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SSE-Bio proposes a structured self-evolving agent with a trainable retrieval policy to improve multi-hop biomedical reasoning, demonstrating significant performance gains over baselines.
This article introduces how Alloomi addresses memory and growth issues of AI Agents in enterprises through Holistic Context and Self-Evolving Agent technologies, enabling them to work like experienced employees.
The author shares three key design choices for building an open-source self-evolving agent: gated self-evolution with a fitness signal, security-focused architecture against prompt injection, and honest benchmarking with confidence intervals. The agent is Apache-2.0 licensed and still in alpha.
This paper introduces a self-evolving framework that uses an LLM-based agent to iteratively create and refine query rewriting rules for BM25 in legal case retrieval, outperforming non-evolutionary baselines on the LeCaRD-v2 benchmark without any parameter training.