{"slug": "lexic-lightweight-eye-tracking-extension-via-injected-complexity", "title": "LEXIC: Lightweight Eye-tracking eXtension via Injected Complexity", "summary": "Researchers introduced LEXIC, a lightweight eye-tracking extension that injects word-level difficulty signals into gaze-only models, achieving AUROC gains of up to +2.9 percentage points on reading comprehension tasks without using language models.", "body_md": "arXiv:2607.08152v1 Announce Type: new\nAbstract: On the recent EyeBench benchmark, predicting reading comprehension from eye movements exposes a stark gap: text-aware models using pretrained language models reach 56--63% AUROC, while gaze-only models operate at chance. We ask how far a gaze-only model can be pushed by lightweight, language-model-free conditioning. Building on the EyeBench AhnCNN baseline, LEXIC-Base, we propose two mechanisms to inject three precomputed word-level difficulty signals, GPT-2 surprisal, word frequency, and word length, into the per-fixation input: direct concatenation, LEXIC-Concat, and a residual mechanism, LEXIC-Res, where a small head predicts typical-reader gaze response and the encoder is conditioned on the deviation. On the OneStop reading comprehension task, with K=5 seed-ensemble training across ten folds, both mechanisms produce statistically consistent AUROC gains on Unseen Text, +1.8 to +2.2 percentage points, Wilcoxon p <= 0.065. LEXIC-Concat additionally lifts Unseen Reader by +2.9 percentage points, p = 0.010. We trace an architectural boundary in LEXIC-Res on Unseen Reader, +1.8 percentage points, p = 0.19, to the prediction head being calibrated to training readers, transferring imperfectly to out-of-distribution readers.", "url": "https://wpnews.pro/news/lexic-lightweight-eye-tracking-extension-via-injected-complexity", "canonical_source": "https://www.machinebrief.com/news/lexic-lightweight-eye-tracking-extension-via-injected-comple-rbmf", "published_at": "2026-07-10 04:00:00+00:00", "updated_at": "2026-07-10 04:20:13.883529+00:00", "lang": "en", "topics": ["machine-learning", "natural-language-processing", "ai-research"], "entities": ["EyeBench", "LEXIC", "GPT-2", "OneStop"], "alternates": {"html": "https://wpnews.pro/news/lexic-lightweight-eye-tracking-extension-via-injected-complexity", "markdown": "https://wpnews.pro/news/lexic-lightweight-eye-tracking-extension-via-injected-complexity.md", "text": "https://wpnews.pro/news/lexic-lightweight-eye-tracking-extension-via-injected-complexity.txt", "jsonld": "https://wpnews.pro/news/lexic-lightweight-eye-tracking-extension-via-injected-complexity.jsonld"}}