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Llms

What I learned about multi-agent coordination running 9 specialized Claude agents

I've been experimenting with multi-agent AI systems and ended up building something more ambitious than I originally planned: a fully ope...

Reddit - Artificial Intelligence · 1 min ·
Robotics

What happens when AI agents can earn and spend real money? I built a small test to find out

I've been sitting with a question for a while: what happens when AI agents aren't just tools to be used, but participants in an economy? ...

Reddit - Artificial Intelligence · 1 min ·
[2601.00809] A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction
Llms

[2601.00809] A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction

Abstract page for arXiv paper 2601.00809: A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction

arXiv - AI · 4 min ·

All Content

[2602.22157] Dynamic Personality Adaptation in Large Language Models via State Machines
Llms

[2602.22157] Dynamic Personality Adaptation in Large Language Models via State Machines

This paper presents a model-agnostic framework for dynamic personality adaptation in Large Language Models (LLMs) using state machines, e...

arXiv - Machine Learning · 4 min ·
[2602.22056] FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation
Machine Learning

[2602.22056] FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation

The paper presents FlowCorrect, a framework for correcting generative flow policies in robotic manipulation using minimal human input, im...

arXiv - Machine Learning · 3 min ·
[2602.21783] Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control
Machine Learning

[2602.21783] Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control

This paper presents a haptic teleoperation system that enables therapists to remotely guide patients using an arm exoskeleton, enhancing ...

arXiv - Machine Learning · 4 min ·
[2602.21756] Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing
Llms

[2602.21756] Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing

The paper presents Persona4Rec, a novel recommendation framework that utilizes offline reasoning with large language models (LLMs) to cre...

arXiv - Machine Learning · 4 min ·
[2602.21741] Robust Long-Form Bangla Speech Processing: Automatic Speech Recognition and Speaker Diarization
Machine Learning

[2602.21741] Robust Long-Form Bangla Speech Processing: Automatic Speech Recognition and Speaker Diarization

This article presents an end-to-end system for Bangla long-form speech recognition and speaker diarization, detailing significant challen...

arXiv - Machine Learning · 3 min ·
[2602.21620] Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners
Machine Learning

[2602.21620] Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners

This article revisits the Bertrand Paradox using a theoretical framework that incorporates no-regret learning strategies in a discrete pr...

arXiv - Machine Learning · 3 min ·
[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Llms

[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework

This paper presents the MAESTRO framework, which utilizes multi-agent large language models to discover high-performance single atom cata...

arXiv - Machine Learning · 3 min ·
[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Machine Learning

[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding

The paper presents ConformalHDC, a framework that integrates uncertainty quantification into hyperdimensional computing for improved neur...

arXiv - Machine Learning · 4 min ·
[2602.21436] Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
Machine Learning

[2602.21436] Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback

This paper presents an efficient uncoupled learning algorithm for bilinear saddle-point problems, achieving last-iterate convergence with...

arXiv - Machine Learning · 3 min ·
[2602.21265] ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning
Llms

[2602.21265] ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning

ToolMATH introduces a benchmark for evaluating tool-augmented language models in realistic multi-tool environments, focusing on long-hori...

arXiv - Machine Learning · 4 min ·
[2602.22179] Learning and Naming Subgroups with Exceptional Survival Characteristics
Machine Learning

[2602.22179] Learning and Naming Subgroups with Exceptional Survival Characteristics

The paper presents Sysurv, a novel non-parametric method for identifying subpopulations with exceptional survival characteristics, enhanc...

arXiv - Machine Learning · 3 min ·
[2602.21919] Learning in the Null Space: Small Singular Values for Continual Learning
Machine Learning

[2602.21919] Learning in the Null Space: Small Singular Values for Continual Learning

The paper presents NESS, a novel continual learning method that leverages small singular values to maintain orthogonality in weight updat...

arXiv - Machine Learning · 4 min ·
[2602.21693] TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
Machine Learning

[2602.21693] TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts

The paper introduces TiMi, a novel approach that enhances time series forecasting by integrating multimodal data through a Mixture of Exp...

arXiv - Machine Learning · 4 min ·
[2602.21680] Hierarchical Lead Critic based Multi-Agent Reinforcement Learning
Machine Learning

[2602.21680] Hierarchical Lead Critic based Multi-Agent Reinforcement Learning

This paper presents a novel Hierarchical Lead Critic (HLC) architecture for Multi-Agent Reinforcement Learning (MARL), enhancing coordina...

arXiv - Machine Learning · 3 min ·
[2602.21674] Error-awareness Accelerates Active Automata Learning
Machine Learning

[2602.21674] Error-awareness Accelerates Active Automata Learning

The paper discusses how error-awareness can enhance Active Automata Learning (AAL) algorithms, enabling them to learn more efficiently fr...

arXiv - Machine Learning · 3 min ·
[2602.21634] AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction
Machine Learning

[2602.21634] AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction

AgentLTV introduces an agent-based framework for automated Lifetime Value (LTV) prediction, enhancing model discovery and performance in ...

arXiv - Machine Learning · 4 min ·
[2602.21565] Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
Machine Learning

[2602.21565] Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation

This article presents a novel approach to using pre-trained GFlowNets for multi-objective generation without the need for additional trai...

arXiv - Machine Learning · 3 min ·
[2602.21546] Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
Machine Learning

[2602.21546] Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling

This article presents a novel approach to the Flexible Job Shop Problem (FJSP) using a state-space model called Mamba, which improves eff...

arXiv - Machine Learning · 4 min ·
[2602.21467] Geometric Priors for Generalizable World Models via Vector Symbolic Architecture
Machine Learning

[2602.21467] Geometric Priors for Generalizable World Models via Vector Symbolic Architecture

This article presents a novel approach to world modeling in AI using Vector Symbolic Architecture (VSA) to enhance generalization and int...

arXiv - Machine Learning · 4 min ·
[2602.21340] HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
Machine Learning

[2602.21340] HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models

The paper introduces the HiPPO Zoo, a framework enhancing state space models with explicit memory mechanisms for improved interpretabilit...

arXiv - Machine Learning · 4 min ·
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