Textual Salience Tips the Balance: Modulating Long-Context Memory Retrieval in LLMs

Reliable retrieval from long contexts is fundamental for LLM agents to reuse interaction histories and prior decisions. Yet even when target information remains available, retrieval can fail when similar memories compete. We ask whether short textual salience cues can shift retrieval priority among competing memories. To isolate this effect from world knowledge and semantic associations, we construct a controlled key–value framework with random bindings and similar-key distractors, evaluating five open-weight LLMs. We test three affectively framed cues—threat, reward, empathy—together with a non-valenced importance cue. Most errors match distractor values already present in context, indicating confusion among competing bindings rather than simple forgetting. Under this interference, well-placed cues improve retrieval accuracy by roughly 30–60 percentage points in most models, although the effect depends on model and placement. Large gains also occur for importance, and no affective family consistently dominates it, indicating a shared textual-salience effect across affectively framed and non-valenced cues. The gains generally do not broadly impair unmarked or updated memories. Internally, target–distractor selection emerges rapidly in mid-to-late layers, where cue-induced differences are strongest; attention shifts toward target values, and MLP interventions at selected layers increase target-value generation probability. Textual salience cues thus act as memory-priority signals that reshape selection among competing memories.