eaf8ebc45c9 · outbound

This paper cites Cliport: What and where pathways for robotic manipulation,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Cliport: What and where pathways for robotic manipulation,

Reference 21

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raw_fallback, observed 2026-08-06T23:20:54.709204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.675782Z digest=sha256:9ac09a6bfd2d8899fc10fb475ecb52464729e7579c5ba08db954e1663ac03354

Observation 03fa2c75-1729-4faa-96db-15822b2ac81d · outbound

This paper cites A joint modeling of vision-language-action for target- oriented grasping in clutter,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System A joint modeling of vision-language-action for target- oriented grasping in clutter,

Reference 22

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raw_fallback, observed 2026-08-06T23:20:54.485488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.772992Z digest=sha256:c9f2d48d255b4660f3c078219c64be64a74e0a528adfd1b12b0eebfcc417def2

Observation 1decbaf5-0f8c-4051-a14b-937639a323c3 · outbound

This paper cites Language-driven 6-dof grasp detection using negative prompt guidance,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Language-driven 6-dof grasp detection using negative prompt guidance,

Reference 23

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raw_fallback, observed 2026-08-06T23:20:54.204834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.913088Z digest=sha256:9101d4164164d4c28bdbd8d9b7d53c4fb9b52181ec6709b1277543bebe48f3b7

Observation f788f13e-2cb7-4708-931e-aed5303dcc64 · outbound

This paper cites Lightweight language-driven grasp detection using con- ditional consistency model,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Lightweight language-driven grasp detection using con- ditional consistency model,

Reference 24

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raw_fallback, observed 2026-08-06T23:20:53.862299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:39.958606Z digest=sha256:cefd617456d37bb96dbc8d13d1a0ba029b798973a5f7515ab7a843ed37ac5837

Observation cfc20712-79ad-406b-a80b-fcdfd678d3a2 · outbound

This paper cites Grasp-anything: Large-scale grasp dataset from foundation models,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Grasp-anything: Large-scale grasp dataset from foundation models,

Reference 25

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raw_fallback, observed 2026-08-06T23:20:53.614791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.029397Z digest=sha256:16306cab745556d0d9bef7a7f16d9c5e03afbba2820e4962d6b052b062a5b541

Observation 7e4bb0c3-4c76-4667-9d59-4b60dcdb74ac · outbound

This paper cites Graspgpt: Leveraging semantic knowledge from a large language model for task- oriented grasping,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Graspgpt: Leveraging semantic knowledge from a large language model for task- oriented grasping,

Reference 26

Resolution
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raw_fallback, observed 2026-08-06T23:20:53.226753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.107111Z digest=sha256:15dd883ee2aea8ed6fbf451c1ea08700fd5b13a1a25266277ec0dd599a94bfc9

Observation d9d89094-d5b9-47f3-bc44-0f4008f8205e · outbound

This paper cites Towards open-world grasping with large vision-language models,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Towards open-world grasping with large vision-language models,

Reference 27

Resolution
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raw_fallback, observed 2026-08-06T23:20:53.016195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.184397Z digest=sha256:9236828892f11279a6d9498f5865f20b811b638186f46a1ab785b629409c73a7

Observation 79774b5f-1e34-47ca-a51d-cdaba29d8d52 · outbound

This paper cites Thinkgrasp: A vision-language system for strategic part grasping in clutter,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Thinkgrasp: A vision-language system for strategic part grasping in clutter,

Reference 28

Resolution
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raw_fallback, observed 2026-08-06T23:20:52.783774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.236074Z digest=sha256:f26913876fd075b65b18aea9706723f82455838becae83dbd99b272f9d55ba31

Observation 4bf2d364-8391-42e5-bfa4-535970f895ef · outbound

This paper cites Visual programming: Compositional visual reasoning without training,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Visual programming: Compositional visual reasoning without training,

