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Paper Citation Record · LEDGER

ACTLLM: Action Consistency Tuned Large Language Model

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2506.21250.

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

pith.paper-citation-record.v1
2506.21250 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:34:09.950077Z

measured 47 of 47 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

47 of 47 outbound references displayed

  • verified exact1
  • verified fuzzy23
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2e14552-e356-442b-b162-1293a29891e7 · outbound

This paper cites VIMA: General Robot Manipulation with Multimodal Prompts.

ACTLLM: Action Consistency Tuned Large Language Model VIMA: General Robot Manipulation with Multimodal Prompts

Reference 1

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source=pdf_text observed=2026-08-06T22:34:05.695868Z digest=sha256:32e6789264659629b1f7e4015c91199f2de0ad0f97f2153fd73a6c7ecc061a3b

Observation d0d6ad18-5a0b-4bac-aa5c-84d75258ecf1 · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model Cliport: What and where pathways for robotic manipulation,

Reference 2

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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=2026-08-06T22:34:05.793845Z digest=sha256:9020b47eb9be3f39868d777407e3df0fc0257e437e83525bec82ff5e6fe97b40

Observation 065e3ccc-7b2c-402e-bdcf-7a848026e07a · outbound

This paper cites Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning.

ACTLLM: Action Consistency Tuned Large Language Model Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning

Reference 3

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source=pdf_text observed=2026-08-06T22:34:05.955444Z digest=sha256:020a84e0b67531adea0ecd4dbddb30fcae8e3d6a44d3de485a0058122ec87de1

Observation 748f74db-ff43-4a7e-b361-2a4b0746d096 · outbound

This paper cites Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,.

ACTLLM: Action Consistency Tuned Large Language Model Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,

Reference 4

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source=pdf_text observed=2026-08-06T22:34:06.038460Z digest=sha256:b03f5a214ffd4dcb8cd2426fea79fff7e01f21c1ed9d1b1dd52600ddf473ace6

Observation 9587ffe8-f932-40e7-9d7a-caf6397504f1 · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model PaLM-E: An Embodied Multimodal Language Model

Reference 6

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source=pdf_text observed=2026-08-06T22:34:06.269291Z digest=sha256:081f03da2ab5aea9af9e6feddd6f603cd848f27d4048cd24ca2e1b1e3ae400a8

Observation fa26ac5c-497d-4a9d-910e-a243197c516b · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model RT-1: Robotics Transformer for Real-World Control at Scale

Reference 7

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source=pdf_text observed=2026-08-06T22:34:06.421490Z digest=sha256:cc8c11e7be0bcf2cfb71e446273676b17fad148cc7a69e625e022e8d058bccd3

Observation d4972fce-dd48-4144-bd14-997f2c0a6a29 · outbound

This paper cites Understanding natural language commands for robotic navigation and mobile manipulation,.

ACTLLM: Action Consistency Tuned Large Language Model Understanding natural language commands for robotic navigation and mobile manipulation,

Reference 8

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raw_fallback, observed 2026-08-06T22:34:15.114337Z

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=2026-08-06T22:34:06.593137Z digest=sha256:47a9bcc81a377e699b236ae4639c9495e5b178d1c517833353831cb0d1c8e945

Observation c0190b68-6726-4d51-925c-eb772a4a5cb2 · outbound

This paper cites Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model.

ACTLLM: Action Consistency Tuned Large Language Model Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

Reference 9

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source=pdf_text observed=2026-08-06T22:34:06.759535Z digest=sha256:146835180d857c5cfc873dd902a4351baf4932b40ab06a7d0824baa32a0809ff

Observation 8034934a-aed6-4a0c-989f-8d9236232a4b · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

ACTLLM: Action Consistency Tuned Large Language Model R3M: A Universal Visual Representation for Robot Manipulation

Reference 10

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source=pdf_text observed=2026-08-06T22:34:06.871309Z digest=sha256:f4ad14e5fbc81e8dadc8e119d08688bac9f3d22d26987aabc68806d45bdabdfc

Observation 2dd77612-a29d-4f7d-ac99-7c3666d3cd22 · outbound

This paper cites Policy adaptation from foundation model feedback,.

