Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes
arXiv:2608.11207v1 Announce Type: new Abstract: When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of…
Policy Convergence and Divergence Across National and Within Regional AI Strategies: A Policy Design Element Analysis
arXiv:2608.11006v2 Announce Type: replace-cross Abstract: Governments worldwide have responded to the rapid expansion of AI by publishing national and regional…
Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion
arXiv:2608.11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators…
TELLME: Test-Enhanced Learning for Language Model Enrichment
arXiv:2608.11788v1 Announce Type: cross Abstract: Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large…
Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
arXiv:2608.10810v2 Announce Type: replace-cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through…
Closed-Loop LLM Co-Pilots for Digital Agriculture
arXiv:2608.09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving…
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
arXiv:2608.09548v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators.…
Smart Enough to Go Extinct? An Evolutionary Challenge to the Value of General Intelligence and Its Ethical Implications for AGI
arXiv:2608.10730v1 Announce Type: cross Abstract: The pursuit of artificial general intelligence (AGI) rests on a seemingly self-evident premise: that general…
Optimal Stopping of Self-Refining Foundation Models
arXiv:2608.10729v1 Announce Type: cross Abstract: Foundation models can improve their outputs through a self-refinement process driven by external feedback. In…
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
arXiv:2608.09138v2 Announce Type: replace-cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often…