Awesome AI Agent Papers

A curated collection of research papers published in 2026 and sourced from arXiv, covering core topics from the AI agent ecosystem like multi-agent coordination, memory & RAG, tooling, evaluation & observability, and security.

Whether you're an AI engineer building agent systems, a researcher exploring new architectures, or a developer integrating LLM agents into products, these papers help you stay on top of what's actually working, what's breaking, and where the field is heading. Updated weekly from arXiv.

Why this list exists

Hundreds of papers are published on arXiv every week, and a growing number of them touch on AI agents. We go through them all, filter the ones that are directly relevant to the AI agent ecosystem, and categorize them so you don't have to. This list only includes papers published from January 2026 onward.

Table of Contents

• Multi-Agent (53)
• Memory & RAG (57)
• Eval & Observability (80)
• Agent Tooling (95)
• AI Agent Security (82)

Paper	arXiv ID
PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation - A persona-conditioned framework that predicts how a specific user would answer UI/UX evaluation questions and explains why in natural language. Trained via contrastive reflection fine-tuning and reflective prompt evolution, reaching human-level realism and generalizing to unseen questions and personas.
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing - A multi-agent pipeline that reads a PDE problem description in plain text and writes, debugs, and validates a classical numerical solver end-to-end. Generates spectral and finite-difference code (no neural networks), scoring ~6 orders of magnitude below FNO and CodePDE baselines.
Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation - Evaluates recommender systems via agent-RS interactions.
CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery - Introduces long-running multi-agent systems that self-evolve via shared persistent memory, asynchronous execution, and heartbeat-based interventions; 3–10× higher improvement rates than fixed evolutionary-search baselines on 10 math/algorithmic/systems tasks.
DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching - Investigates dynamically rewiring agent-to-agent connections at each reasoning round via semantic matching instead of fixed communication topologies.
RuleSmith: Multi-Agent LLMs for Automated Game Balancing - Explores automated game balancing by combining multi-agent LLM self-play with Bayesian optimization on a civ-style game.
CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction - Examines how conformal prediction can filter noisy inter-agent messages to improve multi-robot coordination.