diff --git a/upstream/VoltAgent-awesome-ai-agent-papers/catalogue/README.md b/upstream/VoltAgent-awesome-ai-agent-papers/catalogue/README.md index 373b1f2..63e0529 100644 --- a/upstream/VoltAgent-awesome-ai-agent-papers/catalogue/README.md +++ b/upstream/VoltAgent-awesome-ai-agent-papers/catalogue/README.md @@ -2,9 +2,9 @@ title: "Awesome AI Agent Papers" task: "" lineage_type: import -upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/29037d57/README.md -upstream_sha: 29037d57 -imported_at: 2026-08-17 +upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/b52acd72/README.md +upstream_sha: b52acd72 +imported_at: 2026-09-02 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -33,9 +33,6 @@ validated: false [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) ![Papers Count](https://img.shields.io/badge/Research%20Papers-364+-b31b1b) ![Last Update](https://img.shields.io/github/last-commit/VoltAgent/awesome-ai-agent-papers?label=Last%20update) - - VoltAgent - [![Discord](https://img.shields.io/discord/1361559153780195478.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://s.voltagent.dev/discord) @@ -54,6 +51,16 @@ A curated collection of research papers **published in 2026** and sourced from a 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. +## Sponsors + +| | | +| :-: | :-- | +| Crawlbase | [Crawlbase](https://crawlbase.com/?utm_source=awesome-ai-agent-papers&utm_medium=sponsorship&utm_campaign=voltagent_2026q3&utm_content=readme_listing) is web data infrastructure trusted by 70,000+ developers. Its Crawling API, MCP server, and integrations give AI agents live access to any webpage — with JavaScript rendering, proxy rotation, and anti-bot protection. | + +
+ +Become a Sponsor + ### 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. @@ -70,6 +77,27 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
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Multi-Agent (54)

 @@ -82,6 +110,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th | **[OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review](https://arxiv.org/pdf/2604.19792)** - Presents a decentralized AI peer-review platform where autonomous agents publish, score, verify references, and preserve research papers across a multi-layer storage and retrieval architecture. | arXiv | | **[AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing](https://arxiv.org/pdf/2602.17607)** - 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. | arXiv | | **[Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://arxiv.org/abs/2604.09549)** - Evaluates recommender systems via agent-RS interactions. | arXiv | +| **[CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents](https://arxiv.org/pdf/2608.16897)** - Simulates urban behavior and city dynamics with intention-driven LLM agents that learn habits and preferences via textual adapters, aligning to real population statistics at scale. | arXiv | | **[CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://arxiv.org/pdf/2604.01658)** - 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. | arXiv | | **[DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching](https://arxiv.org/pdf/2602.06039v1)** - Investigates dynamically rewiring agent-to-agent connections at each reasoning round via semantic matching instead of fixed communication topologies. | arXiv | | **[RuleSmith: Multi-Agent LLMs for Automated Game Balancing](https://arxiv.org/pdf/2602.06232v1)** - Explores automated game balancing by combining multi-agent LLM self-play with Bayesian optimization on a civ-style game. | arXiv |