7 papers
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development
Jie Yang, Honglin Guo, Li Ji +11
The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-…
Multi-hop Reasoning via Early Knowledge Alignment
Yuxin Wang, Shicheng Fang, Bo Wang +4
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for Large Language Models (LLMs) to address knowledge-intensive queries requiring domain-specific or up-to-d…
Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge
Qi Luo, Xiaonan Li, Junqi Dai +2
Retrieval-Augmented Generation has shown remarkable results to address Large Language Models' hallucinations, which usually uses a large external corpus to supplement knowledge to…
Towards Global Retrieval Augmented Generation: A Benchmark for Corpus-Level Reasoning
Qi Luo, Xiaonan Li, Tingshuo Fan +2
Retrieval-augmented generation (RAG) has emerged as a leading approach to reducing hallucinations in large language models (LLMs). Current RAG evaluation benchmarks primarily focus…
MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval
Qi Luo, Xiaonan Li, Yuxin Wang +4
Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data, resulting in factual inaccuracies and weak adaptability to new…
REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
Le Zhang, Bo Wang, Xipeng Qiu +2
We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, significantly improving both performance and…