Reference 29

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raw_fallback, observed 2026-08-06T23:20:52.365710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.288021Z digest=sha256:df90f72339ca9bce94c618e27d6f24a783e1562642e90b4670c078151a14a508

Observation 5d04ed58-8e94-45b7-a30c-8d99870746ed · outbound

This paper cites Chameleon: Plug-and-play compositional reasoning with large language models,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Chameleon: Plug-and-play compositional reasoning with large language models,

Reference 30

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raw_fallback, observed 2026-08-06T23:20:52.070158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.360181Z digest=sha256:4121a336158ba6d0bdce8c925cf5f30e28b809c8e05b6fecb37a7e4df007a26b

Observation 50828b12-25b9-42a1-b4db-f929672ee366 · outbound

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

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Vipergpt: Visual inference via python execution for reasoning,

Reference 31

Resolution
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raw_fallback, observed 2026-08-06T23:20:51.794883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.421297Z digest=sha256:bbdf6309c1e134120f37155e011154e658bb1b3e404f0b3b99134ed4b8d9b6e4

Observation df4653e7-480d-4fb7-94b6-c5730a65c855 · outbound

This paper cites Videoagent: A memory-augmented multimodal agent for video understanding,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Videoagent: A memory-augmented multimodal agent for video understanding,

Reference 32

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raw_fallback, observed 2026-08-06T23:20:51.509889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.478212Z digest=sha256:35c5f1f2bab42e6f4dfe4ce0aa58eba8bd3f2eaf3bb3b07f3cb8822ba6b9c705

Observation 0f6e4bc0-1ab0-45af-8e25-d4979012532a · outbound

This paper cites RoboCoder: Robotic Learning from Basic Skills to General Tasks with Large Language Models.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System RoboCoder: Robotic Learning from Basic Skills to General Tasks with Large Language Models

Reference 33

Resolution
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no resolver link, observed 2026-08-06T23:20:40.532400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:40.532400Z digest=sha256:1a9ab6553eee85c15a537c4b26834b5ed54fec162c61b4a91d3b3261346184b4

Observation af9149bf-fda0-49f7-820a-eec8a5527310 · outbound

This paper cites Mp5: A multi-modal open-ended embodied system in minecraft via active perception,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Mp5: A multi-modal open-ended embodied system in minecraft via active perception,

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-06T23:20:51.339267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.565582Z digest=sha256:b6dfe27485d69d5183a02a530e23aac0503a74e40a1302b1fe07bd50afe5b418

Observation 5192b1bb-95ce-4675-ad7c-4fda76e82aba · outbound

This paper cites Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:51.046489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.608150Z digest=sha256:a3ce7976f5de766d0275af7ada0a23d0150e5705ab518921135efbe48003c310

Observation 3c544802-9ba9-4d5b-acee-d2a5beac2ad2 · outbound

This paper cites Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:50.814838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.660103Z digest=sha256:1f009a05f3ab2bb25ee9e6581911abbf838bc1b02e119ddd25f0df745bef80d3

Observation bf683d6a-e217-4e51-b2ad-73a46800adbd · outbound

This paper cites Fast graspability evaluation on single depth maps for bin picking with general grippers,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Fast graspability evaluation on single depth maps for bin picking with general grippers,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:50.592284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.714184Z digest=sha256:317a28db18ec9276abe120cddfa465a120318a6a8f7d3edffa15ed622caf5d62

Observation 7a692630-4884-4d83-a47e-79cc37d73899 · outbound

This paper cites Grasp quality measures: review and performance,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Grasp quality measures: review and performance,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:50.348701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.745975Z digest=sha256:6f774b097edb175da6bf34f67bb823d90aaff970ec25c67f7f4493d57ae2fcee

Observation f10f6f12-1dac-4855-b4fc-ce025d6950d0 · outbound

This paper cites Deep learning for detecting robotic grasps,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Deep learning for detecting robotic grasps,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:50.052152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.797684Z digest=sha256:6bffb73c523617dc596cac827e3e488cf37aaebbb628fea47a3df1098b81ab98

