✨EpochX: Building the Infrastructure for an Emergent Agent Civilization

📝 Summary:
EpochX is a credits-native marketplace infrastructure designed for human-agent production networks. It enables scalable task delegation and verification, generating reusable skills and workflows. This system fosters cumulative improvement and durable human-agent collaboration through economic inc...

🔹 Publication Date: Published on Mar 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27304
• PDF: https://arxiv.org/pdf/2603.27304
• Project Page: https://epochx.cc
• Github: https://github.com/QuantaAlpha/EpochX

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#AIAgents #HumanAICooperation #AIInfrastructure #AIEconomics #EmergentAI

✨ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers

📝 Summary:
OpenClaw agents face critical security vulnerabilities due to extensive operational privileges. ClawKeeper provides comprehensive real-time protection using skill-based, plugin-based, and novel watcher-based mechanisms for state verification and intervention.

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24414
• PDF: https://arxiv.org/pdf/2603.24414
• Project Page: https://huggingface.co/datasets/xunyoyo/clawkeeper
• Github: https://github.com/SafeAI-Lab-X/ClawKeeper

✨ Datasets citing this paper:
• https://huggingface.co/datasets/xunyoyo/clawkeeper

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#AISafety #AgentSecurity #AIagents #Cybersecurity #AIResearch

✨Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time

📝 Summary:
Researchers analyzed AI coding agent contributions to open source projects. They found increasing agent activity but higher code churn over time compared to human-authored code.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00917
• PDF: https://arxiv.org/pdf/2604.00917
• Project Page: https://arxiv.org/html/2604.00917v1

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#AIAgents #SoftwareEngineering #OpenSource #CodeQuality #AIResearch

✨Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw

📝 Summary:
A real-world safety analysis of the personal AI agent OpenClaw reveals significant vulnerabilities due to its broad system access. Attacks targeting its Capability, Identity, or Knowledge CIK dimensions drastically increase success rates, and current defenses are insufficient, indicating inherent...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04759
• PDF: https://arxiv.org/pdf/2604.04759
• Project Page: https://ucsc-vlaa.github.io/CIK-Bench/
• Github: https://github.com/UCSC-VLAA/CIK-Bench

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#AISafety #Cybersecurity #AIAgents #Vulnerability #AIsecurity

✨ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces

📝 Summary:
ClawsBench evaluates LLM productivity agents in realistic workflows with mock services, assessing capability and safety. It shows agents achieve 39-64% task success but also 7-33% unsafe actions, identifying recurring patterns.

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05172
• PDF: https://arxiv.org/pdf/2604.05172
• Project Page: https://clawsbench.com/
• Github: https://github.com/benchflow-ai/ClawsBench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/benchflow/ClawsBench

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#LLM #AIAgents #AISafety #Benchmarking #AIResearch

✨GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows

📝 Summary:
GTA-2 is a new benchmark for General Tool Agents, covering both atomic and real-world, open-ended workflows. It shows frontier models struggle significantly, especially on workflows. The study emphasizes that execution frameworks are crucial for performance, more so than just model capacity.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15715
• PDF: https://arxiv.org/pdf/2604.15715
• Github: https://github.com/open-compass/GTA

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#AIAgents #BenchmarkingAI #LLMs #AIWorkflows #AIResearch

✨AgentSearchBench: A Benchmark for AI Agent Search in the Wild

📝 Summary:
AgentSearchBench is a new benchmark for finding suitable AI agents using execution-grounded performance signals from nearly 10,000 real-world agents. It shows that description-based similarity is insufficient, and lightweight behavioral signals significantly improve agent ranking.

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22436
• PDF: https://arxiv.org/pdf/2604.22436

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#AI #AIAgents #Benchmarking #AgentSearch #MachineLearning

✨Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents

📝 Summary:
Memanto introduces a universal, typed semantic memory layer for AI agents that bypasses complex semantic graphs. It uses an information-theoretic search engine for fast, overhead-free retrieval. This system achieves state-of-the-art accuracy on benchmarks with a single query and no ingestion cost.

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22085
• PDF: https://arxiv.org/pdf/2604.22085
• Project Page: https://memanto.ai/
• Github: https://github.com/moorcheh-ai/memanto-evaluation

✨ Datasets citing this paper:
• https://huggingface.co/datasets/moorcheh/memanto-longmem-results
• https://huggingface.co/datasets/moorcheh/memanto-locomo-results

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#AI #SemanticMemory #InformationRetrieval #AIAgents #MachineLearning

✨Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis

📝 Summary:
DataPRM, a new environment-aware process reward model, enhances LLM reasoning in dynamic data analysis. It actively detects silent errors and distinguishes error types, achieving superior benchmark performance.

🔹 Publication Date: Published on Apr 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24198
• PDF: https://arxiv.org/pdf/2604.24198

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#LLM #RewardModeling #DataAnalysis #AIagents #MachineLearning

✨AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery

📝 Summary:
AutoResearchBench is a new benchmark evaluating AI agents on complex scientific literature discovery tasks. It features deep and wide research, demanding in-depth comprehension and fine-grained information use. Even powerful LLMs show very low accuracy, highlighting the significant challenge for ...

🔹 Publication Date: Published on Apr 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.25256
• PDF: https://arxiv.org/pdf/2604.25256
• Project Page: https://cheryou.github.io/autoresearchbench.github.io/
• Github: https://github.com/CherYou/AutoResearchBench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Lk123/AutoResearchBench

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#AI #LLMs #Benchmarking #ScientificResearch #AIAgents