FIRME：面向演化异构投资者知识的注册表锚定记忆图谱
FIRME: Registry-Anchored Memory Graphs for Evolving, Heterogeneous Investor Knowledge
AI 导读
研究者提出 FIRME（Fusion and Identity Registry for Memory Evolution），一种注册表锚定的记忆图谱，将新观测融合为持久的规范化关系，并保留来源、时间与上下文，用于跨来源、跨时期的投资者知识复用。
正文 · 原文
Abstract:Financial AI workflows that reuse investor knowledge need persistent memory that integrates observations across sources and periods without losing identity or provenance. In a knowledge graph, recurring observations may use different surface forms, fragmenting relationship histories and preventing evidence from accumulating. We introduce Fusion and Identity Registry for Memory Evolution (FIRME), a registry-anchored memory graph that reconciles incoming observations to persistent canonical relationships and retains their source, time, and context. Within periods, rule-based fusion links investor-call discussions to portfolio cash-flow signals, directly or through a fixed product-topic ontology. Across periods, canonical identities and persistent aliases direct recurring observations to the same relationship history; deterministic updates summarize reinforcement, inactivity, and reactivation. We evaluate FIRME on proprietary investor-call and cash-flow records and on S&P 500 earnings-call transcripts. Compared with a historical registry that retains canonical identities and semantic fallback but omits alias memory, FIRME increases final-period Correct Match Rate by 7.8-12.1 percentage points across four dataset-backend settings under a fixed-judge and fallback-review protocol. A blinded human audit of 200 incoming observations per setting finds Correct Match Rate gains of 9.0-16.5 points and reattachment-recall gains of 22.5-29.0 points, with higher observed merge precision in all four samples. A separate audit finds valid evidence paths for 293 of 300 outputs from full within-period fusion in the investor-call setting. Extraction-faithfulness and round-trip diagnostics assess source grounding and preservation of extractor-derived graph content.
| Comments: | 20 pages. Accepted at the 3rd Workshop on LLMs and Generative AI for Finance (AI4F) at ACM ICAIF 2026 (non-archival) |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.40482 [cs.AI] |
| (or arXiv:2610.40482v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40482 arXiv-issued DOI via DataCite |
Submission history
From: Yunbai Zhang [view email]
[v1]
Thu, 29 Oct 2026 17:13:45 UTC (84 KB)
来源：arXiv cs.AI · arxiv.org