Observation 41f17743-2761-4d2a-b6bd-eb7afbe6343b · outbound

This paper cites Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:49.796014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.877229Z digest=sha256:bd9c59a46a1cdbbcd3d1cdfec7c24138b274df14dc0ec7213e818c71eaa12a10

Observation 42e4d2c8-ad5a-40a6-b08b-1d312e6c06de · outbound

This paper cites Antipodal robotic grasping using generative residual convolutional neural network,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Antipodal robotic grasping using generative residual convolutional neural network,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:49.634233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.958055Z digest=sha256:7351bc2581330a661d687226e9deead18b10035bffc6fec4f1ac93d184d7c476

Observation 5b22090c-2e84-4415-8098-3123ecb33340 · outbound

This paper cites Vl-grasp: a 6- dof interactive grasp policy for language-oriented objects in cluttered indoor scenes,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Vl-grasp: a 6- dof interactive grasp policy for language-oriented objects in cluttered indoor scenes,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:49.434408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:40.986915Z digest=sha256:f4839d869c2bf80cc4a77cfeaa6137376927161c2cf41a8a89e9df94b5c27c74

Observation 9fb1aee6-5d1c-4308-8f46-b24c36d52bc3 · outbound

This paper cites Learning 6-dof object poses to grasp category-level objects by language instructions,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Learning 6-dof object poses to grasp category-level objects by language instructions,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:49.254959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.075317Z digest=sha256:13f9b431d242e176ec6ff0127fbff6a772efa68004313420e3eed12cda9fc82c

Observation 02da47ca-eb13-4ea0-882d-ca00ca5b0753 · outbound

This paper cites A joint network for grasp detection conditioned on natural language commands,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System A joint network for grasp detection conditioned on natural language commands,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:49.077267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.134925Z digest=sha256:972ff267c7b59c96e13e134b9efd996677f8c5b0f652c12ca2f96aa7c3fc972b

Observation 2cbfa97f-36f7-4c9f-9613-01c5960b4086 · outbound

This paper cites Reasoning Grasping via Multimodal Large Language Model.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Reasoning Grasping via Multimodal Large Language Model

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:41.189053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:41.189053Z digest=sha256:c458a7d7961b13a687ce871283351d7412763abd7c95b8d207d0f7b225a01f07

Observation faad749a-b314-44ba-9912-c34dc1ef1dba · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:41.227600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:41.227600Z digest=sha256:dd1afbc9854c9cdf19ca9c78fde15e8e557cb131bd91ede00e93f169ca5adc99

Observation 2a147a66-267e-42bd-ae6d-5597e9b241ca · outbound

This paper cites Hydra: A hyper agent for dynamic compositional visual reasoning,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Hydra: A hyper agent for dynamic compositional visual reasoning,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:48.917107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.290925Z digest=sha256:dc147b5face0dd440f68083ec622bdcbc95caab965dd42a384f1453fb35e6014

Observation 5ce42002-57c6-40ae-804e-f4a905d26c36 · outbound

This paper cites Visual program distillation: Distilling tools and programmatic reasoning into vision-language models,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Visual program distillation: Distilling tools and programmatic reasoning into vision-language models,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:48.671109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.354744Z digest=sha256:34caf23fb49071926f6e75708a80705165993f6636838750a0e67b80969cd509

Observation 36b90d28-34cd-4e24-925c-1b0637d5b54c · outbound

This paper cites CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:41.428788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:41.428788Z digest=sha256:369215a34f071ba80a280acc0c332642b5a5bc50cbaada5e04f1cecefe8415ca

Observation 339de7b0-622d-427a-8e72-e10320022808 · outbound

This paper cites Genegpt: Augmenting large language models with domain tools for improved access to biomedical information,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Genegpt: Augmenting large language models with domain tools for improved access to biomedical information,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:48.464990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.525557Z digest=sha256:3297976668ebc94c9601c3a72f7ffe99c1e8e67b8a44d900401c271756ba2c3d

