Why we think this paper is great for you:
This paper directly addresses the progression towards AGI and superintelligence, which is central to your research interests. It provides a framework for understanding and measuring the advancement of autonomous AI.

Abstract
We propose a Kardashev-inspired yet operational Autonomous AI (AAI) Scale that measures the progression from fixed robotic process automation (AAI-0) to full artificial general intelligence (AAI-4) and beyond. Unlike narrative ladders, our scale is multi-axis and testable. We define ten capability axes (Autonomy, Generality, Planning, Memory/Persistence, Tool Economy, Self-Revision, Sociality/Coordination, Embodiment, World-Model Fidelity, Economic Throughput) aggregated by a composite AAI-Index (a weighted geometric mean). We introduce a measurable Self-Improvement Coefficient $κ$ (capability growth per unit of agent-initiated resources) and two closure properties (maintenance and expansion) that convert ``self-improving AI'' into falsifiable criteria. We specify OWA-Bench, an open-world agency benchmark suite that evaluates long-horizon, tool-using, persistent agents. We define level gates for AAI-0\ldots AAI-4 using thresholds on the axes, $κ$, and closure proofs. Synthetic experiments illustrate how present-day systems map onto the scale and how the delegability frontier (quality vs.\ autonomy) advances with self-improvement. We also prove a theorem that AAI-3 agent becomes AAI-5 over time with sufficient conditions, formalizing "baby AGI" becomes Superintelligence intuition.

AI Summary

• The AAI Scale provides a multi-axis, operational, and testable framework for measuring AI progression from fixed automation (AAI-0) to superintelligence (AAI-5), moving beyond qualitative narrative ladders. [3]
• The paper introduces the Self-Improvement Coefficient (κ) and two closure properties (maintenance and expansion) to establish falsifiable criteria for genuinely self-improving AI systems. [3]
• The paper formally proves that an AAI-3 agent, given sufficient self-improvement conditions and resource responsiveness, will inevitably progress to AAI-5 (Superintelligence) in finite time and with finite resources. [3]
• Autonomous AI (AAI) Scale: A multi-axis, operational, and testable framework measuring AI progression from fixed robotic process automation (AAI-0) to full artificial general intelligence (AAI-4) and beyond. [3]
• AAI-Index: A composite capability score for an AI agent, calculated as a weighted geometric mean of ten normalized capability axes (Autonomy, Generality, Planning, Memory/Persistence, Tool Economy, Self-Revision, Sociality/Coordination, Embodiment, World-Model Fidelity, Economic Throughput). [3]
• Self-Improvement Coefficient (κ): The marginal capability gain (dC) per unit of agent-initiated resources (dR), quantifying the efficiency of an agent's self-tuning, tool acquisition, or workflow rewriting. [3]
• A composite AAI-Index, calculated as a weighted geometric mean of ten capability axes, is proposed to penalize lopsided development and encourage balanced AI capability growth. [2]
• OWA-Bench is introduced as a specialized benchmark suite designed to evaluate long-horizon planning, persistence under drift, tool discovery, and multi-agent coordination in open-world settings. [2]
• The AAI scale complements cognitive assessments (like CHC-based definitions) by focusing on agentic performance under deployment constraints, offering a comprehensive two-view evaluation of AI systems. [2]
• Concrete, measurable level gates for AAI-0 through AAI-4 are defined using thresholds on capability axes, the self-improvement coefficient (κ), and auditable closure proofs. [1]

Why we think this paper is great for you:
This paper is highly relevant to your interest in labor market changes and job displacement, specifically exploring how GenAI impacts these shifts. It offers insights into predicting future labor market trends.

Abstract
The rapid advancement of Large Language Models (LLMs) has generated considerable speculation regarding their transformative potential for labor markets. However, existing approaches to measuring AI exposure in the workforce predominantly rely on concurrent market conditions, offering limited predictive capacity for anticipating future disruptions. This paper presents a predictive study examining whether online discussions about LLMs can function as early indicators of labor market shifts. We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes, thereby demonstrating its predictive validity for labor market dynamics. Drawing on a comprehensive dataset that integrates the REALM corpus of LLM discussions, LinkedIn job postings, Indeed employment indices, and over 4 million LinkedIn user profiles, we analyze the relationship between discussion intensity across news media and Reddit forums and subsequent variations in job posting volumes, occupational net change ratios, job tenure patterns, unemployment duration, and transitions to GenAI-related roles across thirteen occupational categories. Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration. These findings suggest that monitoring online discourse can provide actionable intelligence for workers making reskilling decisions and organizations anticipating skill requirements, offering a real-time complement to traditional labor statistics in navigating technological disruption.

Why we think this paper is great for you:
This report explores the practical applications of generative AI models in development, aligning with your interest in AGI applications and development. It highlights how AI is transforming human-AI interaction in coding.

Abstract
Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE). To explore the impact of GenAI on IDEs, 33 experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222. This is the report

Why we think this paper is great for you:
This paper describes an integrated multi-agent system for scientific assistance, which aligns with your interest in AGI applications and development. It showcases a practical implementation of advanced AI for complex workflows.

Abstract
AI Scientific Assistant Core (AISAC) is an integrated multi-agent system developed at Argonne National Laboratory for scientific and engineering workflows. AISAC builds on established technologies - LangGraph for orchestration, FAISS for vector search, and SQLite for persistence - and integrates them into a unified system prototype focused on transparency, provenance tracking, and scientific adaptability. The system implements a Router-Planner-Coordinator workflow and an optional Evaluator role, using prompt-engineered agents coordinated via LangGraph's StateGraph and supported by helper agents such as a Researcher. Each role is defined through custom system prompts that enforce structured JSON outputs. A hybrid memory approach (FAISS + SQLite) enables both semantic retrieval and structured conversation history. An incremental indexing strategy based on file hashing minimizes redundant re-embedding when scientific corpora evolve. A configuration-driven project bootstrap layer allows research teams to customize tools, prompts, and data sources without modifying core code. All agent decisions, tool invocations, and retrievals are logged and visualized through a custom Gradio interface, providing step-by-step transparency for each reasoning episode. The authors have applied AISAC to multiple research areas at Argonne, including specialized deployments for waste-to-products research and energy process safety, as well as general-purpose scientific assistance, demonstrating its cross-domain applicability.

Why we think this paper is great for you:
While broadly related to the labor market, this paper focuses on the gender pay gap and does not directly connect to AI, AGI, or job displacement. It offers a methodological approach to a specific economic issue.

Abstract
We propose a new approach to estimate selection-corrected quantiles of the gender wage gap. Our method employs instrumental variables that explain variation in the latent variable but, conditional on the latent process, do not directly affect selection. We provide semiparametric identification of the quantile parameters without imposing parametric restrictions on the selection probability, derive the asymptotic distribution of the proposed estimator based on constrained selection probability weighting, and demonstrate how the approach applies to the Roy model of labor supply. Using German administrative data, we analyze the distribution of the gender gap in full-time earnings. We find pronounced positive selection among women at the lower end, especially those with less education, which widens the gender gap in this segment, and strong positive selection among highly educated men at the top, which narrows the gender wage gap at upper quantiles.