# Perception under Insufficient Evidence

The work defines perception under insufficient evidence, reformulates perception tasks such as grounding, segmentation and counting as an agentic evidence-seeking process, builds two data generation pipelines yielding EviRover-SFT-5K and EviRover-RL-12K plus the human-verified 688-instance EviLens benchmark spanning five perception categories, and shows that a 4B model trained with supervised fine-tuning followed by agentic reinforcement learning improves over its Qwen3-VL-4B-Instruct backbone by 30 points on average on EviLens, reaches performance comparable to advanced proprietary models, and transfers to WebEyes, ReasonSeg, RefCOCOg, MMMU, MMMU-Pro, MathVerse and BrowseComp-VL with a 15-point gain.

The work defines perception under insufficient evidence, reformulates perception tasks such as grounding, segmentation and counting as an agentic evidence-seeking process, builds two data generation pipelines yielding EviRover-SFT-5K and EviRover-RL-12K plus the human-verified 688-instance EviLens benchmark spanning five perception categories, and shows that a 4B model trained with supervised fine-tuning followed by agentic reinforcement learning improves over its Qwen3-VL-4B-Instruct backbone by 30 points on average on EviLens, reaches performance comparable to advanced proprietary models, and transfers to WebEyes, ReasonSeg, RefCOCOg, MMMU, MMMU-Pro, MathVerse and BrowseComp-VL with a 15-point gain.