We present a probabilistic intent modeling framework for large language model (LLM) agents in multi-turn social dialogue. The framework maintains a belief distribution over a partner's latent intentions, initialized from contextual priors and dynamically updated through likelihood estimation after each utterance. The evolving distribution provides additional contextual grounding for the policy, enabling adaptive dialogue strategies under uncertainty. Preliminary experiments in the SOTOPIA environment show consistent improvements: the proposed framework increases the Overall score by 9.0% on SOTOPIA-All and 4.1% on SOTOPIA-Hard compared with the Qwen2.5-7B baseline, and slightly surpasses an oracle agent that directly observes partner intentions. These early results suggest that probabilistic intent modeling can contribute to the development of socially intelligent LLM agents.

Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex goal-oriented social dialogues. Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across a variety of agent tasks. Existing DPO-based approaches for multi-turn interactions are divided into turn-level and session-level methods. The turn-level method is overly fine-grained, focusing exclusively on individual turns, while session-level methods are too coarse-grained, often introducing training noise. To address these limitations, we propose Segment-Level Direct Preference Optimization (SDPO), which focuses on specific key segments within interactions to optimize multi-turn agent behavior while minimizing training noise. Evaluations on the SOTOPIA benchmark demonstrate that SDPO-tuned agents consistently outperform both existing DPO-based methods and proprietary LLMs like GPT-4o, underscoring SDPO's potential to advance the social intelligence of LLM-based agents. We release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/SDPO.

Rapid advances in the development and adoption of artificial intelligence (AI) technologies provide new opportunities but also raise fears about disruptive labour market and workplace transitions. This working paper examines how social dialogue can shape the AI transition in beneficial ways for both workers and firms. It highlights that social dialogue can generally help foster inclusive labour markets and ease technological transitions, and presents new descriptive evidence together with ongoing initiatives from social partners showing that social dialogue has an important role to play in the AI transition as well. The paper also discusses how AI adoption may affect social dialogue itself, e.g. by adding new pressures on weakening labour relations systems and posing practical challenges to social partners, such as insufficient AI-related expertise and resources to respond to the AI transition. Based on these insights, the paper suggests a few measures for policy makers who would like to support social partners' efforts in shaping the AI transition.

Launched last November 19, the 5-year LaborIA program, financed by the Ministry of Labor, Employment and Integration and operated by Matrice, an institute for technological and social innovation, has been joined by Inria. Its mission is to " better understand artificial intelligence and its effects on work, employment, skills and social dialogue in order to change business practices and public action . In September, Matrice will begin a survey to better measure the impact of AI in 250 companies as well as field investigations. LaborIA is part of the PMIA initiative, which aims to bridge the gap between AI theory and practice by supporting cutting-edge research and applied activities on AI-related priorities. One of the working groups of this initiative is dedicated to the "Future of Work" theme, it is affiliated with the PMIA Expertise Center in Paris and hosted by INRIA and conducts, among other things, analyses on how AI affects and will affect workers and their environment. According to the OECD, 32% of jobs will be impacted by automation over the next twenty years. "The transformations that our society is undergoing, such as the digital and ecological transitions, have an impact that can be observed concretely in our daily lives.

On November 19, Elisabeth Borne, Minister of Labour, Employment and Integration, visited the Matrice innovation institute to sign an agreement with Bruno Sportisse of Inria to create a laboratory dedicated to artificial intelligence. Called LaborIA and operated by Matrice, this resource and experimentation centre will have the mission of "better understanding artificial intelligence and its effects on work, employment, skills and social dialogue in order to develop business practices and public action". According to the OECD's 2019 Employment Outlook report, medium-skilled jobs are increasingly exposed to profound transformations. Over the next 15 to 20 years, the development of automation could lead to the disappearance of 14% of current jobs, and another 32% are likely to be profoundly transformed. The report states that the future of work is in our hands and will depend, to a large extent, on the public policy choices countries make.