arXiv:2604. 20711v2 Announce Type: replace Abstract: Artificial intelligence is increasingly deployed to synthesize large-scale public input in policy consultations and participatory processes.
arXiv:2606. 05256v1 Announce Type: new Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.
arXiv:2607. 01507v1 Announce Type: new Abstract: Empirical research rarely admits a unique analysis.
arXiv:2606. 00005v1 Announce Type: new Abstract: We present the Consilium Protocol, a Byzantine Fault Tolerance-derived architecture for structured multi-model AI deliberation that treats inter-model disagreement as epistemic signal rather than error.
arXiv:2604.13706v2 Announce Type: replace Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reas...
arXiv:2607. 01251v1 Announce Type: cross Abstract: Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight.
arXiv:2606. 29437v1 Announce Type: cross Abstract: The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them?
The paper investigates whether AI evaluators differentiate between an argument’s content and the source attributed to it. Using 2,976 evaluations of six fixed texts across various source attributions, the study finds that the perceived quality of an argument varies with its source, indicating source-position coherence. The authors also note that this pattern holds across topics and model configurations, and that some evaluators explicitly noted mismatches between source and position.
arXiv:2607. 25648v1 Announce Type: cross Abstract: Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources.
The paper argues that modern inference pipelines add an unseen layer of control between a language model’s frozen weights and its output, altering probability distributions before token selection. It introduces the concepts of the Inference Attribution Problem, Probability Placement, and Inference Policy Transparency to describe how such interventions can bias generated language toward specific frames and how these biases cannot be traced solely to model weights. The authors discuss the governance, security, and economic implications of these undisclosed inference policies, referencing EU AI Act, Digital Services Act, and FTC doctrines.
The paper introduces Provenance Density, an interface that visualizes the density of verified claims within a text to counter the Fluency Trap—where users mistake fluent AI-generated hallucinations for truth. In a study with 81 participants, the interface significantly improved users’ ability to distinguish true from fabricated content, while no signal led to no discernment. A technical audit of 200 samples revealed that retrieval density alone is insufficient, and that the Consistency Veto provides most of the discriminative power for dynamic queries.
arXiv:2607. 26512v1 Announce Type: new Abstract: AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them.
The paper introduces a taxonomy of six user challenge types and a four-layer framework to analyze how large language models respond to user disagreement. Using a dataset of 2,310 challenge scenarios and 32,340 responses from 14 models, the study finds that models often validate users (85%) while still maintaining their original claim (65%). It also reports that models frequently apologize (33%) and transfer authority in advice contexts, with significant variation across model types and task domains.