ACTLLM: Action Consistency Tuned Large Language Model Policy adaptation from foundation model feedback,

Reference 11

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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=2026-08-06T22:34:06.973647Z digest=sha256:43e3988ce8a63d17f700b9094f7095f3f98463f63976eb1d6b615209921cbd98

Observation 4d834f1f-e923-4dc1-b89e-e91ec596f5e3 · outbound

This paper cites Programmatically Grounded, Compositionally Generalizable Robotic Manipulation.

ACTLLM: Action Consistency Tuned Large Language Model Programmatically Grounded, Compositionally Generalizable Robotic Manipulation

Reference 12

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source=pdf_text observed=2026-08-06T22:34:07.098920Z digest=sha256:f3cd39cdf5736902965305acef78b9c1449eb013be488a289c7fd15b0c7f0ffd

Observation 234bbfda-8b85-4b10-aeff-9e19fdb23b8a · outbound

This paper cites SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling.

ACTLLM: Action Consistency Tuned Large Language Model SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling

Reference 13

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source=pdf_text observed=2026-08-06T22:34:07.219900Z digest=sha256:f5d88a6abf1afac74fc9d97276b078d4feaf5e930a232d3cd7b8ecc58535c33d

Observation c34df5fc-d3db-4fe0-82bc-95ef4fdce82d · outbound

This paper cites LIV: Language-Image Representations and Rewards for Robotic Control.

ACTLLM: Action Consistency Tuned Large Language Model LIV: Language-Image Representations and Rewards for Robotic Control

Reference 14

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source=pdf_text observed=2026-08-06T22:34:07.321935Z digest=sha256:b3c4e2902de477816f589fc0a6316fc49a86584758ed5d4828359b2c5b8161b2

Observation 1ba05781-4933-44f5-88f4-0c64a23f4219 · outbound

This paper cites Reward design with language models,.

ACTLLM: Action Consistency Tuned Large Language Model Reward design with language models,

Reference 15

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source=pdf_text observed=2026-08-06T22:34:07.450202Z digest=sha256:50b6e2ebdb87b8c7235657ce47dbf954bb397278389f418c2a766ddc65112d0f

Observation 9ac9f634-29b9-4390-89fc-1d9dc1a80649 · outbound

This paper cites Language to Rewards for Robotic Skill Synthesis.

ACTLLM: Action Consistency Tuned Large Language Model Language to Rewards for Robotic Skill Synthesis

Reference 16

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source=pdf_text observed=2026-08-06T22:34:07.558279Z digest=sha256:4d40b29983f6bbad6ddf61cb4eb9ed9526cd9c87da1302d0905d147d00be2163

Observation 13209cc9-00d7-4e39-a934-4804ba8de4c6 · outbound

This paper cites Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences,.

ACTLLM: Action Consistency Tuned Large Language Model Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences,

Reference 17

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raw_fallback, observed 2026-08-06T22:34:14.617055Z

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=2026-08-06T22:34:07.673660Z digest=sha256:6b3ceb093da2462424e8bc8edd97dc511fb38c4b3e12b063563028d1cad15d89

Observation 3e38b1c1-b8e2-416f-bf70-461e69ae78ae · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model ReAct: Synergizing Reasoning and Acting in Language Models

Reference 18

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source=pdf_text observed=2026-08-06T22:34:07.760328Z digest=sha256:ae34581b18de3b86d665892550b3f66f663394c98fc7bfb1ea3f05d337e21766

Observation 77dc246a-a543-4e9d-a7d4-692476309363 · outbound

This paper cites Self-supervised 6d object pose estimation for robot manipulation,.

ACTLLM: Action Consistency Tuned Large Language Model Self-supervised 6d object pose estimation for robot manipulation,

Reference 19

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

source=pdf_text observed=2026-08-06T22:34:07.831247Z digest=sha256:76e08d9813b92ec60a490706ab5a6eb725245ffa0a60d3a549c8a0b7d52ba147

Observation ce8f7226-230e-4956-857a-3aa84e9f4221 · outbound

This paper cites The best of both modes: Separately leveraging rgb and depth for unseen object instance segmentation,.

