Raven: The Harness of Harnesses for Composable Agentic Intelligence

arXiv:2609.33439 · cs.AI, cs.CL, cs.GT, cs.MA, cs.NE · Submitted 2026-09-27 · Read on arXiv

cs.AI, cs.CL, cs.GT, cs.MA, cs.NE

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/EverMind-AI/Raven

Terminology

• Benchmark — 56× in this paper · explained in 2 episodes
• Trace — 28× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Verification — 17× in this paper · explained in 2 episodes
• Adaptation — 15× in this paper · explained in 1 episode
• Clustering — 13× in this paper · explained in 2 episodes
• Provenance — 13× in this paper · explained in 4 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Agent Memory — 9× in this paper · explained in 1 episode
• AI Agents — 7× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Multi-agent Collaboration — 7× in this paper · explained in 2 episodes
• Multi-Agent System — 6× in this paper · explained in 6 episodes
• Agent Harness — 5× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• LLM judge — 5× in this paper · explained in 2 episodes
• Multi-Agent Orchestration Benchmark — 5× in this paper
• Perplexity — 5× in this paper · explained in 3 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Procedural Knowledge — 4× in this paper · explained in 2 episodes
• Agent Skills — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Admissible Space — 2× in this paper · explained in 1 episode
• Failure Modes — 2× in this paper · explained in 3 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Meeting of the Association for Computational Linguistics — 2× in this paper
• Partial Order Accuracy — 2× in this paper
• Task Decomposition — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Algorithm — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Agent Client Protocol — 1× in this paper
• Agentic System — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Formal Methods — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes

Sources

• Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment
• Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction
• Humanity's Last Exam
• xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations
• Evaluating Large Language Models Trained on Code
• PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation
• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
• Memory in the Age of AI Agents
• Towards a Science of Scaling Agent Systems
• Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents
• SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
• JobBench: Aligning Agent Work With Human Will
• MemOS: A Memory OS for AI System
• AgentBench: Evaluating LLMs as Agents
• HarnessBank: Semantic Gene-Bank Search with Gated Verification for Agent-Harness Self-Evolution
• Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents

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