Recognizing Is Not Reversing: A Controlled Inversion Test of Fact-Preserving News Framing — Paper

Abstract

Large language models (LLMs) are increasingly used to analyze and rewrite news, yet current framing studies mainly evaluate generation, detection, or whether rewritten text appears more neutral. They do not directly show whether a model can undo a known framing transformation while keeping the facts fixed. We introduce a controlled inversion test over three established textual realizations of framing: evaluative lexis, agency realization, and information salience. Across 60 news articles and three intervention strengths, this yields 540 paired variants with preserved atomic facts and recorded edits. Across Qwen, DeepSeek, and Kimi, factual preservation remains near 0.84, whereas intervention reversal is 0.044--0.068. Even when both framing type and direction are recognized correctly, pooled reversal reaches 0.071. These results reveal a clear separation between factual fidelity, framing recognition, and framing inversion: recognizing how an article is framed does not imply that the framing can be undone.

Authors 1

Yi Liu

Specification

Official page: arxiv.org/abs/2609.11769
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
arXiv id: 2609.11769
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Categories: cs.CL, cs.AI
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
PDF: arxiv.org/pdf/2609.11769
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

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9

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T19

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5 h ago

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Authors: Yi Liu

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
Large language models (LLMs) are increasingly used to analyze and rewrite news, yet current framing studies mainly evaluate generation, detection, or whether rewritten text appears more neutral. They do not directly show whether a model can undo a known framing transformation while keeping the facts fixed. We introduce a controlled inversion test over three established textual realizations of framing: evaluative lexis, agency realization, and information salience. Across 60 news articles and three intervention strengths, this yields 540 paired variants with preserved atomic facts and recorded edits. Across Qwen, DeepSeek, and Kimi, factual preservation remains near 0.84, whereas intervention reversal is 0.044--0.068. Even when both framing type and direction are recognized correctly, pooled reversal reaches 0.071. These results reveal a clear separation between factual fidelity, framing recognition, and framing inversion: recognizing how an article is framed does not imply that the framing can be undone.	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperRecognizing Is Not Reversing: A Controlled Inversion Test of Fact-Preserving News Framing
New paper: Recognizing Is Not Reversing: A Controlled Inversion Test of Fact-Preserving News Framing
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	4 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.