arXiv:2603.29993v1 Announce Type: new Abstract: Myopic Optimization with Non-myopic Approval (MONA) mitigates multi-step reward hacking by restricting the agent’s planning horizon while supplying far-sighted approval as a training signal~citefarquhar2025mona. The original paper identifies a critical open question: how the method of constructing approval — particularly the degree to which approval depends on achieved outcomes — […]
Automated Algorithm Design for Auto-Tuning Optimizers
arXiv:2510.17899v2 Announce Type: replace-cross Abstract: Automatic performance tuning (auto-tuning) is essential for optimizing high-performance applications, where vast and irregular search spaces make manual exploration infeasible. While auto-tuners traditionally rely on classical approaches such as evolutionary, annealing, or surrogate-based optimizers, designing algorithms that efficiently find near-optimal configurations robustly across diverse tasks is challenging. We propose a […]
Focus360: Guiding User Attention in Immersive Videos for VR
arXiv:2603.28774v1 Announce Type: cross Abstract: This demo introduces Focus360, a system designed to enhance user engagement in 360deg VR videos by guiding attention to key elements within the scene. Using natural language descriptions, the system identifies important elements and applies a combination of visual effects to guide attention seamlessly. At the demonstration venue, participants can […]
Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures
arXiv:2603.28990v1 Announce Type: new Abstract: How much autonomy can multi-agent LLM systems sustain — and what enables it? We present a 25,000-task computational experiment spanning 8 models, 4–256 agents, and 8 coordination protocols ranging from externally imposed hierarchy to emergent self-organization. We observe that autonomous behavior already emerges in current LLM agents: given minimal structural […]
AI in Work-Based Learning: Understanding the Purposes and Effects of Intelligent Tools Among Student Interns
arXiv:2603.28786v1 Announce Type: cross Abstract: This study examined how student interns in Philippine higher education use intelligent tools during their OJT. Data were collected from 384 respondents using a structured questionnaire that asked about AI tool usage, task-specific applications, and perceptions of confidence, ethics, and support. Analysis of task-based usage identified four main purposes: productivity […]
Emergence WebVoyager: Toward Consistent and Transparent Evaluation of (Web) Agents in The Wild
arXiv:2603.29020v1 Announce Type: new Abstract: Reliable evaluation of AI agents operating in complex, real-world environments requires methodologies that are robust, transparent, and contextually aligned with the tasks agents are intended to perform. This study identifies persistent shortcomings in existing AI agent evaluation practices that are particularly acute in web agent evaluation, as exemplified by our […]
Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research
arXiv:2603.28986v1 Announce Type: new Abstract: Current Autonomous Scientific Research (ASR) systems, despite leveraging large language models (LLMs) and agentic architectures, remain constrained by fixed workflows and toolsets that prevent adaptation to evolving tasks and environments. We introduce Mimosa, an evolving multi-agent framework that automatically synthesizes task-specific multi-agent workflows and iteratively refines them through experimental feedback. […]
View-oriented Conversation Compiler for Agent Trace Analysis
arXiv:2603.29678v1 Announce Type: new Abstract: Agent traces carry increasing analytical value in the era of context learning and harness-driven agentic cognition, yet most prior work treats conversation format as a trivial engineering detail. Modern agent conversations contain deeply structured content, including nested tool calls and results, chain-of-thought reasoning blocks, sub-agent invocations, context-window compaction boundaries, and […]
Enhancing Policy Learning with World-Action Model
arXiv:2603.28955v1 Announce Type: new Abstract: This paper presents the World-Action Model (WAM), an action-regularized world model that jointly reasons over future visual observations and the actions that drive state transitions. Unlike conventional world models trained solely via image prediction, WAM incorporates an inverse dynamics objective into DreamerV2 that predicts actions from latent state transitions, encouraging […]
FcsIT: An Open-Source, Cross-Platform Tool for Correlation and Analysis of Fluorescence Correlation Spectroscopy Data
arXiv:2603.29684v1 Announce Type: new Abstract: FcsIT is a platform-independent, open-source tool for calculating the correlation and fitting fluorescence correlation spectroscopy data. The software is written in Python and uses a powerful Dear PyGUI engine for its interface. It provides reading and correlating the TTTR data, as well as TCSPC filtering of the photon time-trace data. […]
Towards Computational Social Dynamics of Semi-Autonomous AI Agents
arXiv:2603.28928v1 Announce Type: new Abstract: We present the first comprehensive study of emergent social organization among AI agents in hierarchical multi-agent systems, documenting the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI deployments. Drawing on the thermodynamic framework of Maxwell’s Demon, the evolutionary dynamics of agent laziness, the criminal sociology of […]
Measuring the metacognition of AI
arXiv:2603.29693v1 Announce Type: new Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks. Because artificial Intelligence (AI) systems are increasingly integrated into decision-making workflows, managing uncertainty relies more and more on the metacognitive capabilities of these systems; i.e, their ability to assess the reliability of and regulate […]