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Abashakashatsi bamenyekanisha EvolveTrade kubakozi bo mu bucuruzi bwa LLM

EvolveTrade ni urwego rwihinduranya rutuma imiterere nini yindimi LLM ishinzwe ubucuruzi ihuza imyitwarire yabo mugihe cyimihindagurikire yisoko no kunoza imikorere no gukomera.

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Source-provided image accompanying Researchers introduce EvolveTrade for self-evolving LLM trading agents
Inyandiko y'ibanzeInkomoko yanditse
Umwanditsi
arxiv.org
Ihuza ry'inkomoko
arxiv.orghttps://arxiv.org/abs/2609.17632
Ubwoko bw'inkomoko
Inyandiko y'ibanze - itangazo ryemewe, impapuro, dosiye, cyangwa urupapuro rwambere-dusoma mu buryo butaziguye.
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Tangira hano

Amagambo y'ingenzi

Ururimi runini (LLM)
Ururimi rwicyitegererezo rwahuguwe kumyandiko minini corpora kubyara no gusesengura inyandiko.
Sisitemu Byihuse
Amabwiriza-yibanze yibanze ashyiraho imyitwarire, politiki, nuburyo bwo gusubiza icyitegererezo.
Gukomera
Ubushobozi bwikitegererezo bwo gukomeza imikorere munsi yurusaku, guhinduranya, cyangwa inyongeramusaruro.
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Byagenze bite

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.

Ibisobanuro birambuye: arxiv.org β†—

Impamvu ari ngombwa

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

Uburyo bukoreshwa: Uburyo bukora

Shakisha ikoranabuhanga ryihishe inyuma yiri terambere.

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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Ibyo kureba

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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