ACTLLM: Action Consistency Tuned Large Language Model The best of both modes: Separately leveraging rgb and depth for unseen object instance segmentation,

Reference 20

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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=2026-08-06T22:34:07.873574Z digest=sha256:3ef3937427c77b764be4e893bf8cc130ef81b947b576942f4b48aaf1ce415243

Observation bf85238b-03e3-42d7-a6b0-51edb3c2a762 · outbound

This paper cites QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation.

ACTLLM: Action Consistency Tuned Large Language Model QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Reference 21

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source=pdf_text observed=2026-08-06T22:34:07.987511Z digest=sha256:3755d122b598b4e925161d1310c6cccd21f32d461491cd657d579ee1cf706432

Observation f7b08611-b7ea-4412-8890-3cd787833f0d · outbound

This paper cites Learning efficient illumination multiplexing for joint capture of reflectance and shape.

ACTLLM: Action Consistency Tuned Large Language Model Learning efficient illumination multiplexing for joint capture of reflectance and shape

Reference 22

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

source=pdf_text observed=2026-08-06T22:34:08.037144Z digest=sha256:e97b3c6d01c629057773cd1462574cccf90f0d3565325fbb1060287b6b8eb2e7

Observation 59912bb7-6d17-4e5a-bfbb-dd1807106524 · outbound

This paper cites Coarse-to- fine q-attention: Efficient learning for visual robotic manipulation via discretisation,.

ACTLLM: Action Consistency Tuned Large Language Model Coarse-to- fine q-attention: Efficient learning for visual robotic manipulation via discretisation,

Reference 23

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

source=pdf_text observed=2026-08-06T22:34:08.040499Z digest=sha256:ce08837bcfade58e12dccd074655f4a62084fe4ae6c055e2e2fabb49c2c8ee72

Observation 5d183426-fc16-4556-97bf-a655df7a95fb · outbound

This paper cites Transporter networks: Rearranging the visual world for robotic manipulation,.

ACTLLM: Action Consistency Tuned Large Language Model Transporter networks: Rearranging the visual world for robotic manipulation,

Reference 24

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

source=pdf_text observed=2026-08-06T22:34:08.044847Z digest=sha256:e24e18d7ad369d850f49da88940948539ff7852ba03247d13bfcb85935cad07b

Observation 547e2f39-98ab-4fae-bf8a-038ec002b573 · outbound

This paper cites Guiding multi-step rearrangement tasks with natural language instructions,.

ACTLLM: Action Consistency Tuned Large Language Model Guiding multi-step rearrangement tasks with natural language instructions,

Reference 25

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raw_fallback, observed 2026-08-06T22:34:13.637993Z

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=2026-08-06T22:34:08.073663Z digest=sha256:7e6c01297a8bc986b344ccd58abba04ccd93814440d7957f9c0ccbacff66f7dd

Observation 01bb29b4-fd02-4bfe-9631-2b2b85a21ddd · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model Do as i can, not as i say: Grounding language in robotic affordances,

Reference 26

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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=2026-08-06T22:34:08.129519Z digest=sha256:184d6ce35dc4ccbdaf723bf2e4c88488f8449ff7fbe9ddf2945aaa661942945f

Observation 69430c70-2470-49a4-a338-8f4d8ac09e5d · outbound

This paper cites Con- cept2robot: Learning manipulation concepts from instructions and hu- man demonstrations,.

ACTLLM: Action Consistency Tuned Large Language Model Con- cept2robot: Learning manipulation concepts from instructions and hu- man demonstrations,

Reference 27

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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=2026-08-06T22:34:08.183203Z digest=sha256:165b67cafe50ad8a39d9995e8eb699785c512d55d7b0a27d13a0cae5e92ee633

Observation 5db86166-f70a-4351-a54b-f96026248fd2 · outbound

This paper cites Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction.

