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GenFirst proposes a generation-before-reconstruction strategy to enable stable end-to-end training of latent generative models, avoiding latent collapse and achieving state-of-the-art results in image synthesis and multimodal generation.
This paper introduces G-CARL, a grounded checklist-aligned reinforcement learning framework for patient-oriented medical report interpretation, along with the MMedReport benchmark, demonstrating improved factuality and alignment with patient needs.
SPARGen introduces a unified multimodal framework that casts 3D reconstruction, dense correspondence, and spatial reasoning as instruction-conditioned generation tasks, enabling shared spatial representations.
Introduces MCTS-Report, a Monte Carlo Tree Search framework for generating multimodal reports from tabular data, along with the MMRBench benchmark. It outperforms strong baselines across structural completeness, numerical accuracy, chart-text alignment, and insight novelty.
This paper presents SenseNova-Vision, a unified multimodal model that formulates computer vision tasks as generation problems, achieving performance comparable to specialized systems across diverse vision tasks. It introduces a large-scale instruction-response corpus and publicly releases the model and datasets.
PairCoder++ introduces a pair programming paradigm where a Driver agent writes code and a Navigator agent reviews it using toolchain verification, achieving significant improvements across 17 benchmarks for generating charts, scientific figures, CAD models, 3D scenes, and other structured artifacts.
This paper proposes NaviGen, a framework for personalized multimodal content generation that encodes user behavior into executable instructions using a dual identifier and a two-stage SFT+RL pipeline, improving personalization across product, game, and short-video domains.
TIGER is an inference-time framework that mitigates hallucinations in multimodal generation by extracting observation and claim graphs and assigning risk scores to repair unsupported facts. It reduces unsupported content across image-to-text, image+text-to-text, audio-to-text, and video-to-text tasks.
AlphaGRPO is a new framework that applies Group Relative Policy Optimization to Unified Multimodal Models, enhancing generation through self-reflective refinement and decompositional verifiable rewards.
STARFlow2 is a new research paper introducing an architecture that bridges language models and autoregressive normalizing flows for unified multimodal generation. It addresses structural mismatches in existing systems by using a shared causal masking mechanism for interleaved text-image sequences.
MM-WebAgent is a hierarchical agentic framework that generates coherent and visually consistent webpages by coordinating AIGC-based element generation through joint optimization of layout and multimodal content. The paper introduces a benchmark and multi-level evaluation protocol, demonstrating improvements over code-generation and agent-based baselines.