arXiv Computer Vision
Vis2State: Visual Reasoning through Executable State Abstractions
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arXiv Computer Vision
Oct 27
VisualCoding: Learning Executable Skills for Reliable Visual Reasoning
arXiv:2610.14403v2 Announce Type: replace Abstract: Learning reusable skills can improve visual reasoning agents, yet applying a skill still requires the agent to implement or invoke a procedure and...
arXiv AI
Oct 12
VETR: Execution-Grounded Self-Improving Agent for Symbolic Graphics Reasoning
VETR (Verified Execution Trajectories for Refinement) is a framework that enables visual agents to recursively improve symbolic graphics reasoning by extracting actionable evidence from execution traces. It retains aligned program/render records and uses an execution refiner to compile observed writes and state changes into constrained alternative programs, which are then validated against captured runtime state. Evaluations on BlenderGym and BlenderBench with GPT‑5.6‑luna and Qwen3.8‑27B demonstrate that VETR consistently outperforms existing refinement strategies such as BlenderAlchemy and VIGA.
arXiv AI
Sep 23
VLM-in-Sandbox: Visual Workspaces for Agentic Visual Reasoning
arXiv:2609.24362v1 Announce Type: new Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language m...
arXiv Machine Learning
Jun 16
REFLEX: Reflective Evolution from LLM Experience
arXiv:2606. 16496v1 Announce Type: cross Abstract: Large multimodal language models (LLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic policies.
arXiv Computer Vision
Sep 22
Visual Graph Reasoning via Knowledge Compilation
arXiv:2609.22327v1 Announce Type: cross Abstract: Visual graph reasoning requires answering graph-theoretic questions directly from graph images, where graph topology and state are conveyed visually...
arXiv Computation and Language
Sep 1
Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence
The paper surveys Multimodal Code Intelligence, focusing on tasks where code is generated, edited, refined, or reasoned about under visually grounded inputs such as screenshots, charts, and videos. It categorizes the field by the role of code—rendered artifact, editable structure, intermediate reasoning trace, or executable tool interface—and organizes benchmarks into four domains: Graphical User Interface, Scientific Visualization, Structured Graphics, and Frontier Tasks and Frameworks. The authors argue that reliable evaluation must include evidence of semantics and interaction beyond visual fidelity, and propose four verification-centered research directions to advance the field toward evidence-grounded executable systems.