Taxonomy of conceptual alignment in human-robot dialogue with dialogue act schema
A preprint introduces a taxonomy for characterizing conceptual alignment in human-robot interaction, framing it as a bidirectional, co-constructive process rather than a unidirectional one. The authors define what triggers alignment initiation and what levels of conceptual understanding are involved, and provide a dialogue act schema as an operational tool for analyzing alignment moves. The work aims to give researchers and designers a structured foundation for comparing and building conceptual alignment systems in HRI.
Related guides (1)
Related events (8)
Our approach to alignment research
OpenAI outlines its alignment research strategy, centered on improving AI systems' ability to learn from human feedback and to assist humans in evaluating AI outputs. The stated long-term goal is to build a sufficiently aligned AI system capable of helping solve remaining alignment problems. This represents OpenAI's public framing of its scalable oversight and RLHF-centric research agenda as of mid-2022.
Deliberative Alignment: Reasoning Enables Safer Language Models
OpenAI introduces deliberative alignment, a new alignment strategy applied to o1 models in which the model is directly taught safety specifications and trained to reason over them at inference time. Unlike prior approaches that embed safety implicitly through RLHF, this method makes safety reasoning explicit and inspectable. The announcement positions deliberative alignment as a meaningful advance in scalable oversight and safe deployment of frontier reasoning models.
HACD-H: Formal theory of social intelligence emergence in long-term human-AI interaction
Researchers propose the Human-AI Coevolution Dynamics Framework (HACD-H), a formal dynamical model treating long-term human-AI interaction as a self-organizing social cognitive system. The framework unifies emotional adaptation, relational organization, social memory, and personality consistency, introducing concepts like relational attractors, trust basins, and social cognitive energy. Empirical evaluation on a ~14,700-turn conversational dataset finds that social intelligence correlates negatively with social cognitive energy (r = -0.391) and that interaction trajectories show progressive energy reduction and phase-transition-like developmental patterns. The work argues social intelligence emerges from coevolution over time rather than from isolated conversational capabilities.
RL-based alignment improves interactivity in full-duplex spoken dialogue models
Researchers propose a post-training alignment method using reinforcement learning to improve interactivity in full-duplex spoken dialogue models, which can listen and speak simultaneously. The method addresses four canonical axes of interactivity—pause handling, turn-taking, backchanneling, and user interruption—each with axis-specific reward functions, plus an LLM-based reward to prevent semantic degradation. The approach is applied to two open-source models, Moshi and PersonaPlex, showing consistent improvements in both offline and real-time multi-turn evaluation.
ALMANAC dataset provides action-level mental model annotations for studying human-agent collaboration
Researchers introduce ALMANAC, a dataset of 2,987 collaboration actions drawn from the Map Task dyadic routing paradigm, each annotated with theory-informed mental model labels covering self-reasoning, perceived partner intent, and perceived team goal. The dataset targets a gap in LLM agent training data: current agents are optimized for task completion but lack process-level collaborative competence grounded in mental model alignment. Six LLMs are benchmarked on predicting human next-turn behavior and mental model states. The work provides a resource for evaluating and potentially training agents toward more human-like collaborative reasoning.
CoTrace: A Goal-Level Attribution Framework for Measuring AI Contributions in Human-AI Collaboration
Researchers introduce CoTrace, a framework that decomposes explicit goals into verifiable requirements and traces both direct and indirect AI contributions across dialogue turns in human-AI collaboration. Applied to 638 real-world collaboration logs, the study finds LLMs account for 11-26% of goal-shaping contribution, with disproportionate influence on lower-level concrete requirements. A user study shows that exposing participants to goal-level attribution analyses shifts their perceived AI contribution by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand AI-assisted work. The work has implications for reliance calibration, AI-assisted work evaluation, and interaction design.
What Makes a Dialog Agent Useful?
A Hugging Face blog post from January 2023 examining the properties that make dialog agents useful, likely covering aspects such as instruction-following, helpfulness, and alignment techniques. Published in the context of growing interest in ChatGPT and RLHF-trained conversational models, the post reflects the community's effort to understand and replicate capable dialog systems. As a tier-2 commentary piece, it offers analytical framing rather than new empirical results.
Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning
This paper empirically validates a creative quality metric from a companion work (Calibrated Surprise, Zou & Xu 2026a) under strict low-resource conditions: ~100 expert chain-of-thought annotations and a small base model. The authors introduce Creative Quality Alignment (CQA) as a class of engineering methods and identify a systematic bias in public alignment datasets toward craft knowledge, with weak coverage of audience modeling and reality-logic. A theoretical argument based on 'architectural duality' in single conditional distribution LLMs is offered to explain why so few examples suffice, distinguishing the result from purely empirical findings like LIMA.