Observation a64254bb-d18d-4680-9da3-7fad1b98e933 · outbound

This paper cites Building cooperative embodied agents modularly with large language models,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Building cooperative embodied agents modularly with large language models,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:48.251824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.586519Z digest=sha256:22d331296292c2f39e04ffeca69d4bcdfb246eb011e33bacacf8e65f2eb6cabc

Observation 57a36006-0d7f-4c47-a3af-c3ae02d75e68 · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:48.028266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.635084Z digest=sha256:d6cafd07300c16a55905a4fd557ed8ee44fec5304019411560d431f23e2bac67

Observation 6e2f7894-c191-4776-b22b-c5d67f5ab59b · outbound

This paper cites Learning to compose visual relations,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Learning to compose visual relations,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:47.843952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.725781Z digest=sha256:082d62a667051c1b9450dfcfe863638f2dedde99ddc4a06e34534dc590c03a5a

Observation 4be45bda-0ff2-400d-afec-a175b6548835 · outbound

This paper cites Cplip: zero-shot learning for histopathology with comprehensive vision-language alignment,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Cplip: zero-shot learning for histopathology with comprehensive vision-language alignment,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:47.709222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.752543Z digest=sha256:4017bed9a00d2f5e4f13e583e1fc4af00a4e2b97ffdb80d25094da8d45058ad3

Observation 500d0d64-2cc0-4406-9d3d-324d8247f16a · outbound

This paper cites Zero-shot object detection through vision- language embedding alignment,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Zero-shot object detection through vision- language embedding alignment,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:47.543015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:41.918872Z digest=sha256:0c99507330c077245228950267a164bf746e861e383231050aaaa9da8ecc8e45

Observation 309a17b8-1804-4757-9f29-28cc4dab23e6 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:41.972161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:41.972161Z digest=sha256:33fe4765c5f4b4713e9b5a515b7409c9654fec6b834a7d4c72fdd83a6f5e1674

Observation 63db8bcb-1795-46c3-9d8f-d0097cc0de0d · outbound

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

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:42.024833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:42.024833Z digest=sha256:a21dfe9c014e6db9bf4cf1ab1508ee44cfdd435682cd1d38de1a0b681182e973

Observation d5c305f3-3ad5-4756-9dbd-d285a2702797 · outbound

This paper cites Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:42.118904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:42.118904Z digest=sha256:432d6d8a932de8514b7bf3fd3a77b17e556704b51ca4ff1064dea99a95453019

Observation 8bd3b039-8bc1-4c45-9a45-2e21b8c89c8e · outbound

This paper cites Shapegrasp: Zero-shot task-oriented grasping with large language models through geometric decomposition,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Shapegrasp: Zero-shot task-oriented grasping with large language models through geometric decomposition,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:47.424671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:42.296239Z digest=sha256:a1505622458929e68dcef6064207349c8b0a749c2ca745a9b7f65155edc2db1a

Observation 3d7a9e15-8312-4fad-bb25-65f9802dd5ce · outbound

This paper cites MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:42.368532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:42.368532Z digest=sha256:09648bfffc22554d05f4ad043d58bf006c15570fe6738697d28cbd74bb652391

Observation 00689c0d-5be4-42ec-9d5c-540bb0f09755 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:47.260024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:42.445912Z digest=sha256:c152cb04d7fda5bac6f8c0e29d97f05308326aadc8d49a1fe0146e994ce94e50

Observation 7e1e31ee-03ba-40a5-b10d-62f014e4d95f · outbound

This paper cites Going denser with open-vocabulary part segmentation,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Going denser with open-vocabulary part segmentation,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:46.737106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:42.505893Z digest=sha256:c9a2228e7f6971258040433a90169b71a7f1c0fd74a90695c75ef11ddcd041e5