ACTLLM: Action Consistency Tuned Large Language Model Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction

Reference 28

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local_arxiv, observed 2026-08-06T22:34:10.290123Z

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=2026-08-06T22:34:08.242765Z digest=sha256:b3ab033cad3bcdfc87dd397a74aa53e652e4a3a088209a0837ebf023112760fc

Observation 76a0802b-89db-4652-b82f-0dc2b57f31ce · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model Cliport: What and where pathways for robotic manipulation,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:13.090031Z

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=2026-08-06T22:34:08.315101Z digest=sha256:79271c1fe41c1416cdf88ca660ff81af7bd31e3c5b2486755eb1dd6dc87cd53e

Observation 5ed296b4-ce06-4836-b864-0cbc8bc4c299 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

ACTLLM: Action Consistency Tuned Large Language Model Learning transferable visual models from natural language supervision,

Reference 30

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raw_fallback, observed 2026-08-06T22:34:12.893955Z

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=2026-08-06T22:34:08.389978Z digest=sha256:54000d8e6ce204038a4c2b6f6efc351a1e34fc03d0e3143b609d3f5ea1e522e9

Observation 629083fa-5f3e-4f0f-a7a1-7a00f417a996 · outbound

This paper cites Perceiver-actor: A multi- task transformer for robotic manipulation,.

ACTLLM: Action Consistency Tuned Large Language Model Perceiver-actor: A multi- task transformer for robotic manipulation,

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:08.445110Z digest=sha256:8a5367434010b400293b68e5a6e3dc9207629f0a9db0d4746ba7bf7030eb1d64

Observation 9e0b2ec6-732b-4b2d-996a-2dd8b497a725 · outbound

This paper cites Code as Policies: Language Model Programs for Embodied Control.

ACTLLM: Action Consistency Tuned Large Language Model Code as Policies: Language Model Programs for Embodied Control

Reference 32

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source=pdf_text observed=2026-08-06T22:34:08.491438Z digest=sha256:59389bc5e8b2198dc7493aaf9b61e8b78b81e7fc4d2b29b5123df3d9b62f7f66

Observation 39d54549-877d-4890-b27b-a23541281d64 · outbound

This paper cites Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,.

ACTLLM: Action Consistency Tuned Large Language Model Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,

Reference 33

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source=pdf_text observed=2026-08-06T22:34:08.570986Z digest=sha256:47358df4db9162599f5dc329cf4837c4e982ff117eec206f729e47b832139009

Observation e7bdd877-3203-4ba2-8011-f48709c8946e · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model Inner Monologue: Embodied Reasoning through Planning with Language Models

Reference 34

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source=pdf_text observed=2026-08-06T22:34:08.623286Z digest=sha256:edb99aafe4846d0efe3cad7132cbc77bb5b743455baf48cfe9950663c14fe98f

Observation 7a3d6bcb-d183-48ad-9c48-f636672f5170 · outbound

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

ACTLLM: Action Consistency Tuned Large Language Model Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 35

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no resolver link, observed 2026-08-06T22:34:08.700073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:08.700073Z digest=sha256:37d7344a879071e1a3c4c9ffd2ca8f1896d23153cf849cd9083eace1f1c2af90

Observation 98caaa48-0b48-4956-8934-ec621caa5113 · outbound

This paper cites Learning language-conditioned robot behavior from offline data and crowd- sourced annotation,.

ACTLLM: Action Consistency Tuned Large Language Model Learning language-conditioned robot behavior from offline data and crowd- sourced annotation,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:12.738882Z

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=2026-08-06T22:34:08.802322Z digest=sha256:d9a3c8a99ce5c16c1d3b1cb0747e141472a0cb8022c4b8509a15199868c95025

Observation a4496016-f95f-41f5-a63e-85f26d224c9e · outbound

This paper cites Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language.

ACTLLM: Action Consistency Tuned Large Language Model Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Reference 37

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unresolved
no resolver link, observed 2026-08-06T22:34:08.897233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:08.897233Z digest=sha256:1086b18ca4d61f7371933796f8e5dceb95dc1e7a88a1f9c1a487f65eb93089c3

Observation 0e6cf36c-3d6b-4d3e-a5a5-6284597625a8 · outbound

This paper cites Reward Design with Language Models.

ACTLLM: Action Consistency Tuned Large Language Model Reward Design with Language Models

Reference 38

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unresolved
no resolver link, observed 2026-08-06T22:34:08.994748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:08.994748Z digest=sha256:d82c6b39f830517665f8d4ce24731820a2f5b4f8491dc23601f624c1f3a75719

Observation ac083bd4-4bbf-4156-aa82-4113d1a0b470 · outbound

This paper cites A survey of imitation learning: Algorithms, recent developments, and challenges,.

