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
Machine Learning
[2506.10167] Wasserstein Barycenter Soft Actor-Critic
The Wasserstein Barycenter Soft Actor-Critic (WBSAC) algorithm enhances sample efficiency in reinforcement learning by combining pessimis...
arXiv - Machine Learning
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3 min
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Llms
[2503.23434] Towards Trustworthy GUI Agents: A Survey
This survey explores the challenges of building trustworthy GUI agents, highlighting the execution gap and proposing a taxonomy for under...
arXiv - Machine Learning
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3 min
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Machine Learning
[2505.11602] Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
This paper explores the regularity and stability properties of selective state-space models (SSMs) with discontinuous gating, focusing on...
arXiv - Machine Learning
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4 min
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Ai Safety
[2602.20465] Prior-Agnostic Incentive-Compatible Exploration
The paper discusses a novel approach to incentive-compatible exploration in bandit settings, addressing the misalignment between principa...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.20297] Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
This paper presents gap-dependent performance guarantees for nearly minimax-optimal reinforcement learning algorithms using linear functi...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.18431] SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
The paper presents SMaRT, an innovative algorithm for online resource allocation in the Kenyan judiciary, focusing on mediator assignment...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.21133] SOM-VQ: Topology-Aware Tokenization for Interactive Generative Models
The paper presents SOM-VQ, a novel tokenization method that enhances interactive generative models by integrating vector quantization wit...
arXiv - Machine Learning
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3 min
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Llms
[2602.21158] SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards
The paper presents SELAUR, a reinforcement learning framework that enhances large language models (LLMs) by integrating uncertainty into ...
arXiv - Machine Learning
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3 min
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Ai Agents
[2602.21020] Matching Multiple Experts: On the Exploitability of Multi-Agent Imitation Learning
This paper explores the challenges of multi-agent imitation learning (MA-IL), particularly the exploitability of learned policies in mult...
arXiv - Machine Learning
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3 min
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Nlp
[2602.20932] Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
This article presents a novel approach to EEG-to-text decoding, exploring how hierarchical abstraction levels affect classification perfo...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.20911] From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning
The paper presents the Semantic-guided Adaptive Expert Forest (SAEF), a novel approach for Class-Incremental Learning (CIL) that enhances...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.20904] Transcoder Adapters for Reasoning-Model Diffing
This paper introduces transcoder adapters, a method for analyzing the internal changes in reasoning models post fine-tuning, demonstratin...
arXiv - Machine Learning
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4 min
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Ai Agents
[2602.20804] Probing Dec-POMDP Reasoning in Cooperative MARL
This paper examines the effectiveness of benchmarks in cooperative multi-agent reinforcement learning (MARL) by analyzing Dec-POMDP reaso...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.20796] Exploring the Impact of Parameter Update Magnitude on Forgetting and Generalization of Continual Learning
This article investigates how the magnitude of parameter updates affects forgetting and generalization in continual learning, proposing a...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.20791] Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities
This paper explores the impact of rehearsal scale on continual learning, revealing counterintuitive effects on adaptability and memory re...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.20730] Rethink Efficiency Side of Neural Combinatorial Solver: An Offline and Self-Play Paradigm
The paper presents ECO, a new learning paradigm for Neural Combinatorial Optimization that enhances efficiency through offline self-play,...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.20729] Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
The paper presents Fuz-RL, a fuzzy-guided framework for safe reinforcement learning that addresses uncertainties in real-world applicatio...
arXiv - Machine Learning
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3 min
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Llms
[2602.20629] QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
The paper presents QEDBench, a benchmark for evaluating the alignment of automated systems in assessing university-level mathematical pro...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.20574] GATES: Self-Distillation under Privileged Context with Consensus Gating
The paper presents GATES, a self-distillation method for document-grounded question answering, enhancing model performance by leveraging ...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.20530] Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
The paper presents a novel framework, Memory-guided Prototypical Co-occurrence Learning (MPCL), aimed at improving mixed emotion recognit...
arXiv - Machine Learning
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4 min
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