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This paper introduces Lodestar, a method that uses reinforcement learning to train a short polarizer prompt string that helps a frozen LLM avoid being misled by misleading retrieved passages in RAG question answering. It improves F1 and exact match scores across five QA benchmarks compared to existing entropy-based selection rules.
The paper introduces Hierarchical Self-Improvement (HSI), a framework that enhances frozen LLM agents by evolving task-specific harnesses through hierarchical self-modification, achieving substantial gains on moderate tasks while being limited by feedback quality and backbone capabilities.
IRIS is a training-free framework that uses frozen large language models to construct reusable identity representations for entities in knowledge graphs, enabling efficient entity alignment across different KGs without pair-dependent processing.
GRAB uses a GNN encoder to convert relational tables into latent tokens for frozen LLMs, achieving significant performance gains in multi-table question answering.
This paper introduces PYTHALAB-MERA, an external controller for frozen local LLMs that uses validation-grounded memory and retrieval to improve coding agent performance. It demonstrates superior success rates in strict validation tasks compared to self-refinement baselines by leveraging execution feedback and temporal difference learning.