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This paper introduces On-Policy Data Evolution (ODE) and a visual-native agent harness to improve multimodal deep search agents. By enabling reusable visual evidence and closed-loop data generation, ODE significantly boosts the performance of Qwen3-VL agents across multiple benchmarks, surpassing Gemini 2.5 Pro.
This paper identifies and formalizes 'recorruption' in multimodal RAG, where adding accurate context causes models to abandon correct predictions due to attentional collapse (visual blindness and positional bias). The authors propose BAIR, a parameter-free inference-time framework that restores visual saliency and penalizes textual distractors, improving reliability across medical, fairness, and geospatial benchmarks.
Introduces ToolsRL, a two-stage reinforcement learning framework that teaches multimodal LLMs to use simple visual tools for complex visual reasoning tasks.
Research shows Chain-of-Thought prompting harms visual-spatial reasoning in multimodal LLMs due to shortcut learning and hallucinating visual details from text alone.
AVR is an adaptive visual reasoning framework that dynamically selects optimal reasoning formats to reduce token usage by 50-90% while maintaining accuracy in visual reasoning tasks. The method addresses reasoning path redundancy by decomposing visual reasoning into three cognitive functions and using FS-GRPO training to encourage efficient format selection.
This paper proposes augmenting visual instruction tuning in multimodal language models with self-supervised tasks expressed as natural language instructions, improving vision-centric reasoning without additional architecture or annotations. By reformulating classical self-supervised pretext tasks as image-instruction-response triplets, the method achieves consistent performance improvements across multiple benchmarks by injecting only 3-10% visually grounded instructions into the training data.
MIT researchers developed VLMFP, a two-stage generative AI approach combining vision-language models with formal planning software to achieve 70% success rate on complex visual planning tasks like robot navigation, nearly 2.3x better than existing baselines. The method automatically translates visual scenarios into planning files that classical solvers can process, enabling effective long-horizon planning in novel environments.
OpenAI releases o3 and o4-mini models that can reason with images in their chain-of-thought process, enabling visual understanding through native image manipulation tools like cropping and zooming without separate specialized models. These models achieve state-of-the-art performance on multimodal benchmarks including STEM questions, chart reading, and visual search tasks.