Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with both human and artificial agents. These interactions represent a critical frontier for LLM-based agents, yet existing evaluation methods fail to measure how well these capabilities generalize to novel social situations. In this paper, we introduce a method for evaluating the ability of LLM-based agents to cooperate in zero-shot, mixed-motive environments using Concordia, a natural language multi-agent simulation environment. Our method measures general cooperative intelligence by testing an agent's ability to identify and exploit opportunities for mutual gain across diverse partners and contexts. We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains across a suite of diverse scenarios ranging from negotiation to collective action problems. Our findings reveal significant gaps between current agent capabilities and the robust generalization required for reliable cooperation, particularly in scenarios demanding persuasion and norm enforcement.

In this paper, we are studying the problem of recognizing attribute-object pairs that do not appear in the training dataset, which is called unseen attribute-object pair recognition. Existing methods mainly learn a discriminative classifier or compose multiple classifiers to tackle this problem, which exhibit poor performance for unseen pairs. The key reasons for this failure are 1) they have not learned an intrinsic attributeobject representation, and 2) the attribute and object are processed either separately or equally so that the inner relation between the attribute and object has not been explored. To explore the inner relation of attribute and object as well as the intrinsic attribute-object representation, we propose a generative model with the encoder-decoder mechanism that bridges visual and linguistic information in a unified end-to-end network. The encoder-decoder mechanism presents the impressive potential to find an intrinsic attribute-object feature representation. In addition, combining visual and linguistic features in a unified model allows to mine the relation of attribute and object. We conducted extensive experiments to compare our method with several state-of-the-art methods on two challenging datasets. The results show that our method outperforms all other methods.

This paper addresses a new problem - jointly inferring human attention, intentions, and tasks from videos. Given an RGB-D video where a human performs a task, we answer three questions simultaneously: 1) where the human is looking - attention prediction; 2) why the human is looking there - intention prediction; and 3) what task the human is performing - task recognition. We propose a hierarchical model of human-attention-object (HAO) which represents tasks, intentions, and attention under a unified framework. A task is represented as sequential intentions which transition to each other. An intention is composed of the human pose, attention, and objects. A beam search algorithm is adopted for inference on the HAO graph to output the attention, intention, and task results. We built a new video dataset of tasks, intentions, and attention. It contains 14 task classes, 70 intention categories, 28 object classes, 809 videos, and approximately 330,000 frames. Experiments show that our approach outperforms existing approaches.

This paper addresses the problem of jointly recognizing object fluents and tasks in egocentric videos. Fluents are the changeable attributes of objects. Tasks are goal-oriented human activities which interact with objects and aim to change some attributes of the objects. The process of executing a task is a process to change the object fluents over time. We propose a hierarchical model to represent tasks as concurrent and sequential object fluents. In a task, different fluents closely interact with each other both in spatial and temporal domains. Given an egocentric video, a beam search algorithm is applied to jointly recognizing the object fluents in each frame, and the task of the entire video. We collected a large scale egocentric video dataset of tasks and fluents. This dataset contains 14 categories of tasks, 25 object classes, 21 categories of object fluents, 809 video sequences, and approximately 333,000 video frames. The experimental results on this dataset prove the strength of our method.

This paper presents a hierarchical composition approach for multi-view object tracking. The key idea is to adaptively exploit multiple cues in both 2D and 3D, e.g., ground occupancy consistency, appearance similarity, motion coherence etc., which are mutually complementary while tracking the humans of interests over time. While feature online selection has been extensively studied in the past literature, it remains unclear how to effectively schedule these cues for the tracking purpose especially when encountering various challenges, e.g. occlusions, conjunctions, and appearance variations. To do so, we propose a hierarchical composition model and re-formulate multi-view multi-object tracking as a problem of compositional structure optimization. We setup a set of composition criteria, each of which corresponds to one particular cue. The hierarchical composition process is pursued by exploiting different criteria, which impose constraints between a graph node and its offsprings in the hierarchy. We learn the composition criteria using MLE on annotated data and efficiently construct the hierarchical graph by an iterative greedy pursuit algorithm. In the experiments, we demonstrate superior performance of our approach on three public datasets, one of which is newly created by us to test various challenges in multi-view multi-object tracking.