Ai Agents
Microsoft's newest open-source project: Runtime security for AI agents
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Reddit - Artificial Intelligence
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1 min
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AI Agents
Autonomous agents, tool use, and agentic systems
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![[2510.16609] Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Llms
[2510.16609] Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods
Abstract page for arXiv paper 2510.16609: Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods
arXiv - Machine Learning
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4 min
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Machine Learning
[2604.02131] Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization
Abstract page for arXiv paper 2604.02131: Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization
arXiv - Machine Learning
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3 min
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All Content
Llms
[2602.21420] Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning
This paper introduces the Asymmetric Confidence-aware Error Penalty (ACE) to enhance reinforcement learning by addressing overconfident e...
arXiv - Machine Learning
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4 min
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Ai Agents
[2602.21401] The Headless Firm: How AI Reshapes Enterprise Boundaries
The paper discusses how AI is transforming enterprise structures by reducing coordination costs, leading to the emergence of the 'Headles...
arXiv - AI
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4 min
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Ai Infrastructure
[2602.21368] Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration
This paper presents a method for certifying the reliability of black-box AI systems using self-consistency sampling and conformal calibra...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.21360] Representation Theorems for Cumulative Propositional Dependence Logics
This paper presents representation theorems for cumulative propositional dependence logic and team semantics, establishing key equivalenc...
arXiv - AI
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3 min
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Ai Startups
[2602.21327] Equitable Evaluation via Elicitation
The paper discusses an AI-driven approach for equitable skill evaluation, addressing biases in self-presentation among job seekers. It pr...
arXiv - Machine Learning
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3 min
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Llms
[2602.21269] Group Orthogonalized Policy Optimization:Group Policy Optimization as Orthogonal Projection in Hilbert Space
The paper introduces Group Orthogonalized Policy Optimization (GOPO), a novel algorithm for aligning large language models using Hilbert ...
arXiv - Machine Learning
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4 min
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Llms
[2602.21255] A General Equilibrium Theory of Orchestrated AI Agent Systems
This paper presents a general equilibrium theory for orchestrated AI agent systems, modeling large language model (LLM) agents within a p...
arXiv - AI
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4 min
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Llms
[2602.21251] AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI
AgenticTyper is a novel AI-driven tool that automates the typing of legacy JavaScript projects, significantly reducing manual effort and ...
arXiv - AI
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3 min
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Llms
[2602.21231] ACAR: Adaptive Complexity Routing for Multi-Model Ensembles with Auditable Decision Traces
The paper presents ACAR, a framework for adaptive complexity routing in multi-model ensembles, demonstrating improved task routing accura...
arXiv - Machine Learning
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4 min
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Llms
[2602.21228] ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
The paper presents ImpRIF, a method to enhance large language models' implicit reasoning capabilities, improving their performance in fol...
arXiv - AI
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4 min
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Llms
[2602.21227] Budget-Aware Agentic Routing via Boundary-Guided Training
The paper presents Budget-Aware Agentic Routing, a method for optimizing the use of large language models in autonomous agents by balanci...
arXiv - AI
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4 min
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Ai Agents
[2602.21220] Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation
The paper presents a novel memory system for AI agents, utilizing continuous fields governed by partial differential equations to enhance...
arXiv - Machine Learning
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3 min
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Llms
[2602.22070] Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts
This study explores how large language models (LLMs) exhibit inconsistent biases towards algorithmic agents and human experts in decision...
arXiv - AI
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4 min
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Ai Agents
[2602.22094] Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning
This paper presents a novel approach using Petri nets to identify infeasibilities in sequential task planning, enhancing robustness and e...
arXiv - AI
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3 min
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Llms
[2602.21858] ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices
The paper introduces ProactiveMobile, a benchmark aimed at enhancing proactive intelligence in mobile devices, addressing the limitations...
arXiv - AI
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4 min
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Llms
[2602.22067] Semantic Partial Grounding via LLMs
The paper proposes SPG-LLM, a novel approach for semantic partial grounding in AI planning that utilizes large language models (LLMs) to ...
arXiv - AI
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3 min
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Machine Learning
[2602.21889] 2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
The paper presents the 2-Step Agent framework, which models the interaction between decision makers and AI decision support systems, high...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.21746] fEDM+: A Risk-Based Fuzzy Ethical Decision Making Framework with Principle-Level Explainability and Pluralistic Validation
The paper presents fEDM+, an enhanced fuzzy ethical decision-making framework that improves explainability and validation by integrating ...
arXiv - AI
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4 min
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Llms
[2602.21814] Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem
This study investigates how different prompt architectures affect reasoning quality in large language models, specifically addressing the...
arXiv - AI
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3 min
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Machine Learning
[2602.21745] The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems
The ASIR Courage Model presents a phase-dynamic framework for understanding truth transitions in both human and AI systems, emphasizing t...
arXiv - AI
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4 min
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