LoRA fine-tuning is standard for adapting LLMs to reranking, but it remains unclear where in the network task-specific relevance behavior is learned and what attention-level changes accompany that learning. Through ablation and attention experiments, we identify where LoRA attention updates to RankLLaMA improve performance and whether those gains coincide with interpretable relevance-oriented attention patterns such as lexical matching, rarity sensitivity, and query-document interaction. We find that given LoRA fine-tuned MLPs throughout the network, restricting LoRA attention updates to a compact mid-network region is sufficient for recovering over half of the performance gained by applying LoRA to all attention layers, and that omitting attention fine-tuning in this region hurts performance more than elsewhere in the network. Additionally, we show that regions where applying LoRA affects performance the most overlap with regions where fine-tuning increased attention to axiomatic IR features. Rarity sensitivity, document-query interaction, and several compositional features are highly correlated with gains in ranking performance. Our results support an interpretable, correlational account of how relevance-oriented behavior emerges during LoRA fine-tuning and point toward improved strategies for adapting rerankers.

Agentic language models solve tasks through reasoning, tool calls, observations, and terminal answers, exposing rich process information beyond the final answer. For practitioners, knowing which process patterns lead to success or failure is essential to diagnose agent errors and build better process reward models without relying on final-answer supervision. However, existing analyses often evaluate only final outcomes or, when examining traces, flatten them into homogeneous step sequences, fragmenting each reasoning round into disconnected steps. Our key insight is that an agent’s reasoning, action, and observation within a round are better treated as typed segments of a single underlying process state, rather than independent steps. We propose MULTI-VIEW-RA, a reasoning abstraction that represents each round through two complementary views: accumulated state and within-round change. Using unlabeled clustering and binary outcome labels, we apply MULTI-VIEW-RA to two practical use cases: diagnosing failures by surfacing the process states that separate correct from incorrect trajectories, and providing a transition-level correctness signal that is a candidate source of process supervision. Across Math, HotpotQA, and FEVER with multiple Qwen and Llama models, MULTI-VIEW-RA outperforms step-level explainability baselines in cases with sufficient success rates, with particularly strong gains on longer, tool-rich traces.