HiLight: A Framework for Highlighting Evidence in Long Contexts for Large Language Models
Researchers have introduced HiLight, a reinforcement learning framework that trains a lightweight 'Emphasis Actor' to insert highlight tags around critical evidence spans in long texts before passing them to a frozen large language model (LLM) for reasoning. The system addresses a known weakness of LLMs — missing decisive information buried in lengthy or noisy contexts — without compressing or rewriting the original input. The approach requires no human-labeled evidence data and transfers zero-shot to unseen LLM families, suggesting it learns generalizable evidence structure.
HiLight, presented in a preprint on arXiv, decouples the task of evidence selection from downstream reasoning by pairing a trainable Emphasis Actor with an unmodified, frozen LLM Solver. Rather than summarizing or rewriting context — methods that risk discarding or distorting key information — the Actor inserts minimal highlight tags around pivotal text spans, leaving the original context intact. The Actor is trained using reinforcement learning with only the Solver's task-performance reward as a signal, meaning no ground-truth evidence annotations are needed and the Solver itself requires no modification or access. The framework was evaluated on sequential recommendation and long-context question answering tasks, outperforming strong prompt-based and automated prompt-optimization baselines in both settings. Notably, the learned highlighting policy transferred zero-shot to both smaller and larger LLM families not seen during training, including an API-based model, indicating the Actor generalizes beyond the specific backbone used for training. The paper was submitted in April 2026 and revised in June 2026, and has not yet undergone formal peer review.
What's missing
As a preprint, HiLight has not undergone peer review. The paper does not report ablations on how performance scales with context length or noise level, nor does it address computational overhead of the Actor at inference time. It is also unclear how the framework performs on domains beyond recommendation and question answering, or whether highlight tag formatting could itself introduce artifacts for certain LLM tokenizers.
What different sources said
- arXiv cs.AICenter
Learning Evidence Highlighting for Frozen LLMs
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