Qurtoo
Short digests of research papers about prompting and agents: what each paper found, why it matters, and how to use it.
Benchmark that tests whether an LLM can take a real GitHub issue and a full repository and produce a passing code change. Went from 2% solve rate in 2023 to 70%+ by 2025 -- the clearest quantitative record of agentic coding progress we have.
Carlos E. Jimenez, John Yang, Alexander Wettig, et al.
Generalizes chain-of-thought by having the model explore multiple reasoning branches, score each, and prune. Dramatically better on puzzle-like problems (24, crosswords, creative writing with constraints) at the cost of 5-10x the tokens.
Shunyu Yao, Dian Yu, Jeffrey Zhao, et al.
The paper that named and benchmarked indirect prompt injection. Demonstrated end-to-end attacks against Bing Chat, GitHub Copilot Chat, and other production LLM integrations via poisoned web content, email, and code comments. The practical wake-up call for agent security.
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.
Introduced the Reason-Act-Observe loop: models that alternate between thinking and calling tools dramatically outperform models that only think OR only act. The founding paper of modern agentic LLM architectures.
Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.
Sample the model N times at nonzero temperature, take the mode of the final answers. Cheapest known reliability upgrade for discrete-answer tasks -- easily doubles accuracy on math reasoning at the cost of N-x compute.
Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.
Appending "let's think step by step" or showing worked-example reasoning in the prompt dramatically improves LLM accuracy on math and multi-step problems. The paper that named and formalized chain-of-thought. Still the cited reference for CoT despite being from 2022.
Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.
Fine-tune a giant model by training tiny adapter matrices alongside it, leaving the base frozen. Cuts training memory by 10-100x, lets you host one base model with many LoRAs for different tasks, runs on a single consumer GPU.
Edward J. Hu, Yelong Shen, Phillip Wallis, et al.
Coined the term RAG. Showed that combining a language model with retrieval over a knowledge base beats fine-tuning on knowledge-intensive tasks while staying updateable without retraining. The foundation of every modern document-chat, support bot, and enterprise search app.
Patrick Lewis, Ethan Perez, Aleksandara Piktus, et al.
