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Contextuality and Contradictions: Exploring Real-World Identifications of AI-Generated Writing

arXiv:2611.08704v1 Announce Type: new Abstract: As AI-generated content spreads into nearly every domain of human interaction, practices and discourses of AI detection, both formal and informal, have become an everyday part of how people interact with text and with each other. In this paper, we explore the real-world practices and discourses of AI identification that have emerged in response to a shifting discursive landscape where the human provenance of a text can no longer be taken for granted. We pair qualitative coding with computational analysis of lexical change, grounded in linguistic anthropological perspectives on language and communication, to investigate AI identifications among English Wikipedia editors and students. We find that these identifications draw on a diversity of evidentiary types, from discrete textual features and judgments about content to circular sense-making claims and presumptions about the author's moral character, and that they are contradictory, dynamic, and contextually dependent. In practice, what often motivates an AI identification is not the presence of any specific textual element, but pragmatic infelicity: a perceived mismatch between the elements present and those deemed appropriate within the text's particular context. Consequently, real-world identifications of AI-generated writing do not merely neutrally point out the perceived presence of "AI-like" features, but can be leveraged as a discursive resource to maintain and define sociolinguistic norms: what can be said in a given context, what is appropriate, and, ultimately, what is human. Further, the design and implementation of systems and tools that treat AI-likeness as a context-free property of text may therefore inherit, and enforce at scale, the norms of whichever contexts they were trained on.

Is Token Confidence Just Chain Length? A Signal-Processing Audit of Reasoning Traces

arXiv:2610.36709v1 Announce Type: new Abstract: Reasoning language models answer math problems by writing long chains of thought, and sampling many chains per problem and taking a majority vote raises accuracy at a large cost in generated text. A popular way to cut that cost scores each chain by the model's confidence in each token it writes, where a token is a word or part of a word. The method averages this confidence over a sliding window of 2048 tokens and keeps or stops chains based on the lowest average. We treat the confidence, recorded token by token, as a signal in the sense of audio or radio, and examine the method with standard signal-processing tools such as spectral analysis and statistical tests for detecting a sudden change. We sampled 16 chains for each of 500 problems from

Conceptizer: Next-Token Prediction Is Not All You Need

arXiv:2610.33027v1 Announce Type: new Abstract: Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perp

SAGE Benchmark and GAR Framework Released: Advancing Multimodal Semantic Alignment Evaluation

On October 26, 2026, arXiv published the paper arXiv:2610.31838v1, introducing Sense-Aligned Grounding Evaluation (SAGE)—a unified 10-candidate image-selection benchmark—along with the training‑agnostic Gloss-Augmented Reasoning (GAR) framework, designed for cross‑noun, verb, and entity‑centered multimodal grounding evaluation.

Why it matters：This study is the first to unify three types of tasks—VWSD, VerSe, and WikiDiverse—under a term-centric grounded evaluation protocol, introducing a mechanism for identifying hard negative samples and context‑exchange diagnostics. Empirical results demonstrate that the synergistic effect of definition enhancement and constraint‑guided reasoning can significantly improve the Hits@1 metric (from 65.90 to 71.22), thereby providing a more fine-grained, interpretable new evaluation paradigm for multimodal semantic alignment.

Latent Coherence: Do Frontier Language Models' Probability Judgments Obey the Law of Total Probability?

arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments satisfy the law of total probability, $P(X)=\sum_z P(z)P(X|z)$, when the latent $z$ must be inferred from records in the context? In separate single-turn calls, models are asked for $P(X)$, for $P(z)$ over the latent's states, and for $P(X|z)$; a causal family applies the same test to $P(y|do(x))$ and the back-door adjustment. Every query has an exact ground-truth probability, and a subset of items is asked in three wordings to separate