The paper investigates when an interpretation in generative AI is considered established, arguing that passing local factual checks is insufficient. It introduces three concepts—interpretive appearance, evaluation contract, and standing substitution—to analyze how interpretations gain recognition within sociotechnical processes. The authors propose delayed closure as a practice to keep recognized interpretations revisable and outline five public requirements for transparency, evidence, failure handling, contract revision, and responsibility.
By Deyu Jing
The article presents a minimal working model for large language model (LLM) systems, emphasizing four key distinctions—pretraining vs. deployment, distribution vs. samples, types of memory, and task competence vs. agency. Using this framework, it diagnoses six common misconceptions about LLMs (next‑token prediction, regression to the mean, training‑data regurgitation, model memory, alignment, and understanding), explaining what each misconception captures correctly, where it conflates distinctions, and the implications for evaluation, design, and governance. The model is applied to AI policy language, illustrating how policy can misrepresent these distinctions and offering a diagnostic toolkit to correct such errors.
By Zhicheng Lin
The paper argues that as AI systems increasingly generate code, the bottleneck has shifted to supervising these systems, revealing a vocabulary gap between cybernetic coordination (actions aligning with the world) and epistemic coordination (understanding that can be verified). It critiques current oversight that merely approves outputs, proposing instead that every consequential choice by an agent must include a retrievable condition explaining why it was made, enabling third‑party verification. The authors illustrate this with three delegation episodes, introduce a two‑part reconstruction test, and propose the ORRCF convention to embed such conditions in all recorded decisions.
By J\'er\'emie Lumbroso
arXiv:2608. 05602v1 Announce Type: new Abstract: Generative AI systems are increasingly deployed in high-stakes professional contexts, where their outputs shape what users believe, how they reason, and what they treat as settled.
By Nimisha Karnatak, Max Van Kleek, Nigel Shadbolt
The paper proposes a normative framework for ethical use of large language models (LLMs) in scientific research, treating reasoning as a distributed process where human control remains essential for epistemic legitimacy. It introduces key constructs—content origin, human verification, responsibility assignment, accountable ownership, and epistemic outcome—to separate claim provenance from verification and responsibility. The authors argue that the ethical boundary hinges on adequate verification and accountable human ownership, and they propose an "epistemic audit" to document delegation, verification, provenance, and responsibility for transparent, reviewable AI-assisted reasoning.
By Kalin Stoyanov
arXiv:2603. 28371v2 Announce Type: replace-cross Abstract: When an agent can articulate why something works, we typically take this as evidence of genuine understanding.
By Camilo Chac\'on Sartori
The paper examines how large language model (LLM) outputs are increasingly used in contexts that demand justified interpretations, such as law, education, policy analysis, and public moral debate. It identifies a recurring failure—interpretive misplacement—where model-generated readings are treated as settled meanings without explicit interpretive frames, provenance, or defensible alternatives, leading to accountability loss. Drawing on philosophical hermeneutics, the author proposes design principles for human‑AI co‑interpretation, reorganizes existing LLM techniques into hermeneutically responsible patterns, and discusses implications for legal practice, education, scholarship, and public discourse, while framing digital hermeneutics as a literacy for critically engaging with AI‑mediated texts.
By Behrooz Razeghi
arXiv:2607. 07891v1 Announce Type: cross Abstract: Roy Harris's Integrationist linguistics offers a compelling critique of the referentialist tradition embedded deep at the heart of computational approaches to language, arguing that language is not a code that maps onto a pre-given world but a situated, bipartite activity oriented toward prospective joint action.
By J. Mark Bishop, Stephen J. Cowley
The article discusses the evolution of AI across three generations—from explicit logic to neural networks to large language models (LLMs)—and how LLMs introduce new systemic risks. It applies a forensic‑psychology profiling method to identify ten key features of LLMs, such as hallucinations, bias, and cognitive atrophy, revealing an entity that confabulates, amplifies user biases, and erodes human competence. The report concludes with a four‑pillar framework for AI resilience, emphasizing cognitive sovereignty, measurable control, partial autonomy, and openness to safeguard society.
By Thomas Bartz-Beielstein, Eva Bartz
arXiv:2608. 00961v2 Announce Type: replace-cross Abstract: AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction.
By Donna M Bye, Levin Kuhlmann
arXiv:2607. 01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation.
By Yang Zhao, Yingshuo Li, Zeyu Zhang
The paper defends the 'Whole Hog Thesis', arguing that sophisticated large language models such as ChatGPT are full linguistic and cognitive agents, possessing understanding, beliefs, desires, knowledge, and intentions. It rejects low‑level computational starting points and instead builds its case from high‑level behavioral observations, using Holistic Network Assumptions to link actions to mental states. The authors systematically rebut common objections—such as hallucinations and planning errors—by showing these resemble human fallibility and by challenging the necessity of traditional conditions like embodiment or semantic grounding.
By Herman Cappelen, Josh Dever