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COMPANY · ENTITY #3757

SAGE

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

SAGE: statistical acceptance gate reduces regressions in self‑evolving LLM agents

New arXiv paper (arXiv:2609.36043v1) introduces SAGE, a Statistical Acceptance Gate for self‑evolving LLM agents that (1) uses per‑item paired comparisons to detect regressions hidden by aggregate scores and (2) applies a one‑sided paired statistical test to accept edits only when wins are reliably greater than losses. Across five benchmarks and four backbone LLMs under an equal‑budget protocol, SAGE markedly lowers regression rates in 19 of 20 settings and matches or improves final scores (e.g., regression rate from 36.5% to 0% on LiveMath and from 42.8% to 0% on OfficeQA with DeepSeek‑V4).

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

SAGE: Governed LLM pipeline for converting enterprise guidelines into structured artifacts (arXiv:2609.17775v1)

Researchers released SAGE (arXiv:2609.17775v1), a multi-stage LLM pipeline that adds governance to extraction from complex enterprise guideline documents via a shared versioned rule store, schema-validated inter-stage contracts, deterministic structural checks, LLM semantic scoring, consistency filtering, and end-to-end provenance. On 120 documents the authors report reducing turnaround from days to 20–100 minutes, extracting 3,896 rules and producing 812 artifacts for review with a 96% document-level success rate and 3.2% hallucination (versus 15.7% without governance).

7.0