Reflection with Action-Induced Visual Differences for Desktop GUI Agents

• URL: http://arxiv.org/abs/2608.24015v1
• Date: Tue, 25 Aug 2026 03:10:01 GMT
• Title: Reflection with Action-Induced Visual Differences for Desktop GUI Agents
• Abstract summary: The Planner-Operator-Reflector (POR) framework is widely used in GUI agents to maintain objective alignment in complex tasks.<n>Existing reflectors collapse change detection and outcome verification into one step, leaving evidence implicit and yielding weakly grounded decisions.<n>We propose Evidence-First Reflection (EFR), a two-stage reflector that explicitly decouples action-induced visual differences extraction from outcome verification.
• Score: 29.424541963900598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Planner-Operator-Reflector (POR) framework is widely used in GUI agents to maintain objective alignment in complex tasks through modular collaboration. However, desktop GUIs introduce a key challenge: large, dense interfaces often exhibit subtle or scattered state changes, placing most of the burden on the reflector, which must compare pre- and post-action screens, while the planner and operator reason over a single state. Existing reflectors collapse change detection and outcome verification into one step, leaving evidence implicit and yielding weakly grounded decisions. To address this limitation, we propose Evidence-First Reflection (EFR), a two-stage reflector that explicitly decouples action-induced visual differences extraction from outcome verification. EFR identifies the action location and candidate changed regions with Set-of-Marks annotations, describes and filters action-relevant changes, and makes the final judgment from the cleaned evidence. This evidence-reasoning decoupled design makes reflection better grounded in screen transitions, while reducing both visual search complexity and reasoning burden. Experiments on OSWorld-Verified and WindowsAgentArena demonstrate that EFR improves reflector accuracy by 7.11%, yielding average end-to-end task success gains of 5.94% and 4.95% on the two benchmarks, respectively.

Related papers

• StepReflect: Structured UI Transition Reflection for Mobile GUI Agents [22.922734013100825]
  We propose StepReflect, which formulates per-step GUI reflection as supervised structured prediction conditioned on explicit transition specifications and paired visual evidence.<n> offline, the resulting 8B model achieves 82.16% transition-level accuracy on AndroidWorld, exceeding zero-shot GPT-5.2 by 11.83 percentage points.<n>Online, across M3A, Agent-SAMA, MAI-UI-8B, and Seed-2.0-Pro, StepReflect achieves higher task success in three of four agent configurations and remains within one successful task of the GPT-5.2 Reflection Agent in the fourth.
  arXiv  Detail & Related papers  (2026-08-06T04:17:27Z)
• VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects [6.909117285273132]
  Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.<n>Existing trackers rely on re-identification (re-ID) models trained through single-instance assignment.<n>We propose Video-Level Association re-ID (VLA-ReID), which reformulates re-ID as video-level association modeling.
  arXiv  Detail & Related papers  (2026-07-19T09:27:37Z)
• MMAgent-R$^2$: Learning to Rerank and Reject for Agentic mRAG [50.46218163777298]
  Knowledge-based Visual Question Answering (KB-VQA) requires models to retrieve visual entities matching the query image from large-scale encyclopedic knowledge bases and answer related questions.<n>We propose MMAgent-R$2$, an agentic mRAG framework that integrates visual reranking and active rejection as its internal verification mechanism.
  arXiv  Detail & Related papers  (2026-07-08T13:15:39Z)
• A Task-State Representation for Long-Horizon Mobile GUI Agents [53.359524744953575]
  Task-State Representation (TSR) is a training-free framework that explicitly decouples task state from sensory input.<n> Experiments across four mobile GUI benchmarks validate TSR's effectiveness, yielding up to a 12 absolute point increase in success rate.
  arXiv  Detail & Related papers  (2026-07-01T06:37:21Z)
• Contrastive Reflection for Iterative Prompt Optimization [0.6533652962118279]
  We present Contrastive Reflection, an iterative prompt-optimization framework for agentic IR.<n>We instantiate the framework with a tree-based slice selector, but the contribution is the contrastive loop rather than the tree itself.
  arXiv  Detail & Related papers  (2026-06-29T19:16:07Z)
• AgentV-RL: Scaling Reward Modeling with Agentic Verifier [63.55502685076245]
  Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS)<n>We propose Agentic Verifier, a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process.<n>We show that Agentic Verifier yields consistent performance gains under both parallel and sequential TTS.
  arXiv  Detail & Related papers  (2026-04-17T12:27:36Z)
• MIRROR: Multimodal Iterative Reasoning via Reflection on Visual Regions [42.03378622674476]
  We propose the MIRROR framework for Multimodal Iterative Reasoning via Reflection On visual Regions.<n>By embedding visual reflection as a core mechanism, MIRROR is formulated as a closed-loop process comprising draft, critique, region-based verification, and revision.<n>Experiments on both general vision-language benchmarks and representative vision-language reasoning benchmarks show that MIRROR improves correctness and reduces visual hallucinations.
  arXiv  Detail & Related papers  (2026-02-21T07:56:59Z)
• Agentic Reward Modeling: Verifying GUI Agent via Online Proactive Interaction [7.731207237810125]
  VAGEN is a framework that employs a verifier agent equipped with interaction tools to autonomously plan verification strategies.<n>We show that VAGEN significantly improves evaluation accuracy compared to LLM-as-a-Judge baselines.
  arXiv  Detail & Related papers  (2026-01-31T07:36:54Z)
• VisRAG 2.0: Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation [64.82775032985485]
  Visual retrieval-augmented generation (VRAG) augments vision-language models (VLMs) with external visual knowledge to ground reasoning and reduce hallucinations.<n>Yet current VRAG systems often fail to reliably perceive and integrate evidence across multiple images, leading to weak grounding and erroneous conclusions.<n>We propose EVisRAG, an end-to-end framework that learns to reason with evidence-guided multi-image to address this issue.
  arXiv  Detail & Related papers  (2025-10-10T13:34:23Z)
• Visual-RFT: Visual Reinforcement Fine-Tuning [75.20572976629646]
  Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers.<n>Visual-RFT further extends the application areas of RFT on visual tasks.
  arXiv  Detail & Related papers  (2025-03-03T18:16:32Z)
• From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning [71.41062111470414]
  Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities.<n>We present a novel framework that expands the capability of standard object detectors beyond mere object recognition to complex event understanding.<n>Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training.
  arXiv  Detail & Related papers  (2025-02-09T10:30:54Z)
• Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance [78.34235841168031]
  We present a novel two-stage network with reflection-aware guidance (RAGNet) for single image reflection removal (SIRR) RAG can be used (i) to mitigate the effect of reflection from the observation, and (ii) to generate mask in partial convolution for mitigating the effect of deviating from linear combination hypothesis. Experiments on five commonly used datasets demonstrate the quantitative and qualitative superiority of our RAGNet in comparison to the state-of-the-art SIRR methods.
  arXiv  Detail & Related papers  (2020-12-02T03:14:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.