MOSAIC introduces a structured agentic framework that treats automated data science as a staged, context-grounded model selection problem, improving performance and traceability over traditional AutoM…

This paper introduces the concept of Budget-Aware Agents (BAGEN), showing that current LLM agents often fail to manage resources proactively, and proposes that incorporating early stop and interval es…

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

The paper introduces the concepts of Agentic Technical Debt and Stochastic Tax to categorize and manage the unique governance and operating liabilities inherent in complex, multi-step AI agent systems…

This paper introduces progressive crystallization, a lifecycle for AI agents in IT operations that converts validated agent behaviors into cheaper and more reproducible deterministic workflows, increa…

The paper proposes an Entropy Dynamics framework to analyze the stability and failure modes of centralized orchestration in Multi-Agent Systems, identifying a 'Reasoning Trap' where complex reasoning…

The paper introduces an LLM-agent framework to solve the 'last-mile forecasting' problem, bridging the gap between raw statistical predictions and business-ready forecasts by incorporating weakly stru…

The paper introduces DataOrchestra, a framework for example-specific processing of pretraining data for Large Language Models, achieving stable gains and reducing compute.

The authors compare and evaluate 20 software configuration optimizers based on six assumptions about the data, and find that no single optimizer outperforms others across all budgets. They propose a t…

This paper introduces CAM-DF, a cost-aware tool acquisition method for LLM agents, which determines the optimal number of tools to select based on relevance and cost.

This paper proposes a dependency-aware autoscaling framework for serverless computing, integrating graph-based bottleneck identification, short-term workload forecasting, multi-model consensus, and co…

Maestro Order is a model-agnostic orchestration harness that turns unreliable models into reliable problem-solving systems by composing them with structural primitives and a budget-aware controller.

The paper introduces Hierarchical Adaptive Budgeter (HAB), a framework that improves LLM reasoning efficiency by adaptively allocating computational resources to match the intrinsic complexity of both…

KairosAgent is a novel agentic framework that combines Large Language Models (LLMs) for semantic reasoning and Time Series Foundation Models (TSFMs) for numerical forecasting, achieving superior multi…

This paper introduces cost-aware Retrieval-Augmented Generation (RAG), demonstrating that fixed evidence selection is brittle and that adaptive, agentic controllers are necessary for effective knowled…

This paper compares traditional machine learning models (Random Forests, XGBoost, SVM) against a complex Unified Multi-Task Time Series Model for churn prediction, concluding that conventional methods…