# Evergreen: Efficient Claim Verification for Semantic Aggregates

arXiv: https://arxiv.org/abs/2604.26180 · HTML: https://mlsanity.com/paper/2604.26180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 39 of its 82 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 30 of 39)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [Estimating means of bounded random variables by betting](https://mlsanity.com/paper/2010.09686/) (2020)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval](https://mlsanity.com/paper/2101.00436/) (2021)
- [Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models](https://mlsanity.com/paper/2405.05374/) (2024)
- [FIRE: Fact-checking with Iterative Retrieval and Verification](https://mlsanity.com/paper/2411.00784/) (2024)
- [TWEETSUMM -- A Dialog Summarization Dataset for Customer Service](https://mlsanity.com/paper/2111.11894/) (2021)
- [Optimizing LLM Queries in Relational Data Analytics Workloads](https://mlsanity.com/paper/2403.05821/) (2024)
- [UQE: A Query Engine for Unstructured Databases](https://mlsanity.com/paper/2407.09522/) (2024)
- [CAESURA: Language Models as Multi-Modal Query Planners](https://mlsanity.com/paper/2308.03424/) (2023)
- [The Design of an LLM-powered Unstructured Analytics System](https://mlsanity.com/paper/2409.00847/) (2024)
- [Abacus: A Cost-Based Optimizer for Semantic Operator Systems](https://mlsanity.com/paper/2505.14661/) (2025)
- [VerdictDB: Universalizing Approximate Query Processing](https://mlsanity.com/paper/1804.00770/) (2018)
- [Cortex AISQL: A Production SQL Engine for Unstructured Data](https://mlsanity.com/paper/2511.07663/) (2025)
- [Multi-Objective Agentic Rewrites for Unstructured Data Processing](https://mlsanity.com/paper/2512.02289/) (2025)
- [Accelerating Approximate Aggregation Queries with Expensive Predicates](https://mlsanity.com/paper/2108.06313/) (2021)
- [QUEST: Query Optimization in Unstructured Document Analysis](https://mlsanity.com/paper/2507.06515/) (2025)
- [Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees](https://mlsanity.com/paper/2509.02896/) (2025)
- [Smoke: Fine-grained Lineage at Interactive Speed](https://mlsanity.com/paper/1801.07237/) (2018)
- [RePanda: Pandas-powered Tabular Verification and Reasoning](https://mlsanity.com/paper/2503.11921/) (2025)
- [Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases](https://mlsanity.com/paper/2512.03278/) (2025)
- [Featurized-Decomposition Join: Low-Cost Semantic Joins with Guarantees](https://mlsanity.com/paper/2512.05399/) (2025)
- [Accelerating Aggregation Queries on Unstructured Streams of Data](https://mlsanity.com/paper/2308.09157/) (2023)

## Abstract (arXiv)

With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an LLM. However, the resulting semantic aggregate may contain claims that are not grounded in the underlying relation. Verifying such claims is challenging: they often involve quantifiers, groupings, and comparisons over relations that far exceed LLM context windows and require a costly combination of semantic and symbolic processing. We present Evergreen, a system that recasts claim verification as a semantic query processing task with tailored optimizations and provenance capture. Evergreen compiles each claim into a declarative semantic verification query that can execute on the same query engine used to produce the aggregate. To reduce cost, Evergreen avoids unnecessary LLM calls through verification-aware optimizations, including early stopping, relevance sorting, and estimation with confidence sequences, as well as general-purpose optimizations for semantic queries, such as operator fusion, similarity filtering, and prompt caching. Each verdict is accompanied by citations that identify a minimal set of tuples justifying the result, with semantics based on semiring provenance for first-order logic. On a benchmark of production-inspired workloads over restaurant review and customer support datasets, Evergreen's optimized configurations occupy the entire cost-quality Pareto frontier. With a strong LLM, Evergreen preserves verification quality at an F1 of 0.94 while reducing cost by 3.1x relative to unoptimized verification; with a substantially weaker LLM, it surpasses the strongest external baseline's F1 (0.87 vs. 0.83) at 7.0x lower cost.