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Penyelidik memperkenalkan EvolveTrade untuk ejen perdagangan LLM yang berkembang sendiri

EvolveTrade ialah rangka kerja yang berkembang sendiri yang membolehkan ejen perdagangan LLM model bahasa besar menyesuaikan tingkah laku mereka di bawah rejim pasaran yang berubah-ubah dan meningkatkan prestasi dan keteguhan mereka.

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Source-provided image accompanying Researchers introduce EvolveTrade for self-evolving LLM trading agents
Dokumen sumber utamaSumber direkodkan
Penerbit
arxiv.org
Pautan sumber
arxiv.orghttps://arxiv.org/abs/2609.17632
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Istilah utama

Model Bahasa Besar (LLM)
Model bahasa yang dilatih mengenai korpora teks besar-besaran untuk menjana dan menganalisis teks.
Gesaan Sistem
Arahan keutamaan tinggi yang menetapkan tingkah laku, dasar dan gaya tindak balas untuk model.
Kekukuhan
Keupayaan model untuk mengekalkan prestasi di bawah bunyi bising, peralihan atau input lawan.
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Apa yang berlaku

Researchers introduced EvolveTrade, a self-evolving framework for large language model LLM trading agents. The framework treats the of a tool-using trading agent as a text-parameterized policy, which is revised after each update interval using accumulated decision traces and realized portfolio feedback.

The framework treats the of a tool-using trading agent as a text-parameterized policy, which is revised after each update interval using accumulated decision traces and realized portfolio feedback.

This approach enables LLM trading agents to adapt their behavior under changing market regimes, improving their performance and .

The development of EvolveTrade has significant implications for the field of LLM trading agents, highlighting the importance of adapting the reusable procedure governing tool use to build more robust agents.

Butiran sumber: arxiv.org β†—

Mengapa ia penting

EvolveTrade enables LLM trading agents to adapt their behavior under changing market regimes improving their performance and . This is a key direction for building more robust LLM trading agents.

EvolveTrade improves the performance of LLM trading agents by enabling them to adapt their behavior under changing market regimes.

The framework achieves improved Sharpe Ratio and Cumulative Return over fixed-policy LLM baselines in most evaluated settings.

Behavioral analyses show that self-evolved policies increase code-mediated analysis and activate regime-relevant computations.

Case-level policy-to-return attributions trace how policy-induced allocation changes contribute to realized return differences.

Interactive Mechanism

Mekanisme Interaktif: Bagaimana Ia Berfungsi Sebenarnya

Terokai teknologi asas di sebalik pembangunan ini secara interaktif.

Agent Lifecycle Stage:
1
User Intent & Planning: "Audit customer refund request #4092 and settle payment."
2
Tool Calling: Emits structured JSON call crm_get_transaction(id='4092').
3
Guardrail & Verification:πŸ›‘οΈ Paused: High-value action requires human operator sign-off.
4
Final Settlement: Refund recorded, email receipt dispatched, and audit log stored.
Core takeaway: An AI agent is not just a language modelβ€”it is a closed loop of planning, tool invocation, and environment feedback. Production systems require self-healing retries and strict human approval guardrails.
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Apa yang perlu ditonton seterusnya

The development of EvolveTrade has significant implications for the field of LLM trading agents. It highlights the importance of adapting the reusable procedure governing tool use to build more robust agents.

The impact of EvolveTrade on the performance of LLM trading agents will be closely watched.

The framework's ability to adapt to changing market regimes will be a key area of focus.

The potential applications of EvolveTrade in other areas such as finance and economics will be explored.

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