#prompt-optimization News & Analysis

8 articles tagged with #prompt-optimization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

8 articles

AIBullisharXiv – CS AI · 4d ago7/10

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AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs

AlphaLab is an autonomous research system using frontier LLMs to automate experimental cycles across computational domains. Without human intervention, it explores datasets, validates frameworks, and runs large-scale experiments while accumulating domain knowledge—achieving 4.4x speedups in CUDA optimization, 22% lower validation loss in LLM pretraining, and 23-25% improvements in traffic forecasting.

🧠 GPT-5🧠 Claude🧠 Opus

AIBullisharXiv – CS AI · Mar 46/104

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MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks

Researchers introduce MASPOB, a bandit-based framework that optimizes prompts for Multi-Agent Systems using Graph Neural Networks to handle topology-induced coupling. The system reduces search complexity from exponential to linear while achieving state-of-the-art performance across benchmarks.

AIBullisharXiv – CS AI · Feb 277/106

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Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning

Researchers developed a hierarchical multi-agent LLM framework that significantly improves multi-robot task planning by combining natural language processing with classical PDDL planners. The system uses prompt optimization and meta-learning to achieve success rates of up to 95% on compound tasks, outperforming previous state-of-the-art methods by substantial margins.

$COMP

AINeutralarXiv – CS AI · 3d ago6/10

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When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies

Researchers demonstrate that large language models can extract predictive features from financial news with valid intermediate signals (Information Coefficient >0.15), yet these features fail to improve reinforcement learning trading agents during macroeconomic shocks. The findings reveal a critical gap between feature-level validity and downstream policy robustness, suggesting that valid signals alone cannot guarantee trading performance under distribution shifts.

AINeutralarXiv – CS AI · Mar 96/10

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ContextBench: Modifying Contexts for Targeted Latent Activation

Researchers have developed ContextBench, a new benchmark for evaluating methods that generate targeted inputs to trigger specific behaviors in language models. The study introduces enhanced Evolutionary Prompt Optimization techniques that better balance effectiveness in activating AI model features while maintaining linguistic fluency.

AINeutralarXiv – CS AI · Mar 55/10

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Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

Researchers present a blueprint for evaluating and optimizing multi-agent conversational shopping assistants, addressing challenges in multi-turn interactions and tightly coupled AI systems. The paper introduces evaluation rubrics and two prompt-optimization strategies including a novel Multi-Agent Multi-Turn GEPA approach for system-level optimization.

AIBullisharXiv – CS AI · Mar 36/106

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Retrieval, Refinement, and Ranking for Text-to-Video Generation via Prompt Optimization and Test-Time Scaling

Researchers introduce 3R, a new RAG-based framework that optimizes prompts for text-to-video generation models without requiring model retraining. The system uses three key strategies to improve video quality: RAG-based modifier extraction, diffusion-based preference optimization, and temporal frame interpolation for better consistency.

AIBullisharXiv – CS AI · Feb 276/105

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Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models

Researchers demonstrated that prompt optimization using Genetic-Pareto (GEPA) significantly improves language models' ability to detect errors in medical notes. The technique boosted accuracy from 0.669 to 0.785 with GPT-5 and from 0.578 to 0.690 with Qwen3-32B, achieving state-of-the-art performance on medical error detection benchmarks.