[upstream-sync] README.md from VoltAgent/awesome-ai-agent-papers@a2ffba2b [catalogue]

2026-08-21 16:40:10 +00:00
1 changed files with 25 additions and 3 deletions
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 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/a2ffba2b/README.md
+upstream_sha: a2ffba2b
+imported_at: 2026-08-21
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -70,6 +70,27 @@ Hundreds of papers are published on arXiv every week, and a growing number of th

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+You ship products with AI, but every launch still dies quietly because nobody posts about it. [EveryFeed](https://everyfeed.ai/) plugs your AI assistant into a social workspace that drafts, schedules, and publishes across 35+ channels — no agency, no marketing hire.
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+<a href="https://everyfeed.ai/">
+<img src="https://cdn.voltagent.dev/awesome-repo/everyfeed-social.png" alt="everyfeed"  /><br/>
+</a>
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+<br/>
+<br/>
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+Stop building from a blank page. [LaunchKit](https://launchkit.getdesign.md/) gives your AI coding assistant a complete, working product to start from — websites, startups, and web apps that are clickable on day one.
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+<a href="https://launchkit.getdesign.md/">
+<img src="https://cdn.voltagent.dev/awesome-repo/new-launchkit.png" alt="launchkit"  /><br/>
+</a>
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+<br/>
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 <details open id="multi-agent">
 <summary><h3 style="display:inline">Multi-Agent (54)</h3></summary>

@@ -82,6 +103,7 @@ 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. | <a href="https://arxiv.org/abs/2604.19792"><img src="https://img.shields.io/badge/arXiv-2604.19792-b31b1b.svg" alt="arXiv" /></a> |
 | **[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. | <a href="https://arxiv.org/abs/2602.17607"><img src="https://img.shields.io/badge/arXiv-2602.17607-b31b1b.svg" alt="arXiv" /></a> |
 | **[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. | <a href="https://arxiv.org/abs/2604.09549"><img src="https://img.shields.io/badge/arXiv-2602.06039-b31b1b.svg" alt="arXiv" /></a> |
+| **[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. | <a href="https://arxiv.org/abs/2608.16897"><img src="https://img.shields.io/badge/arXiv-2608.16897-b31b1b.svg" alt="arXiv" /></a> |
 | **[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. | <a href="https://arxiv.org/abs/2604.01658"><img src="https://img.shields.io/badge/arXiv-2604.01658-b31b1b.svg" alt="arXiv" /></a> |
 | **[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. | <a href="https://arxiv.org/abs/2602.06039v1"><img src="https://img.shields.io/badge/arXiv-2602.06039-b31b1b.svg" alt="arXiv" /></a> |
 | **[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. | <a href="https://arxiv.org/abs/2602.06232v1"><img src="https://img.shields.io/badge/arXiv-2602.06232-b31b1b.svg" alt="arXiv" /></a> |