AI Agents

Nvidia goes all-in on AI agents while Anthropic pulls the plug

TLDR: Nvidia is partnering with 17 major companies to build a platform specifically for enterprise AI agents, basically trying to become ...

[P] Implemented ACT-R cognitive decay and hyperdimensional computing for AI agent memory (open source)

Built a memory server for AI agents (MCP protocol) and implemented two cognitive science techniques in v7.5 I wanted to share. ACT-R Cogn...

"They operate like slot machines": AI agents are scrambling power users' brains

[2402.08646] Inference of Abstraction for a Unified Account of Symbolic Reasoning from Data

This paper presents a unified probabilistic framework for symbolic reasoning, drawing inspiration from neuroscience, and aims to enhance ...

[2305.11098] A Simple Generative Model of Logical Reasoning and Statistical Learning

This paper presents a Bayesian model that unifies logical reasoning and statistical learning, proposing a framework for human-like machin...

[2206.13174] Towards Unifying Perceptual Reasoning and Logical Reasoning

The paper presents a probabilistic model that unifies perceptual reasoning and logical reasoning, highlighting their shared processes of ...

[2504.10917] Towards A Universal Graph Structural Encoder

The paper presents GFSE, a universal graph structural encoder designed to capture transferable structural patterns across various graph d...

[2602.20144] Agentic AI for Scalable and Robust Optical Systems Control

The paper presents AgentOptics, an AI framework for autonomous control of optical systems, achieving high task success rates and demonstr...

[2602.20135] KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

The paper introduces KNIGHT, a framework for generating multiple-choice questions using knowledge graphs, enhancing efficiency and adapta...

[2503.23608] Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's

This paper explores a high-dimensional computing architecture that mimics biological learning processes, proposing a model that integrate...

[2602.20133] AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization

AdaEvolve introduces a novel framework for optimizing large language model-driven evolution, addressing inefficiencies in resource alloca...

[2602.20130] To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

The paper presents Selective Chain-of-Thought (Selective CoT), a method to enhance medical question answering efficiency using large lang...

[2502.09257] From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards

This paper explores contextual combinatorial semi-bandits, presenting an algorithm that improves sample complexity in sparse reward scena...

[2502.08941] Analysis of Off-Policy $n$-Step TD-Learning with Linear Function Approximation

This paper analyzes off-policy n-step TD-learning algorithms with linear function approximation, demonstrating their convergence and fund...

[2602.20119] NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning

NovaPlan introduces a framework for zero-shot long-horizon manipulation in robotics, integrating video language planning with geometrical...

[2502.04591] Are We Measuring Oversmoothing in Graph Neural Networks Correctly?

This article critiques traditional metrics for measuring oversmoothing in Graph Neural Networks (GNNs) and proposes a rank-based approach...

[2602.20113] StyleStream: Real-Time Zero-Shot Voice Style Conversion

StyleStream introduces a novel real-time zero-shot voice style conversion system that enhances voice synthesis by disentangling linguisti...

[2602.20100] Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine

This article discusses the shift from expert annotation to AI-driven unsupervised learning in biomedicine, highlighting its potential to ...

[2410.13331] Improving Discrete Optimisation Via Decoupled Straight-Through Estimator

The paper presents the Decoupled Straight-Through Estimator (Decoupled ST), a new method for optimizing discrete variables in neural netw...

[2602.20064] The LLMbda Calculus: AI Agents, Conversations, and Information Flow

The LLMbda Calculus introduces a formal framework for understanding AI agents' conversations, addressing vulnerabilities like prompt inje...

[2602.20057] AdaWorldPolicy: World-Model-Driven Diffusion Policy with Online Adaptive Learning for Robotic Manipulation

The paper presents AdaWorldPolicy, a novel framework for robotic manipulation that utilizes world models and online adaptive learning to ...

[2310.01770] A simple connection from loss flatness to compressed neural representations

This article explores the relationship between loss flatness and compressed neural representations, introducing new measures and empirica...

[2602.20055] To Move or Not to Move: Constraint-based Planning Enables Zero-Shot Generalization for Interactive Navigation

This paper presents a novel constraint-based planning framework for mobile robots, enabling zero-shot generalization in interactive navig...