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CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting — arXiv:2609.33143

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33143
License (as archived): CC0-1.0
Archived: 2026-10-07T21:52:38.415Z
SHA-256 of the archived text: 2242bb14e165d0f8cd74b682e085c37fe141e9533e24d9ecd597e9421342fbeb
Stable URL of this page: https://metraipolis.com/documento/dc_77331a7fbf00

How to cite

CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting — arXiv:2609.33143 — arXiv (en), https://arxiv.org/abs/2609.33143 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_77331a7fbf00 (Metraipolis archive dc_77331a7fbf00, sha256 2242bb14e165d0f8cd74b682e085c37fe141e9533e24d9ecd597e9421342fbeb)

BibTeX

@misc{dc77331a7fbf00,
  title = {CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting — arXiv:2609.33143},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33143},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_77331a7fbf00; sha256 2242bb14e165d0f8cd74b682e085c37fe141e9533e24d9ecd597e9421342fbeb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33143. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrative disclosures. LLMs can distill textual evidence but can produce scale-misaligned forecasts, whereas history-based anchors are stable but miss forecast-time signals such as product transitions, supply constraints, and management guidance. We introduce CAME (Company-Aware Evidence-Memory Experts), a residual-forecasting framework that refines a no-leakage statistical anchor when current semantic evidence and prior error patterns justify an adjustment. On a development-inclusive rolling backtest of 336 company-quarters from 12 large public technology and platform firms, CAME achieves the lowest aggregate point-estimate error among the reported methods, with statistically supported macro-sMAPE gains over the matched Statistical Anchor, and outperforms History + Guidance on all six aggregate metrics. CAME also links adjustments to source-linked evidence cards and guarded memory traces, supporting forecast inspection, provenance, and failure localization.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.33143 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T21:27:08.486Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).