Beyond Naming: Re-benchmarking Open-World Classification with Discrimination

Open-world classification seeks to recognize objects without a predefined candidate set. With MLLMs, this ability is realized and evaluated through free-form naming, based primarily on whether the generated name is correct and sufficiently specific. However, a model may name a category correctly while failing to distinguish it from visually similar classes. We introduce DOWC (Discrimination-Aware Open-World Classification), a benchmark that jointly evaluates naming and discrimination through textual and visual discrimination tasks. Our evaluation reveals that base models exhibit substantially weaker discrimination than naming, particularly in purely visual settings. To address this gap, we propose DBN (Discriminate Before You Name), which introduces an explicit local class comparison before naming. DBN first constructs a comparison neighborhood from self-proposed plausible classes, selects the candidate best supported by the image, and uses this selection to guide naming. We instantiate DBN in two forms: DBN-RL internalizes this process through supervised warm-up and reinforcement learning, with rule-based rewards coupling answer correctness and selection consistency, whereas DBN-TF applies it training-free to frozen models through coarse-to-fine candidate proposal and confidence-based selection. Across five base models, DBN-RL and DBN-TF improve discrimination by +11.0 and +9.4 points and naming by +7.8 and +7.1 points, respectively. These results show that explicit local discrimination provides a stronger basis for open-world recognition.