Morning Minute: AI Agents Cut BTC Quantum Attack Benchmark by 86%

🤖 AI/ML Daily Signal — Morning Edition 10 Sep 2026 · 07:24 UTC ──────────────────────────────── 🔥 1. AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning 🔬 arXiv cs.LG (Machine Learning) · Score: 9/10 AhaBench introduces a benchmark evaluating whether language agents can effectively learn from prior experience across long-horizon tasks, including follow-up questions, example reuse, and tool feedback adaptation. This is essential for practitioners building production agents that need to improve performance over time. Read more → ⭐ 2. One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning 🔬 arXiv cs.LG (Machine Learning) · Score: 8/10 Proposes adaptive anisotropic learning rates for LoRA instead of uniform rates, improving fine-tuning efficiency for large language models. This addresses a fundamental limitation in the most widely-used parameter-efficient fine-tuning method practitioners rely on today. Read more → ⭐ 3. ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs 🔬 arXiv cs.LG (Machine Learning) · Score: 8/10 ACE consolidates separate per-expert adapters into shared param