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arXiv:2607. 12180v1 Announce Type: cross Abstract: An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions.
The paper introduces the Human‑AI Interaction Description Language (HAIDL), a formal model that treats human‑AI interactions as durable data objects rather than fleeting prompt‑response exchanges. HAIDL captures detailed context—including computational environment, user requests, prompts, AI responses, feedback, provenance, and model metadata—using a Backus‑Naur Form‑inspired syntax. The authors implement HAIDL in HAIDL‑X, a JupyterLab middleware that records interactions, builds contextual prompts, interfaces with AI providers, and stores data for replay, analysis, and learning about user behavior.
The paper introduces Test-Time Adaptation through Human‑Agent Interaction (TAHI), a method that uses iterative human feedback to adapt AI agents to individual users’ criteria. By integrating cross‑session interaction data into agent context and weights, and building an evolving rubric module, the authors demonstrate that agents can improve task success by 4.5–20.9% after only a few interactions. The evolving rubric also serves as a scalable annotation tool, detecting 16.0–22.3% more failures than language models or humans alone, and personalized agents can even generalize improvements up to 8.8% across users.
The paper introduces the Core Sentiment Inventory (CSI), a new personality trait evaluation tool for large language models (LLMs) that addresses reliability and validity issues found in existing methods like the Big Five Inventory (BFI). CSI is designed specifically for LLMs, supports both English and Chinese, and provides detailed psychological portraits of model behavior. Experiments show that CSI captures nuanced behavioral patterns, improves reliability, and correlates strongly (above 0.85) with real-world LLM outputs.
Humans possess an innate ability to understand fine-grained interpersonal relationships, which is central to everyday social interactions. Although such reasoning is inherently multimodal, it remains largely unexplored by existing multimodal large language models (MLLMs).
arXiv:2609.07594v1 Announce Type: cross Abstract: Conversational Task Assistants (CTAs) are multimodal dialogue systems that support users in complex real-world tasks such as cooking and DIY through...