111 papers
This paper introduces XR-DT, an Extended Reality-enhanced Digital Twin framework that integrates a novel Human-Aware Model Predictive Path Integral (HA-MPPI) controller with an attention-based trajectory prediction model to enable safe, efficient, and interpretable motion planning for mobile robots operating alongside humans.
The paper introduces KramaBench, a comprehensive benchmark featuring 104 real-world data-to-insight challenges across diverse domains, which reveals that current AI systems struggle to orchestrate end-to-end data pipelines over data lakes, achieving a maximum of only 55% accuracy despite strong performance in isolated tasks.
This paper introduces SysDPO, a framework that aligns complex, multi-component Compound AI Systems with human preferences by modeling them as Directed Acyclic Graphs and extending Direct Preference Optimization to overcome the challenges of non-differentiable interactions and the difficulty of translating system-level preferences to component levels.
This paper presents a multimodal large language model-based multi-agent system that significantly outperforms existing state-of-the-art methods in automatically extracting structured chemical information from diverse and complex literature graphics, thereby advancing AI-driven chemical research.
This paper introduces MASFactory, a graph-centric framework that utilizes a human-in-the-loop "Vibe Graphing" approach to automatically compile natural language intents into executable multi-agent system workflows, thereby addressing challenges in manual implementation, component reuse, and context integration while demonstrating effectiveness across seven public benchmarks.
This paper presents a white-box analysis of the EleutherAI/gpt-j-6B model, demonstrating through contrastive prompting that its internal representation of trust aligns most closely with the Castelfranchi socio-cognitive model, thereby validating the feasibility of using LLM activation spaces to analyze socio-cognitive constructs and inform human-AI collaboration.
The paper introduces RACAS, a robot-agnostic agentic system that uses natural language communication between LLM/VLM-based modules to control diverse robotic platforms without requiring code modifications or retraining, successfully demonstrating its effectiveness across wheeled, multi-jointed, and underwater robots.
This paper proposes a schema-gated agentic AI architecture that resolves the trade-off between conversational flexibility and execution determinism in scientific workflows by enforcing machine-checkable specifications as mandatory execution boundaries, a solution validated through multi-model LLM scoring of 20 existing systems.
This paper introduces Conversational Demand Response (CDR), a bidirectional coordination framework leveraging agentic AI to enable natural language interactions between aggregators and prosumers, thereby combining automated scalability with enhanced user transparency and agency to sustain residential demand response participation.
This paper introduces an algorithm capable of computing all evolutionarily stable strategies within nondegenerate normal-form games involving three or more players.
This paper proposes a fully autonomous, state-of-the-art Integrated Air Defense system utilizing two BDI-based agents that employ meta-level plan reasoning to automatically detect threats, assess risks, and allocate weapons without manual intervention, thereby advancing network-centric warfare capabilities.
The paper introduces RepoLaunch, an agent that automates the build and test pipeline for code repositories across any language and platform, thereby enabling fully automated creation of software engineering datasets for benchmarking and training coding agents.
This paper introduces a multi-operator reinforcement learning framework that integrates discrete choice theory to model competitive dynamics in Autonomous Mobility-on-Demand systems, demonstrating that competition fundamentally alters learned pricing and fleet rebalancing strategies compared to monopolistic settings while maintaining robust convergence.
MedCoRAG is an interpretable, end-to-end framework for hepatic disease diagnosis that combines hybrid evidence retrieval from knowledge graphs and clinical guidelines with a multi-agent collaborative reasoning process to emulate multidisciplinary consultation and outperform existing methods in accuracy and transparency.
Jagarin is a three-layer mobile architecture comprising the DAWN urgency engine, ARIA email proxy, and ACE institutional protocol that enables personal AI agents to hibernate until contextually necessary, thereby resolving the conflict between battery efficiency and timely duty fulfillment without compromising privacy or requiring persistent background execution.
SCoUT is a scalable multi-agent reinforcement learning framework that improves coordination by dynamically grouping agents into latent clusters for efficient value estimation and employing counterfactual advantages to precisely guide decentralized communication decisions on when and whom to message.
This paper proposes a decentralized exploration framework for large robot swarms that combines a self-organizing algorithm for dynamic team formation with a novel large language model-based strategy for autonomous target selection, validated through extensive simulations.
This paper challenges the prevailing view of AI memory as a mere functional tool by proposing the "Memory-as-Ontology" paradigm and its associated "Animesis" system, which prioritizes identity continuity and governance over retrieval performance to enable persistent digital citizens that survive model transitions.
This paper introduces iAgentBench, a dynamic open-domain question answering benchmark designed to evaluate the cross-source sensemaking capabilities of information-seeking agents on high-traffic topics by requiring the integration of evidence from multiple sources rather than simple retrieval.
This paper proposes a novel three-level Stackelberg game framework that incorporates heterogeneous leaders and non-follower agents to accurately model congestion-coupled strategic interactions, demonstrating that ignoring non-participating agents leads to distorted equilibrium predictions in systems like EV charging markets.