Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial source material, they can alter the investment judgment supported by the original source. We frame this problem as information fidelity: compression loses fidelity when it changes the decision induced by the source. In agentic systems, such losses may recur across intermediate steps and amplify throughout the decision process. Across financial filings and earnings-call transcripts, we find that LLM-based compression can produce fluent and factually plausible compressed contexts that nevertheless alter downstream decisions. We analyze two diagnostic patterns associated with fidelity loss: decontextualization, where salient evidence is retained but separated from the caveats and contextual qualifiers needed for correct interpretation, and model dependency, where different compressors expose different views of the same source. We then propose Agentic Context Compression, which generates multiple candidate compressions and audits their disagreements against the original source. Our results suggest that financial compression should be evaluated not only by efficiency or factuality, but also by its ability to preserve decision-relevant context.

Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in textual data, leaving potentially informative signals under-explored. Large language models (LLMs) offer a promising alternative as they are able to extract nuanced semantics. In this paper, we ask whether semantic signals extracted by LLMs from corporate disclosures predict abnormal returns (alpha) beyond broad market movements and common risk factors. We introduce a simple framework, *LLM as extractor, embedding as ruler*, which extracts context-aware, metric-focused textual spans and quantifies semantic changes across consecutive disclosure periods using embedding similarity. This allows us to measure the degree of metric shifting - how much firms move away from previously emphasized metrics, referred to as moving targets. In portfolio and cross-sectional regression tests against a recent traditional baseline, our method yields a moving-targets signal that delivers significant risk-adjusted alpha. Qualitative analysis suggests that these gains stem from preserving contextual qualifiers and filtering out non-metric terms that traditional approaches often miss.