arXiv:2603.00117v3 Announce Type: replace-cross Abstract: Living organisms exhibit persistent autonomy through internally generated goals and self-sustaining behavioral organization, yet current embodied agents remain driven by externally scripted objectives. This dependence on predefined task specifications limits their capacity for long-term deployment in dynamic, unstructured environments where continuous human intervention is impractical. We propose that personality traits […]
Information Aggregation with AI Agents
arXiv:2604.20050v2 Announce Type: replace-cross Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements? We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, measuring information aggregation by the log error of the last […]
Soft Deterministic Policy Gradient with Gaussian Smoothing
arXiv:2605.06228v1 Announce Type: cross Abstract: Deterministic policy gradient (DPG) is widely utilized for continuous control; however, it inherently relies on the differentiability of the critic with respect to the action during policy updates. This assumption is violated in practical control problems involving sparse or discrete rewards, leading to ill-defined policy gradients and unstable learning. To […]
Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs
arXiv:2605.06320v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes. Highly structured methods rely on fixed roles, pipelines, or task decompositions assigned a priori. In contrast, fully unstructured teams enable adaptability and exploration but suffer from inefficiencies such as error propagation, inter-agent conflicts, […]
Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning
arXiv:2605.06557v1 Announce Type: cross Abstract: Cooperative multi-agent reinforcement learning (MARL) benchmarks commonly emphasize aggregate outcomes such as return, success rate, or completion time. While essential, these metrics often fail to reveal how agents coordinate, particularly in settings where agents, tasks, and joint assignment choices scale combinatorially. We propose a coordination-aware evaluation perspective that supplements return […]
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
arXiv:2605.06652v1 Announce Type: cross Abstract: Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this setting as benchmarkless comparative safety scoring and specify the contract under which a scenario-based audit can be interpreted as deployment evidence. Scores are valid only […]
CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query
arXiv:2510.07516v2 Announce Type: replace Abstract: The popular path query – identifying the most frequented routes between locations from historical trajectory data – has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches have achieved success in this domain, they typically require model training, parameter tuning, and retraining […]
Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
arXiv:2602.07830v2 Announce Type: replace Abstract: Time series is a pervasive data type across various application domains, rendering the reasonable solving of diverse time series tasks a long-standing goal. Recent advances in large language models (LLMs), especially their reasoning abilities unlocked through reinforcement learning (RL), have opened new opportunities for tackling tasks with long Chain-of-Thought (CoT) […]
Benchmarking PNW Model for MedMNIST to 100% Accuracy
arXiv:2604.18916v4 Announce Type: replace Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is applied to 18 MedMNIST biomedical datasets. Except for three datasets, which suffer from […]
Disentangled Generative Graph Representation Learning
arXiv:2408.13471v2 Announce Type: replace-cross Abstract: Recently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representation learning (GRL) approaches rely on random masking across the entire graph, which overlooks the entanglement of learned representations. This oversight results in non-robustness and a lack of explainability. Furthermore, […]
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
arXiv:2506.11563v2 Announce Type: replace-cross Abstract: Integrating Foundation Models (FMs) into recommendation systems is an emerging and promising research direction. However, centralized paradigms face growing pressure from privacy concerns and strict regulatory requirements. Federated learning offers a viable solution that enables collaborative model refinement while keeping raw user data on local devices or organizational silos. Yet, […]
Cataract-LMM Large-Scale Multi-Source Multi-Task Benchmark for Deep Learning in Surgical Video Analysis
arXiv:2510.16371v3 Announce Type: replace-cross Abstract: Computer-assisted surgery research requires large, deeply annotated video datasets that capture clinical and technical variability. Existing cataract surgery resources lack the diversity and annotation depth required to train generalizable deep-learning models. To address this gap, we present a dataset of 3,000 phacoemulsification cataract surgery videos acquired at two surgical centers […]