Paper Citation Record · LEDGER

ProgRM: Build Better GUI Agents with Progress Rewards

As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 5 inbound Pith citation observations for arXiv:2505.18121.

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

pith.paper-citation-record.v1
2505.18121 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:49.267563Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T19:26:37.281563Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:47:09.687885Z

Reference resolution

45 of 45 outbound references displayed

verified exact1
verified fuzzy11
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aec7cd57-76d8-4c11-a7ff-73119782bc71 · outbound

This paper cites Digi-q: Learning vlm q-value functions for training device-control agents.

ProgRM: Build Better GUI Agents with Progress Rewards Digi-q: Learning vlm q-value functions for training device-control agents

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.949072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f7254977-e58d-4cd0-abab-2582825f887b · outbound

This paper cites Digirl: Training in-the-wild device-control agents with autonomous re- inforcement learning.

ProgRM: Build Better GUI Agents with Progress Rewards Digirl: Training in-the-wild device-control agents with autonomous re- inforcement learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.762133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5895103-8609-41bb-847b-5452b5004258 · outbound

This paper cites Learning about progress from experts.

ProgRM: Build Better GUI Agents with Progress Rewards Learning about progress from experts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.581616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 879f22e9-00ab-40d8-9a23-badacf93344a · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

ProgRM: Build Better GUI Agents with Progress Rewards Training Verifiers to Solve Math Word Problems

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.337260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53605823-cbb5-4d82-80f2-6a76092eb65e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ProgRM: Build Better GUI Agents with Progress Rewards DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.419221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bb8ec98-2eac-4dc7-8c12-73a5d9dd3a60 · outbound

This paper cites Dungeons and data: A large-scale nethack dataset.

ProgRM: Build Better GUI Agents with Progress Rewards Dungeons and data: A large-scale nethack dataset

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.400251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e7dc26f3-39d4-42cb-91c6-261d8a91c241 · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

ProgRM: Build Better GUI Agents with Progress Rewards REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.597283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58e481f7-a907-4cbb-9051-e260b61b71d4 · outbound

This paper cites Exploring Expert Failures Improves LLM Agent Tuning.

ProgRM: Build Better GUI Agents with Progress Rewards Exploring Expert Failures Improves LLM Agent Tuning

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:37:50.068075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a4ae53f1-e8c2-4bcc-ac12-def7e240e590 · outbound

This paper cites Making language models better reasoners with step-aware verifier.

ProgRM: Build Better GUI Agents with Progress Rewards Making language models better reasoners with step-aware verifier

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.740939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b426dbb-a6d4-449f-b9d5-68468ac898a7 · outbound

This paper cites Let’s verify step by step.

ProgRM: Build Better GUI Agents with Progress Rewards Let’s verify step by step

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.786546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a6e6515-a8a4-4cac-a2c8-9cca2eecd7b2 · outbound

This paper cites UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning.

ProgRM: Build Better GUI Agents with Progress Rewards UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.862230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50f6f860-4632-4c42-8826-d133a6404fdb · outbound

This paper cites NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild.

ProgRM: Build Better GUI Agents with Progress Rewards NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:45.943894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47146464-9f0a-4fd5-8d7f-bafd72db849c · outbound

This paper cites Xu, Aman Madaan, Jiarui Liu, Robert Lo, Abishek Sridhar, Sudipta Sengupta, Dan Roth, Graham Neubig, and Shuyan Zhou.

ProgRM: Build Better GUI Agents with Progress Rewards Xu, Aman Madaan, Jiarui Liu, Robert Lo, Abishek Sridhar, Sudipta Sengupta, Dan Roth, Graham Neubig, and Shuyan Zhou

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.202532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3f932b9-fe28-43b6-9684-dfa389c944ac · outbound

This paper cites Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents.

ProgRM: Build Better GUI Agents with Progress Rewards Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:46.119179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5b3b549-039f-4284-99eb-97810c3fe91c · outbound

This paper cites Autonomous Evaluation and Refinement of Digital Agents.

