Qijian Zheng, Wanqing Lin, Yuanfang Wang, Yijun Ge, Jordan Bate, Katie Aafjes-van Doorn, Feng Liu
Facilitative Interpersonal Skills (FIS) is a performance-based assessment method that provides a structured framework for evaluating the interpersonal competencies of mental health professionals. However, existing FIS assessment relies heavily on human observer ratings, making the evaluation process labor-intensive, costly, and difficult to scale. Progress toward automated FIS assessment has also been constrained by the lack of publicly available professional data in psychotherapy settings. To address this limitation, we introduce Therapy-FIS, a dataset for automated FIS assessment in standardized psychotherapy tasks. To our knowledge, Therapy-FIS is the first publicly available dataset in this domain. It contains facial, acoustic, and textual signals derived from standardized therapist responses to standardized mock patient stimulus clips, based on 890 therapist video recordings and 8 patient videos, and is rated with eight FIS subscale ratings and an overall FIS score. Built on Therapy-FIS, we further establish a benchmark with two downstream tasks: Therapist Performance Rating and Joint Therapist--mock-Patient Performance Rating. We also introduce a competitive baseline, DyadicFIS, with gated fusion and routed readout under therapist-only and dyadic settings. DyadicFIS obtains the best macro CCC(Concordance Correlation Coefficient) scores on both tasks (0.2807 and 0.3299). Therapy-FIS offers a reproducible resource for data-driven automated assessment of therapeutic interpersonal skills in mental health.
Sahel Sharifymoghaddam, Lingwei Gu, Yijun Ge, Jimmy Lin
Evaluating advances in agentic search requires testing whether methods generalize across tasks and corpus scale. Changing web search services impede controlled comparisons, while small corpora assembled around benchmark questions provide a limited test of generalization. We introduce CMASS, the ClimbMix Agentic Search Suite, which projects three search benchmarks---BrowseComp-Plus, Total Recall QA, and DeepSearchQA---and the multi-hop question-answering dataset HotpotQA onto a shared, fixed corpus. The target, ClimbMix, is a pre-training corpus of 553M documents and approximately 400B tokens, assembled independently of the benchmark questions. Our projection pipeline verifies grounding, answer uniqueness, and set completeness through independent agent audits, controlled revision, and human review. The 423 questions span entity identification, multi-hop reasoning, exhaustive answer sets, and aggregation. Evaluations of three models expose substantial headroom for open-weight agents and show that high answer accuracy can coexist with incomplete evidence coverage. By enabling measurement of accuracy alongside evidence coverage, search effort, and closed-book performance, CMASS supports developing agents that find sufficient evidence and produce complete answers efficiently. The pipeline code and dataset are available at https://cmass.tech
Yijun Ge University of Waterloo, Kushaldeep Mujral, Karthik Nambiar, Jimmy Lin University of Waterloo
We present rosaOS, an open-source agentic operating system for embodied LLMs: interactive, LLM-driven agents coordinate various software tools and physical devices through a desktop companion, the Reachy Mini robot. Existing LLM–robotic systems are generally built as a tight, intertwined stack, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration. Our system aims to provide a classic OS-inspired architecture where an agentic kernel manages all task execution and mediates device access, while process agents invoke tools to perform actions. We adopt industry-standard interfaces with MCP for agentic tool-calling and ROS for robot interactions, and demonstrate rosaOS on a multi-device setup including a quadruped robot, a wheeled mobile robot, and a smart lamp, all controlled through interactions with the Reachy Mini. By incorporating MCP extensibility with ROS hardware interoperability, rosaOS enables a plug-and-play ecosystem for building embodied agentic systems. Our OS is available at rosaos.ai.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表