Paper Citation Record · LEDGER

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts

As of 3 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2605.06175.

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

pith.paper-citation-record.v1
2605.06175 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:36:25.966163Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact16
verified fuzzy2
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch16

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 62e98d9a-2e63-46dc-b2c7-4d34498c3ac3 · outbound

This paper cites Qwen3-VL Technical Report.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Qwen3-VL Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-11T04:45:59.171303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 49a304cc-10c0-4f43-aa55-a318157bded5 · outbound

This paper cites Qwen3-VL Technical Report.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Qwen3-VL Technical Report

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T01:05:49.969337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e9f0f5bd-c9ca-4780-860a-0b09e710712a · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T01:05:49.974895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e1a92389-09b8-41e8-a11c-54c9918d5164 · outbound

This paper cites UniVLA: Learning to Act Anywhere with Task-centric Latent Actions.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T15:28:07.265740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8f0c9af0-7db2-4619-b826-70f52390f731 · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts WorldVLA: Towards Autoregressive Action World Model

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T22:57:08.352786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 208a1b0b-7f69-4265-93f1-6e5ec990624c · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 6

Resolution: verified exact; doi, observed 2026-05-11T01:05:49.961182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7f40815d-92de-4c25-8d1a-7d9a32e52d36 · outbound

This paper cites StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T04:45:59.350356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation efbdb5b4-7b52-47b2-a260-236ef9541c4e · outbound

This paper cites ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:59.368359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation db17cc31-403e-459d-a61c-099224faacee · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:40:26.497476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d1592a18-e81f-4b59-9e4a-41fb09be0a70 · outbound

This paper cites Foundation models in robotics: Applications , challenges, and the future.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Foundation models in robotics: Applications , challenges, and the future

Reference 10

Resolution: metadata mismatch; doi, observed 2026-05-11T01:05:49.984664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 09cda666-603a-4a54-a857-136d45b8526c · outbound

This paper cites Gemma 3 Technical Report.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Gemma 3 Technical Report

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-11T04:45:59.188692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7ec7ce8e-5642-46d4-a007-b9b9a70229ee · outbound

This paper cites Hilbert’s sixth problem: derivation of fluid equations via Boltzmann’s kinetic theory.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Hilbert’s sixth problem: derivation of fluid equations via Boltzmann’s kinetic theory

Reference 12

Resolution: verified exact; doi, observed 2026-05-11T01:05:49.958238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4a932b17-8c9c-414f-9f50-cdc89a5363db · outbound

This paper cites ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:45:59.407024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 529dbde5-179c-4eb2-b129-9bafb15acb07 · outbound

This paper cites NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T15:53:29.501619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dae0e16e-215d-4ade-b1a9-cc77904b96a2 · outbound

This paper cites AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T04:45:59.291929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ff874070-5842-4c17-a118-21f6a57be2c6 · outbound

This paper cites A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T22:05:50.945454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a1fe5c0a-d7f1-41fe-87b5-ea6f9a8aedd0 · outbound

This paper cites Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T04:45:59.299721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4e9fa900-ef4b-4d4c-a0e8-9e2c879e885d · outbound

This paper cites MolmoAct: Action Reasoning Models that can Reason in Space.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts MolmoAct: Action Reasoning Models that can Reason in Space

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:35:23.409560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 909513c3-68d7-4d84-a971-79577968e705 · outbound

This paper cites What Matters in Building Vision-Language-Action Models for Generalist Robots.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts What Matters in Building Vision-Language-Action Models for Generalist Robots

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:37:50.975242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 53aa8d41-8435-466f-a449-a3fd8343e5ae · outbound

This paper cites Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T00:20:26.976115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 776adc76-6661-485f-8c5b-11602b3ce823 · outbound

This paper cites A Survey on Vision-Language-Action Models for Embodied AI.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts A Survey on Vision-Language-Action Models for Embodied AI

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-11T04:45:59.316405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6a47af49-27f7-465f-a746-108c770a0c6b · outbound

This paper cites URL https://proceedings.neurips.cc/paper_files/ paper/2024/hash/db36f4d603cc9e3a2a5e10b93e6428f2-Abstract-Conference.html.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts URL https://proceedings.neurips.cc/paper_files/ paper/2024/hash/db36f4d603cc9e3a2a5e10b93e6428f2-Abstract-Conference.html

Reference 22

Resolution: verified exact; doi, observed 2026-05-11T01:05:49.993497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4d9117d0-372c-45e9-9846-b4206cf66fc7 · outbound

This paper cites arXiv preprint arXiv:2512.11921 , year =.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts arXiv preprint arXiv:2512.11921 , year =

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:45:59.255911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2498e2be-7397-422c-856b-0d6b299ca27f · outbound

This paper cites FAST: Efficient Action Tokenization for Vision-Language-Action Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T08:52:32.433811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ea2d567c-9972-4f73-b34d-4fbfe8a856cc · outbound

This paper cites Interactive Post-Training for Vision-Language-Action Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Interactive Post-Training for Vision-Language-Action Models

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T14:25:47.344448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 43185d61-e56a-4798-8867-cd2c1e22271f · outbound

This paper cites KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:59.323784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5852c08a-60e9-48b4-9b5c-3f30dc081e8d · outbound

This paper cites doi: 10.18653/v1/2025.naacl-long.248.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts doi: 10.18653/v1/2025.naacl-long.248

Reference 28

Resolution: verified exact; doi, observed 2026-05-11T01:05:49.977912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 54fbf5b6-c48d-4a25-815b-fedfe200d0ce · outbound

This paper cites VLANeXt: Recipes for Building Strong VLA Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts VLANeXt: Recipes for Building Strong VLA Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-21T02:04:11.439936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ab59e0c9-a1ef-4a60-b9e2-2a4057c1c88c · outbound

This paper cites Instructvla: Vision-language-action instruction tuning from understanding to manipulation.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Instructvla: Vision-language-action instruction tuning from understanding to manipulation

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:59.236832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ca2170db-7722-4fea-a936-6214e2a6f4fd · outbound

This paper cites ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-11T04:45:59.206389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 29af73d3-0458-4317-b699-06e6716c5e65 · outbound

This paper cites Twinbrainvla: Un- leashing the potential of generalist vlms for embodied tasks via asymmetric mixture-of-transformers.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Twinbrainvla: Un- leashing the potential of generalist vlms for embodied tasks via asymmetric mixture-of-transformers

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:59.195801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f89a8cae-51f2-4740-ba52-ea7eb7cd0256 · outbound

This paper cites VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-06-02T02:03:37.239527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 0bd4786d-5963-4a28-8427-4e38cb15e77c · outbound

This paper cites X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T14:57:47.957311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b3049d91-f57b-49c7-9b09-f33f26c79e95 · outbound

This paper cites an unresolved cited work.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-05-16T00:20:26.969210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3a92e11a-307a-40a9-a2f5-4d6832b68086 · outbound

This paper cites an unresolved cited work.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-05-16T00:20:26.965906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 50d1fda1-28cd-4ccf-b552-d9afd0ba1b17 · outbound

This paper cites Left Brain.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts Left Brain

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T00:20:26.972972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

Observation 62d87d70-948a-4b0b-b1d6-a84fc2be323d · inbound

VistaVLA: Geometry- and Semantic-Aware 3D Gaussian-Grounded VLA for Robotic Manipulation cites this paper.

VistaVLA: Geometry- and Semantic-Aware 3D Gaussian-Grounded VLA for Robotic Manipulation VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T06:36:25.966163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.