Ai Agents
Sierra's Bret Taylor says the era of clicking buttons is over | TechCrunch
Co-founder of Sierra predicts that AI agents will make software interfaces obsolete.
TechCrunch - AI
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4 min
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AI Agents
Autonomous agents, tool use, and agentic systems
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Ai Agents
Visa rolls out AI agent shopping infrastructure
submitted by /u/tekz [link] [comments]
Reddit - Artificial Intelligence
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1 min
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Llms
I compiled every major AI agent security incident from 2024-2026 in one place - 90 incidents, all sourced, updated weekly
After tracking AI agent security incidents for the past year, I put together a single reference covering every major breach, vulnerabilit...
Reddit - Artificial Intelligence
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1 min
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All Content
![[2602.15875] Fly0: Decoupling Semantic Grounding from Geometric Planning for Zero-Shot Aerial Navigation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Llms
[2602.15875] Fly0: Decoupling Semantic Grounding from Geometric Planning for Zero-Shot Aerial Navigation
The paper presents Fly0, a novel framework that separates semantic grounding from geometric planning to enhance zero-shot aerial navigati...
arXiv - AI
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3 min
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Llms
[2602.15873] Test-Time Adaptation for Tactile-Vision-Language Models
This paper presents a novel approach to test-time adaptation (TTA) for tactile-vision-language (TVL) models, addressing challenges posed ...
arXiv - AI
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3 min
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Ai Safety
[2602.16543] Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning
This paper presents a framework for analyzing the vulnerabilities of Safe Reinforcement Learning (Safe RL) policies against adversarial a...
arXiv - Machine Learning
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3 min
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Llms
[2602.15867] Playing With AI: How Do State-Of-The-Art Large Language Models Perform in the 1977 Text-Based Adventure Game Zork?
This paper evaluates the performance of state-of-the-art Large Language Models (LLMs) in the 1977 text-based adventure game Zork, reveali...
arXiv - AI
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4 min
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Ai Agents
[2602.15865] AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support
This article reviews the role of AI in decision support, analyzing whether AI systems act as tools or collaborative teammates. It highlig...
arXiv - AI
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3 min
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Machine Learning
[2602.16525] Capacity-constrained demand response in smart grids using deep reinforcement learning
This paper explores a capacity-constrained demand response strategy for smart grids using deep reinforcement learning, aiming to optimize...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.16523] Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study
This paper explores reinforcement learning techniques for parameterized quantum state preparation, comparing one-stage and two-stage trai...
arXiv - Machine Learning
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4 min
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Llms
[2602.15858] State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
This paper explores how state representations impact the reasoning capabilities of large language models (LLMs) in dynamic environments, ...
arXiv - AI
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4 min
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Llms
[2602.15854] Decoupling Strategy and Execution in Task-Focused Dialogue via Goal-Oriented Preference Optimization
This paper presents Goal-Oriented Preference Optimization (GOPO), a new framework for enhancing task-oriented dialogue systems by decoupl...
arXiv - AI
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4 min
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Llms
[2602.15848] Can LLMs Assess Personality? Validating Conversational AI for Trait Profiling
This study evaluates the effectiveness of Large Language Models (LLMs) in assessing personality traits compared to traditional questionna...
arXiv - AI
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3 min
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Ai Infrastructure
[2602.16363] Improved Bounds for Reward-Agnostic and Reward-Free Exploration
This paper presents improved algorithms for reward-free and reward-agnostic exploration in Markov decision processes, enhancing the abili...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.15832] What Persona Are We Missing? Identifying Unknown Relevant Personas for Faithful User Simulation
This article explores the identification of unknown user personas in simulations, introducing the PICQ dataset and evaluating leading LLM...
arXiv - AI
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3 min
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Ai Agents
[2602.16666] Towards a Science of AI Agent Reliability
This paper explores the reliability of AI agents, proposing twelve metrics to evaluate their performance across dimensions like consisten...
arXiv - Machine Learning
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3 min
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Llms
[2602.16653] Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments
The article explores the Agent Skill Framework, assessing its effectiveness in enhancing small language models (SLMs) for industrial appl...
arXiv - AI
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4 min
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Llms
[2602.16512] Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
The article presents the Framework of Thoughts (FoT), a new foundation framework designed to enhance the reasoning capabilities of large ...
arXiv - AI
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3 min
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Machine Learning
[2602.16316] A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
This paper introduces WS-KAN, a novel weight-space architecture for Kolmogorov-Arnold Networks (KANs), demonstrating its superior perform...
arXiv - Machine Learning
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4 min
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Robotics
[2602.16435] Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
The paper presents CAFE, a novel framework for automated feature engineering that combines causal discovery with multi-agent reinforcemen...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.16274] Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains
This paper presents a high-probability regret bound for online Q-learning in infinite-horizon discounted Markov decision processes, analy...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.16424] Verifiable Semantics for Agent-to-Agent Communication
This paper introduces a certification protocol for agent-to-agent communication in multiagent AI systems, addressing semantic drift and e...
arXiv - AI
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3 min
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Machine Learning
[2602.16264] Prediction of Major Solar Flares Using Interpretable Class-dependent Reward Framework with Active Region Magnetograms and Domain Knowledge
This article presents a novel supervised classification framework for predicting major solar flares using class-dependent rewards and dee...
arXiv - Machine Learning
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4 min
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