This work compares the trade-offs between retrieval-augmented generation, prefix caching, and fine-tuning in large language models to show that when the knowledge is stable and matches the questions asked, fine-tuning is the greenest, fastest, and most accurate, because it produces shorter answers.
LiteRAG is presented, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction and attains the highest overall quality among the evaluated methods.
Daniel Alejandro Coll Tejeda, Pedro García López, Daniel Barcelona-Pons· 0 citations
This work compares country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct across Qwen, Llama, and Gemma to separate early readability, natural strength, causal steering, and later content dependence.
Wen-Lin Wei, Yuan Fang, Ren-He Jiang et al.· 0 citations
The results indicate that front-loading code understanding into a reusable, codebase-specialised vector store enables small local models to deliver grounded and repository-specific answers, making the agent well suited as a privacy-preserving development tool for in-house scientific codebases.
Aaron Nobile, Andreas Adelmann, Mohsen Sadr· 0 citations
For the well-specified operations problems the authors study, a single untuned LLM query can already produce algorithms competitive with specialized methods, suggesting that frontier LLMs can be a serious empirical baseline for algorithm design in well-specified OR problems.
A controlled comparison of representation-based (KV-cache based) and parametric (fine-tuning-based) adaptation methods on five knowledge-intensive benchmarks shows that in the oracle setting, Cartridges are the most accurate injection method at nearly every storage budget, outperforming parametric methods by 10 points.
RAILS is presented, a retrieval-augmented incremental LLM clusterer that turns clustering into a simple loop over a growing label pool and scales through document batching with bounded concurrency.
Armin Oliya, Aleksandra Sawczuk, Radosław Białobrzeski· 0 citations
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