arXiv AI June 2, 2026 By Sherzod Hakimov, Mattia D'Agostini, Ivan Samodelkin, David Schlangen

The Image Reconstruction Game: Drawing Common Ground Through Iterative Multimodal Dialogue

arXiv:2606. 01901v1 Announce Type: cross Abstract: We introduce the Image Reconstruction Game, a fully automated benchmark in which a vision-language model issues corrective instructions to an image generator across multiple turns, making accumulated common ground directly observable as a rendered image.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 4

FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration

arXiv:2608. 00111v1 Announce Type: cross Abstract: Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference.

By Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe, Jung-Hsien Chiang

diffusion safety

arXiv AI

Jul 21

Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models

arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.

By Junhao Liu, Jian-Wei Zhang, Tao Huang, Miles Yang, Zhao Zhong, Liefeng Bo

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 10

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.

By Zhengqin Li, Cheng Zhang, Jakob Engel, Zhao Dong

llms benchmarks

arXiv Computer Vision

Sep 2

Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures

arXiv:2609.00663v1 Announce Type: new Abstract: Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separatin...

By Can Polat, Mustafa Kurban, Erchin Serpedin, Hasan Kurban

llms multimodal benchmarks

arXiv Computer Vision

Aug 25

FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors

arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...

By Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan

diffusion reinforcement-learning fine-tuning

arXiv AI

Jun 26

Reconstruction Alignment Improves Unified Multimodal Models

arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.

By Ji Xie, Trevor Darrell, Luke Zettlemoyer, XuDong Wang

rag diffusion multimodal benchmarks safety

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Related stories

FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration

Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures

FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors

Reconstruction Alignment Improves Unified Multimodal Models