AI Agents

Stop Overcomplicating AI Workflows. This Is the Simple Framework

I’ve been working on building an agentic AI workflow system for business use cases and one thing became very clear very quickly. This is ...

The "Jarvis on day one" trap: why trying to build one AI agent that does everything costs you months

Something I've been thinking about after spending a few months actually trying to build my own AI agent: the biggest trap in this space i...

NeuBird AI Raises $19.3 Million To Scale Agentic AI

[2412.18899] GAI: Generative Agents for Innovation

The paper explores GAI, a framework for generative agents that enhances collective reasoning to foster innovation, evaluated through a ca...

[2602.17658] MARS: Margin-Aware Reward-Modeling with Self-Refinement

The paper presents MARS, a novel margin-aware reward modeling framework that enhances training efficiency by focusing on ambiguous prefer...

[2602.17641] FAMOSE: A ReAct Approach to Automated Feature Discovery

The paper presents FAMOSE, a novel framework that utilizes the ReAct paradigm for automated feature discovery in machine learning, enhanc...

[2602.17632] SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

The paper presents SMAC, a novel offline reinforcement learning method that enhances the transition from offline to online learning witho...

[2602.17616] Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs

The paper presents VCPO, a method to stabilize off-policy reinforcement learning for large language models, addressing high variance issu...

[2602.17605] Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

This paper presents a novel framework for geospatial discovery that integrates active learning and online meta-learning, focusing on rele...

[2602.17550] MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning

The paper presents MASPO, a novel framework that addresses inefficiencies in existing Reinforcement Learning with Verifiable Rewards (RLV...

[2602.17526] The Anxiety of Influence: Bloom Filters in Transformer Attention Heads

This article explores how certain transformer attention heads act as membership testers, identifying token repetition across various lang...

[2602.17410] Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers

This paper presents ILRec, a novel framework that enhances LLM-based recommendation systems by utilizing self-hard negative signals from ...

[2602.17395] SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery

The paper presents SpectralGCD, a novel approach for Generalized Category Discovery (GCD) that enhances multimodal learning by efficientl...

[2602.17394] Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks

The paper presents SIREN, an AI framework for enhancing UAV-assisted emergency networks by converting voice communications into structure...

[2602.17345] What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?

This paper explores vulnerabilities in embodied AI systems, highlighting the inadequacy of existing analyses focused solely on LLMs or CP...

[2602.17315] Flickering Multi-Armed Bandits

The paper introduces Flickering Multi-Armed Bandits (FMAB), a new framework that adapts the set of available actions based on previous ch...

[2602.17271] Federated Latent Space Alignment for Multi-user Semantic Communications

This paper presents a novel approach to federated latent space alignment in multi-user semantic communications, addressing semantic misma...

[2602.17242] TAPO-Structured Description Logic for Information Behavior: Procedural and Oracle-Based Extensions

The paper introduces TAPO-Structured Description Logic (TAPO--DL), a formal framework that models information behavior through procedural...

[2602.17213] Extending quantum theory with AI-assisted deterministic game theory

This paper presents an AI-assisted framework for predicting outcomes of complex quantum experiments by integrating deterministic game the...

[2602.17185] The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions

This article explores how the linguistic expressions of personality in conversational agents (CAs) influence user perceptions and decisio...

[2602.17176] Universal Fine-Grained Symmetry Inference and Enforcement for Rigorous Crystal Structure Prediction

This paper presents a novel approach to crystal structure prediction by utilizing large language models for fine-grained symmetry inferen...

[2602.17171] In-Context Learning in Linear vs. Quadratic Attention Models: An Empirical Study on Regression Tasks

This study compares in-context learning (ICL) performance between linear and quadratic attention models on regression tasks, highlighting...

[2602.17098] Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization

This article presents a comparative study of Deep Reinforcement Learning (DRL) and Mean-Variance Optimization (MVO) for optimal portfolio...