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Automated micro-step prompt refinement can more than double an agent's domain-specific reasoning and scientific rigor scores without fine-tuning underlying model weights.
Achieving superior performance with one-third the resources, Qwen3.8-Flash-Next redefines efficiency in large-scale language models.
A previously observed performance gain in interactive agents disappears under rigorous confirmation testing, challenging the reliability of existing evaluation benchmarks.
LiveMem enables LLMs to retain crucial information across context changes, outperforming existing memory methods and ensuring continuity in long-running interactions.