ACTLLM: Action Consistency Tuned Large Language Model A survey of imitation learning: Algorithms, recent developments, and challenges,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:12.353488Z

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=2026-08-06T22:34:09.096516Z digest=sha256:490b5714795db891cd5a20e6ff81dbc2979c0cfadbcc77ef0ec71e15aa8e48fb

Observation f159e39c-3ec3-4fb5-8da2-a11a81cba9b0 · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

ACTLLM: Action Consistency Tuned Large Language Model Parameter-efficient transfer learning for nlp,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:11.702612Z

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=2026-08-06T22:34:09.163012Z digest=sha256:6802add1fea96af5427d764bbaee23a33115fa21df112d2e9bc76d74579e4954

Observation 91df2c20-0ade-4021-954d-30573be608b3 · outbound

This paper cites Grounded language-image pre-training,.

ACTLLM: Action Consistency Tuned Large Language Model Grounded language-image pre-training,

Reference 41

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unresolved
no resolver link, observed 2026-08-06T22:34:09.262602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:09.262602Z digest=sha256:943acfac5034ca8f77f62778430e221fc51665586b612ad959f1137900dd7b25

Observation ae8e1eb1-c7d6-4867-be38-d6574c958233 · outbound

This paper cites Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt quality,.

ACTLLM: Action Consistency Tuned Large Language Model Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt quality,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:11.453831Z

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=2026-08-06T22:34:09.373504Z digest=sha256:57a35bcd202b1ad1ccc65f3836f2e3c87fcfb712bdd308411249a8ed4cb5941f

Observation b199ac2d-0a86-4f1d-9bec-73bebedc31fd · outbound

This paper cites What Makes for Good Visual Tokenizers for Large Language Models?.

ACTLLM: Action Consistency Tuned Large Language Model What Makes for Good Visual Tokenizers for Large Language Models?

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T22:34:09.441859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:09.441859Z digest=sha256:67133417dddd8fea5300a358bbeae81d3ef3852436ce744053c4a2dc3eb74e4b

Observation 8b9eefd8-ea96-4443-a648-c260ecad85da · outbound

This paper cites The Llama 3 Herd of Models.

ACTLLM: Action Consistency Tuned Large Language Model The Llama 3 Herd of Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T22:34:09.557338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:34:09.557338Z digest=sha256:93f768f8319f79b4dc9ec869bb5d597e5f9e068d1deb4a6d25458d2bb3466a32

Observation f8c97811-981f-4e8f-9488-d5cede496671 · outbound

This paper cites Mdetr-modulated detection for end-to-end multi-modal understand- ing,.

ACTLLM: Action Consistency Tuned Large Language Model Mdetr-modulated detection for end-to-end multi-modal understand- ing,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:11.303393Z

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=2026-08-06T22:34:09.658313Z digest=sha256:89a71e181214b906120fe272c5a6761f3e36a9d2befe846ed05a0327e515a1a7

Observation 934363c5-c57c-40c3-832c-f25e8a42ba4a · outbound

This paper cites Learning to augment synthetic images for sim2real policy transfer,.

ACTLLM: Action Consistency Tuned Large Language Model Learning to augment synthetic images for sim2real policy transfer,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:11.081138Z

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=2026-08-06T22:34:09.745308Z digest=sha256:74bda1b6c8e913187562cb95e36a99ebe318bcc3c3c6caa467a8af8233587f98

Observation 079f6813-fe5f-44a9-a0ba-f6a69c21b92a · outbound

This paper cites A generalist agent,.

ACTLLM: Action Consistency Tuned Large Language Model A generalist agent,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:10.851856Z

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=2026-08-06T22:34:09.851168Z digest=sha256:14a59ac027433742a53eb5b358f4ce472cc3745d862efa4e92d77d6f6213482e

Observation f998f949-044c-4477-8535-9fa6b4067564 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

ACTLLM: Action Consistency Tuned Large Language Model Flamingo: a visual language model for few-shot learning,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:34:10.584680Z

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=2026-08-06T22:34:09.950077Z digest=sha256:329572da9e4d6edd1ac4059093e33abbdaebfcfbb93d7cefd669020393c6e3a2

Pith citing papers

No inbound Pith citation observations are available.