4arXiv cs.AI (Artificial Intelligence)·
Copewell: Multi-agent swarm architecture for equitable mental health support
Researchers present Copewell, a multi-agent swarm system designed to expand mental wellness access, particularly in low- and middle-income countries. The architecture combines multi-source emotional assessment (self-reported, physiological, contextual), valence-arousal emotion routing via Russell's Circumplex Model, and dual-mode intervention delivery. The system incorporates a dedicated Ethics Supervisor agent and privacy-first design, with early practitioner engagement informing the architecture but no large-scale empirical evaluation yet reported.
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PsyBridge: Hybrid AI framework for multi-dimensional mental health assessment
Researchers propose PsyBridge, a hybrid decision-support framework that integrates PHQ-9, GAD-7, cognitive evaluation, and personality profiling into a unified architecture for mental health risk classification. The system uses a weighted aggregation mechanism to produce interpretable outputs and was evaluated on a semi-synthetic dataset of 500 patient profiles. PsyBridge achieves 0.84 accuracy, outperforming standalone screening tools, with ablation studies confirming the value of multi-dimensional integration. The work targets digital healthcare and telehealth deployment contexts.
MA²P: A Meta-Cognitive Multi-Agent Framework for Complex Persuasion
The paper introduces MA²P, a multi-agent framework designed for complex persuasion tasks where the persuadee's internal states are latent. The system coordinates perception management, mental-state inference, strategy execution, memory, and evaluation modules, and adds a meta-cognitive configurator that selects domain-appropriate strategies from a structured knowledge base to reduce cross-domain performance variance. Experiments show higher persuasion success rates compared to baselines. The work addresses a known weakness of LLMs in producing generic or weakly grounded persuasive responses.
ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents
This paper introduces ENPMR-Bench, a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval in memory-augmented language agents deployed for emotional support applications. The benchmark includes over 1,800 memory-augmented dialogues grounded in Maslow's hierarchy of needs, with structured mappings between emotional needs and supportive memory types. Experiments show that both embedding-based and LLM-driven retrieval paradigms fall significantly short of golden memory conditions on empathy scores, and while chain-of-thought prompting helps, a substantial performance gap remains. The work highlights a systematic gap in current agent memory systems when applied to affective rather than purely factual retrieval tasks.
CollabSim: CSCW-grounded framework for evaluating collaborative competence in LLM multi-agent systems
Researchers introduce CollabSim, a configurable simulation framework for systematically evaluating collaborative competence in LLM-based multi-agent systems (MAS). The framework draws on Computer-Supported Cooperative Work (CSCW) theory to define collaborative capabilities beyond task outcomes, including common ground establishment, shared task understanding, and misalignment repair. Experiments across four LLMs demonstrate the framework can distinguish model performance patterns and reveal task-dependent effects of agent design choices. The work addresses a gap in MAS evaluation, which has historically focused on individual task-solving rather than coordination quality.
Agentopia: Long-term multi-agent life simulation framework for training LLMs on social behavior
Researchers introduce Agentopia, a framework for simulating 10 years of social life across 100 LLM-powered agents, enabling study of emergent social behaviors and long-term personal growth dynamics. The system defines a 'life reward' metric mirroring human well-being and uses it to train LLMs via rejection sampling. Training on simulated social experience yields a +15.6% improvement on downstream role-playing benchmarks, suggesting that synthetic social simulation can generalize to real capability gains.
EMPATH: Multilingual multi-turn safety benchmark for emotional-support chatbots reveals score inflation and run-to-run reliability failures
EMPATH is a new arXiv benchmark for evaluating the safety of emotional-support chatbots, using an auditor model to generate multi-turn crisis conversations and a calibrated judge model to score transcripts across 19 metrics in five dimensions. Built for Mexican Spanish and US English, the benchmark surfaces score inflation on 10 of 19 metrics under uncalibrated rubrics and finds that run-to-run reliability is a per-model safety property: one model swings 2–10 points on a crisis metric across identical reruns, and DeepSeek V4 Pro produces different conversations at temperature 0. Evaluation of three frontier models shows aggregate scores within 0.74 points but per-metric divergences up to six points, with rankings stable across a cross-family judge at 93% within ±1.
SwarmHarness: Decentralized Skill-Based Task Routing Protocol for AI Agent Networks
SwarmHarness is a proposed decentralized protocol enabling AI compute sharing and task routing across heterogeneous nodes (workstations, inference servers, edge devices) without a central coordinator. It combines a DHT-based registry for peer discovery, a utility-function router dispatching tasks by capability/load/latency/trust, and a Shapley-value-based credit mechanism to align incentives among participating nodes. The system is designed as a foundational primitive for autonomous multi-agent networks where agents can hire compute, route subtasks, and settle credits without human intermediation. It positions itself against existing approaches like Golem, BrokerChain, BOINC, and Petals by integrating decentralization with a native incentive layer.
MECoBench: Benchmark for Multimodal Agent Collaboration in Embodied Environments
Researchers introduce MECoBench, a benchmark and evaluation platform for assessing multimodal LLM collaboration in visually grounded embodied environments. The benchmark spans diverse real-world tasks, two cooperation structures, and three collaboration modes. Key findings include that collaboration generally improves task completion but depends on balancing gains against coordination complexity, that communication is essential to collaboration benefits, and that collaboration improves robustness under noisy conditions.