ProgRM: Build Better GUI Agents with Progress Rewards Autonomous Evaluation and Refinement of Digital Agents

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:46.228152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4efd62e6-4159-4d0d-b049-b694982913da · outbound

This paper cites WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning.

ProgRM: Build Better GUI Agents with Progress Rewards WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:46.329762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e89bd61-c05a-4608-9851-7de29189cb15 · outbound

This paper cites UI-TARS: Pioneering Automated GUI Interaction with Native Agents.

ProgRM: Build Better GUI Agents with Progress Rewards UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:46.431629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18d80b30-3761-4e5e-b55e-c5e36827d8ce · outbound

This paper cites Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning.

ProgRM: Build Better GUI Agents with Progress Rewards Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:46.502834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4bdfdd7-bfd0-451a-a2a5-ffbd21fb45d1 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert- networks.

ProgRM: Build Better GUI Agents with Progress Rewards Sentence-bert: Sentence embeddings using siamese bert- networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.001358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c464d765-6783-4c58-ba4f-a10440cdcbe1 · outbound

This paper cites Step: Stacked llm policies for web actions.

ProgRM: Build Better GUI Agents with Progress Rewards Step: Stacked llm policies for web actions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.788218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c9cf3a05-12da-493e-98f2-dce10c1ab90d · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

ProgRM: Build Better GUI Agents with Progress Rewards R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.033881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eb32102-56d0-490f-a9e5-92e377d9949e · outbound

This paper cites Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments.

ProgRM: Build Better GUI Agents with Progress Rewards Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.108403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 486f8fcf-9f3c-4375-be1f-f7656a1a18bf · outbound

This paper cites OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis.

ProgRM: Build Better GUI Agents with Progress Rewards OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.185783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6421f46-7dfa-4f7a-8464-a8c8d1c8b002 · outbound

This paper cites Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception.

ProgRM: Build Better GUI Agents with Progress Rewards Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.238146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19a62709-4c66-4c0c-b607-d22e1aa6c55a · outbound

This paper cites Math-shepherd: Verify and reinforce llms step-by-step without human annotations.

ProgRM: Build Better GUI Agents with Progress Rewards Math-shepherd: Verify and reinforce llms step-by-step without human annotations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.380315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 053bc850-e181-465d-8960-9813e0b5f08f · outbound

This paper cites DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents.

ProgRM: Build Better GUI Agents with Progress Rewards DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.485240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8029fb17-4eaa-44da-a0b9-37a8b23ba8c9 · outbound

This paper cites Reinforcing Language Agents via Policy Optimization with Action Decomposition.

ProgRM: Build Better GUI Agents with Progress Rewards Reinforcing Language Agents via Policy Optimization with Action Decomposition

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.590688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3e49663-2092-4537-8873-2260e3312495 · outbound

This paper cites GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents.

ProgRM: Build Better GUI Agents with Progress Rewards GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.700415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19c05f48-6249-4353-acce-6092cd98e096 · outbound

This paper cites Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning.

ProgRM: Build Better GUI Agents with Progress Rewards Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.804784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a6a36ef-b5cf-4007-84c6-d943abba95e4 · outbound

This paper cites Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction.

ProgRM: Build Better GUI Agents with Progress Rewards Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:47.937341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 835c7d55-3d95-4219-9486-da9488b80267 · outbound

This paper cites Agenttrek: Agent trajectory synthesis via guiding replay with web tutorials.

ProgRM: Build Better GUI Agents with Progress Rewards Agenttrek: Agent trajectory synthesis via guiding replay with web tutorials

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.672358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3450faf6-7e13-4dc7-8f4e-c5518ff85d63 · outbound

This paper cites Qwen2.5 Technical Report.

ProgRM: Build Better GUI Agents with Progress Rewards Qwen2.5 Technical Report

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.119240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd0674c3-a3c1-4f74-af92-f26678f2827c · outbound

This paper cites OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning.

