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When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors

Paper recorded by Signals 4 on 2026-10-09 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.CL · 自然语言处理 · first seen 2026-10-12

Abstract

Vision-Language Models (VLMs) increasingly analyze and reason about images, yet it remains unclear whether their internal representations faithfully encode the structured relations required for such reasoning. We introduce EICS (Evaluation-Invariant Concept Stability), a metric for testing whether representations consistently encode concepts under controlled one-concept interventions, and CASP-VL

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#266 most recent of 660 cs.CL papers we have recorded · ↑ newer: RSI-Anything: Unlocking Recursive Self-Improvement with World Models · ↓ older: Structural Automata for Neural Semantic Parsing
Cite this page: When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors: the #266 most recent of 660 cs.CL papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/when-vlms-destroy-what-they-know-spatial-representations-fall-below-lexical-prio.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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