Observation f292db17-dd10-4101-948c-c97482a66fed · outbound

This paper cites NBMOD: Find It and Grasp It in Noisy Background.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System NBMOD: Find It and Grasp It in Noisy Background

Reference 63

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:20:44.514806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:42.654748Z digest=sha256:7d75f1a36886b57fead754da599074d43d332a827576a0ac1f8518ac3db833c0

Observation 78a074e2-e803-4052-aeec-dd9683c90acb · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:42.731550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:42.731550Z digest=sha256:5dc8746a8ca67594a03151ffe4ac6f93b1a7c1720cbc61a53a4ab7da74a919cc

Observation 85ef4368-3eac-4b9d-a2f5-46b44ec53390 · outbound

This paper cites Towards robust monocular depth estimation: Mixing datasets for zero- shot cross-dataset transfer,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Towards robust monocular depth estimation: Mixing datasets for zero- shot cross-dataset transfer,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:46.514746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:42.855477Z digest=sha256:8233514f3084757785d2159c5a2cc4ae44ba77fa0b36cc8ef964c8c52406178e

Observation dc55bcde-017d-451e-b737-630020d5c272 · outbound

This paper cites Segment anything,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Segment anything,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:46.314732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:42.951504Z digest=sha256:8b4e6309e12476c2addfeec7da7496cb2a72550bfab030626efa5b8fd0f1cb31

Observation a6724e8b-00b7-410e-85ad-67293c5a751e · outbound

This paper cites Scaling open-vocabulary object detection,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Scaling open-vocabulary object detection,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:46.134672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:43.091070Z digest=sha256:b8e32de456dda5f7e27c90def10697f4c7d2b21c8cf562bba41fc0e1200907ed

Observation c45167f0-e4fe-44da-95a7-270f09aec9a1 · outbound

This paper cites Language-driven grasp detection with mask-guided at- tention,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Language-driven grasp detection with mask-guided at- tention,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:46.005098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:43.142152Z digest=sha256:0ad204108d364e438eae9b6c31ef85b9168957418d81fae53d0475c61808b463

Observation 0312bd0e-7654-4a09-a987-eb6be1cc849f · outbound

This paper cites GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning

Reference 69

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:20:44.279602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:43.185720Z digest=sha256:551cb9f3c9c525fd9b5e8c4c28537eca05df85c3729e8ddb9802d217f020332c

Observation 724131e9-4f71-42b9-90cd-dda6d602113b · outbound

This paper cites GraspSAM: When Segment Anything Model Meets Grasp Detection.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System GraspSAM: When Segment Anything Model Meets Grasp Detection

Reference 70

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:20:43.804832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:43.282965Z digest=sha256:833811d91e4fc69650fc31be2588dc0ea32156d25f0710cae69cd01e64a96ecb

Observation 0367be81-da20-47eb-b16a-ad330c98c9f0 · outbound

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

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:43.355361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:43.355361Z digest=sha256:cea08f2cee65486e7bd158ccaeccf550c589bf841732e6cbbd3cd9f7b7011d21

Observation f2da0074-c305-49a7-8ee9-1b6f0fc0f39d · outbound

This paper cites Benchmarking in manipulation research: Using the yale-cmu- berkeley object and model set,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Benchmarking in manipulation research: Using the yale-cmu- berkeley object and model set,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:45.663971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:43.439427Z digest=sha256:0c7408bb595bdab4635db2183326e5f37491a226ec9332d9e677d98761a5a2a4

Observation e6588256-eead-46be-a03c-b47216a53b9c · outbound

This paper cites Machine learning-based framework for optimally solving the analytical inverse kinematics for redundant manipulators,.

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System Machine learning-based framework for optimally solving the analytical inverse kinematics for redundant manipulators,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:45.405318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:43.532697Z digest=sha256:6942378696dfb441c147340830e786567700ffa433f311c2307beb68a6894dc6

Pith citing papers

No inbound Pith citation observations are available.