ProgRM: Build Better GUI Agents with Progress Rewards OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.213972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 750b3d33-83c5-4606-8ed4-199bcbfa0d59 · outbound

This paper cites Free Process Rewards without Process Labels.

ProgRM: Build Better GUI Agents with Progress Rewards Free Process Rewards without Process Labels

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.320653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dc867a9-8a39-4ef1-ae06-9424c5cdf5d8 · outbound

This paper cites UFO: A UI-Focused Agent for Windows OS Interaction.

ProgRM: Build Better GUI Agents with Progress Rewards UFO: A UI-Focused Agent for Windows OS Interaction

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.529789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8db64b3d-63b0-47ef-b6ef-d92909160a9a · outbound

This paper cites Appagent: Multimodal agents as smartphone users.

ProgRM: Build Better GUI Agents with Progress Rewards Appagent: Multimodal agents as smartphone users

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.664282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d601e41c-ad3e-4be6-92fe-9e5ad8d03326 · outbound

This paper cites Large language models are semi-parametric reinforcement learning agents.

ProgRM: Build Better GUI Agents with Progress Rewards Large language models are semi-parametric reinforcement learning agents

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.549721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 32ad9c9e-7ec1-40c1-99e7-bdf899b9763d · outbound

This paper cites Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction.

ProgRM: Build Better GUI Agents with Progress Rewards Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.872226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 129c17e0-bb24-4cf3-ad7e-bbdcd6ef5525 · outbound

This paper cites The Lessons of Developing Process Reward Models in Mathematical Reasoning.

ProgRM: Build Better GUI Agents with Progress Rewards The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.959454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0dee5fd-9a2b-44d8-a18e-b0cb28957dc1 · outbound

This paper cites Gpt-4v(ision) is a generalist web agent, if grounded.

ProgRM: Build Better GUI Agents with Progress Rewards Gpt-4v(ision) is a generalist web agent, if grounded

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.386446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54802348-9e11-49bd-a93e-166b08e21376 · outbound

This paper cites VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model.

ProgRM: Build Better GUI Agents with Progress Rewards VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:49.160588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 901c9048-5ede-4c28-ae38-b51dac1b22fd · outbound

This paper cites Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents.

ProgRM: Build Better GUI Agents with Progress Rewards Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:49.207033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b897fa9b-2afd-4675-9485-e29701257712 · outbound

This paper cites prototype.

ProgRM: Build Better GUI Agents with Progress Rewards prototype

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.229956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 853b27f3-2c39-475c-a86a-ae09ebfd1b8f · outbound

This paper cites URL https://doi.org/10.18653/v1/D19-1410.

ProgRM: Build Better GUI Agents with Progress Rewards URL https://doi.org/10.18653/v1/D19-1410

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:46.769331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d04d547-2c34-45ac-a980-11f7e7716d76 · outbound

This paper cites Free Process Rewards without Process Labels.

ProgRM: Build Better GUI Agents with Progress Rewards Free Process Rewards without Process Labels

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:48.423228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 32164952-9169-4ee7-af71-03c15a4fad7f · inbound

UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics cites this paper.

UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics ProgRM: Build Better GUI Agents with Progress Rewards

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-16T03:00:31.666131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 81223177-eb8d-47d6-92ed-1976515667bf · inbound

GUI Agents with Reinforcement Learning: Toward Digital Inhabitants cites this paper.

GUI Agents with Reinforcement Learning: Toward Digital Inhabitants ProgRM: Build Better GUI Agents with Progress Rewards

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:31:29.615649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 331eab4f-af11-4384-8355-7588e38095c9 · inbound

Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability cites this paper.

Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability ProgRM: Build Better GUI Agents with Progress Rewards

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:35:56.866360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 106cf729-2ff8-4143-ace8-1a0164978cba · inbound

Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding cites this paper.

Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding ProgRM: Build Better GUI Agents with Progress Rewards

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:32:35.010485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6c8feefd-d674-4c20-a654-fa417d286531 · inbound

StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents cites this paper.

StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents ProgRM: Build Better GUI Agents with Progress Rewards

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:09.689614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.