EverMine: Dissecting the Self-Evolution of Research Capabilities in Long-Horizon Alpha Research

arXiv:2609.33524 · cs.AI, q-fin.ST · Submitted 2026-09-27 · Read on arXiv

cs.AI, q-fin.ST

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/deepseek-ai/deepseek-harness

Terminology

• alpha — 39× in this paper · explained in 1 episode
• Core — 6× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Self-Evolving Agents — 4× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Agentic Context Engineering — 2× in this paper
• Harness-Aware Self-Evolving — 2× in this paper
• Agent Harness — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Information Coefficient — 1× in this paper
• KV cache — 1× in this paper · explained in 3 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
• DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
• AQuA: Recursively Self-Improving Quantitative Trading Research Agents
• QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
• R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
• AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment
• Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions
• Agentic Empirical Asset Pricing: Methodological Foundations
• Reflexion: Language Agents with Verbal Reinforcement Learning
• FactorMiner: A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discovery
• Qlib: An AI-oriented Quantitative Investment Platform
• AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents
• ExpeL: LLM Agents Are Experiential Learners

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