Abstract Large language models (LLMs) excel on many NLP benchmarks, but their behavior on real-world, semi-structured prediction remains underexplored. We present LLAMADRS, a benchmark for structured clinical assessment from dialogue built on the CAMI corpus of psychiatric interviews, comprising 5,804 expert annotations across 541 sessions. We evaluate 25 open-source models (standard and reasoning-augmented; 0.6B–400B parameters) and generate over 400,000 predictions. Our results demonstrate that strong open-source LLMs achieve item-level accuracy with residual error below clinically substantial thresholds. Additionally, an Item-then-Sum (ITS) strategy, assessing symptoms individually through discrete LLM calls before synthesizing final scores, significantly reduces error relative to Direct Total Score (DTS) prediction across most model architectures and scales, despite reasoning models attempting similar decomposition in the reasoning traces of their DTS predictions. In fact, we find that performance gains attributed to “reasoning” depend fundamentally on prompt design: standard models equipped with structured task definitions and examples match reasoning-augmented counterparts. Among the latter, longer reasoning traces correlate with reduced error; while higher model scale does across both architectures. Our results clarify when and why reasoning helps and offer actionable guidance for deploying LLMs in semi-structured clinical assessment.

Generating natural co-speech gestures and facial expressions for effective human-agent interactions requires modeling the intricate interplay between verbal, non-verbal, and contextual cues observed in dyadic human communication. Two types of contextual cues are of particular interest: (1) individual factors of the interlocutors, such as their demographic attributes, and (2) situational factors, like the outcome of a preceding event. To facilitate their study, we introduce the GeSTICS Dataset, a novel multimodal corpus comprising 9,853 questions and 10,460 answers from audiovisual recordings of post-game sports interviews by 147 interviewees. The dataset contains speech data, including textual transcriptions, lexical descriptors, and acoustic features, as well as visual data encompassing the interviewee’s body pose and facial expressions, with an emphasis on capturing these modalities during both the question-listening and answering phases of the interview. Furthermore, GeSTICS incorporates metadata about individual factors, such as the age and cultural background of the interviewees, and situational factors, like the results of the games, which are often overlooked in existing multimodal datasets. Our preliminary analysis of GeSTICS reveals that the effects of speech features, such as loudness and lexical choice, on the production of co-speech gestures in both speaking and listening phases are moderated by situational factors and the interviewee’s individual factors. GeSTICS is designed to enhance the generation of realistic nonverbal behaviors in virtual agents, animated characters, and human-robot interaction systems, thus contributing to more engaging and effective human-agent communication. The analysis code and the dataset are available at https://gestics.github.io.

Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. This will allow human-robot interactions in which language about novel tasks and environments is learned from end-users, reducing dependence on textual inputs and potentially mitigating the effects of demographic bias found in widely available speech recognition systems. We leverage recent work in self-supervised speech representation models and show that learned representations of speech can make language grounding systems more inclusive towards specific groups while maintaining or even increasing general performance.

The rapid urbanization combined with the continuing growth of the world's overall population entails efficient provisioning of services and infrastructure in the cities. Some of the challenges of this gradual shift can be addressed by the global phenomenon of smart cities. Despite various definitions, the concept refers to data-driven solutions to operate cities more efficiently. As the infrastructure requires constant data collection, sensors and various wireless communication technologies are the building blocks of smart cities. In this paper, we consider a smart city application to monitor the road network obtained through one of the volunteered geographic information (VGI) systems. To analyze the impact of the employed wireless communication technology on the network connectivity, we have deployed sensors to the infrastructure according to the obtained geospatial data and varied the transmission range of the sensors. The results show considerable decline in the number of occurred partitions and the minimum number of relay nodes required to ensure network-wide connectivity when the transmission range is increased.