# Perceptron — SkillRater: Untangling Capabilities in Multimodal Data

- Company: Perceptron (perceptron.inc)
- Announced: 2026-02-13
- Category: not stated
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.perceptron.inc/blog/skillrater
- Record: https://forck.live/items/18617-skillrater-untangling-capabilities-in-multimodal-data
- Subject: Perceptron Isaac / Perceptron Mk1

Decomposing data quality into independent capability dimensions February 13, 2026 SkillRater: Untangling Capabilities in Multimodal Data Decomposing data quality into independent capability dimensions February 13, 2026 SkillRater: Untangling Capabilities in Multimodal Data Decomposing data quality into independent capability dimensions One Score Isn't Enough Data filtering methods typically assign each training sample a single quality score. This assumes quality is one-dimensional. It isn't. When training requires multiple capabilities — visual understanding, OCR, STEM reasoning — a single scorer can't maximize signal for all of them simultaneously. Quality is better understood as multidimensional, with each axis corresponding to a capability the model must acquire. Methods like CLIP score filtering measures image-text alignment but says nothing about reasoning utility. DataRater , a meta-learned scorer, works well for text but underperforms on multimodal data. The reason: the capabilities we care about have near-orthogonal data requirements. Compressing them into one scalar forces tradeoffs. Rater Per Capability …

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