arXiv:2601.15224v2 Announce Type: replace-cross Abstract: Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Language Models (VLMs) excel at describing what is visible, it remains unclear whether they can infer how far a task has progressed from partial observations. To this end, we introduce Progress-Bench, a benchmark for systematically evaluating progress reasoning in VLMs. Beyond benchmarking, we further explore a human-inspired two-stage progress reasoning paradigm through both training-free prompting a
The continuous advancements in Vision-Language Models (VLMs) necessitate evaluating their capabilities beyond static image description to more dynamic task understanding, driving the need for PROGRESSLM and Progress-Bench.
A strategic reader should care because improving AI's ability to reason about progress in tasks unlocks more complex automation and better human-AI collaboration, particularly in dynamic environments.
This research introduces a method and benchmark to systematically evaluate and enhance VLMs' understanding of task progression, moving AI closer to true long-horizon reasoning.
- · AI developers
- · Robotics
- · Automation industries
- · AI agents
- · Tasks requiring manual progress monitoring
- · Less advanced VLM architectures
VLMs will become more capable of understanding and predicting the state of ongoing processes.
This improved progress reasoning will enable the deployment of more sophisticated AI assistants and autonomous systems in complex operational settings.
Long-term, this could lead to AI systems that can independently manage and optimize multi-stage projects, significantly increasing productivity across various sectors.
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Read at arXiv cs.CL