3 papers
cs.CL2026
ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces
Jinu Lee, Shivam Agarwal, Amruta Parulekar +3
Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the re…
cs.IR2025
Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking
Jerry Huang, Siddarth Madala, Cheng Niu +2
Reranking algorithms have made progress in improving document retrieval quality by efficiently aggregating relevance judgments generated by large language models (LLMs). However, i…
cs.CL2025
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning
Jerry Huang, Siddarth Madala, Risham Sidhu +4
Retrieval-augmented generation (RAG) systems rely on retrieval models for identifying relevant contexts and answer generation models for utilizing those contexts. However, retrieve…