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Related Articles from SNS
Humans' ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model Annotations for Agent Collaboration
arXiv:2606.06388v1 Announce Type: new Abstract: Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as human collaborators. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning,partners' intentions, and shared goals during the collaborative process. Today's agents rarely develop such capabilities since...
Humans' ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model Annotations for Agent Collaboration
arXiv:2606.06388v2 Announce Type: replace Abstract: Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as human collaborators. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning,partners' intentions, and shared goals during the collaborative process. Today's agents rarely develop such capabilities...
CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement
arXiv:2606.05793v1 Announce Type: new Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conversation-level collaborative studies lack grounded interaction and behavioral execution, motivating the need for cooperative game environments that enable contextualized and immersive collaboration. To this end, this paper proposes CollabBench, a benchmark for evaluating and training collaborative...
Bilateral and multilateral international scientific collaboration of EU member states: OpenAlex vs Scopus (2000-2024)
arXiv:2606.06336v1 Announce Type: new Abstract: This study examines the evolution of bilateral and multilateral scientific collaboration among EU Member States and between the EU and global partners from 2000 to 2024 using data from OpenAlex and Scopus. The results show that OpenAlex, when restricted to cited articles, yields findings broadly comparable to those obtained from Scopus for assessing country-level research collaboration. Relative Intensity of Collaboration (RIC) values are...
Evolution of bilateral and multilateral collaboration of EU-14 countries across disciplines, 2010-2024
Announce Type: new Abstract: This study explores the evolution of bilateral and multilateral research collaboration of nine EU-14 member states, both within Europe and globally, across six disciplines, between 2010 and 2024, using OpenAlex data. Results indicate that bilateral collaboration rates remained relatively stable and predominantly concentrated within EU-14 countries, followed by the USA, the UK, and China. Multilateral collaboration rates increased significantly across all...
INTACT: Ego-Guided Typed Sparse Evidence Retrieval for Heterogeneous Collaborative Perception
arXiv:2606.04437v1 Announce Type: new Abstract: Collaborative perception extends the perceptual range of autonomous vehicles by sharing information across agents, but heterogeneous sensors and perception models make intermediate feature fusion difficult to deploy at scale. Existing heterogeneous collaboration methods typically follow a translation-first paradigm: collaborator features must be aligned, adapted, or projected into an ego-compatible space before fusion. Such...
HeedVision: Attention Awareness in Collaborative Immersive Analytics Environments
arXiv:2505.07069v3 Announce Type: replace Abstract: Group awareness--the ability to perceive the activities of collaborators in a shared space--is a vital mechanism to support effective coordination and joint data analysis in collaborative visualization. We introduce collaborative attention-aware visualizations (CAAVs) that track, record, and revisualize the collective attention of multiple users over time. We implement this concept in HeedVision, a standards-compliant WebXR system built...
Incentivized Collaboration in Active Learning
arXiv:2311.00260v2 Announce Type: replace Abstract: In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for incentivized collaboration. Here, rational agents aim to obtain labels for their data sets while keeping label complexity at a minimum. We focus on designing (strict) individually rational (IR) collaboration protocols, ensuring that agents cannot reduce their expected label complexity by acting...
SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
arXiv:2606.04514v1 Announce Type: new Abstract: Recent LLM-based recommenders enhance language models with collaborative embeddings from user-item interactions, but making such embeddings available does not ensure their proper use during inference. Through a diagnostic attention analysis, we find that the utilization of collaborative embeddings is depth-dependent and alignment-sensitive, suggesting that LLMs need to balance their internal semantic knowledge with external collaborative...
Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
arXiv:2508.10312v2 Announce Type: replace Abstract: Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate...