Verbal-R3 uses a verbal reranker to generate analytic narratives that guide retrieval and reasoning in LLMs, achieving SOTA results on complex QA benchmarks.
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
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PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
citing papers explorer
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Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
Verbal-R3 uses a verbal reranker to generate analytic narratives that guide retrieval and reasoning in LLMs, achieving SOTA results on complex QA benchmarks.
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Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
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LLM4C2Rust: Large Language Models for Automated Memory-Safe Code Transpilation
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.