Tinghui Zhang | Searcharxiv

arXiv2026

Beyond Single-Modal Analytics: A Framework for Integrating Heterogeneous LLM-Based Query Systems for Multi-Modal Data

With the increasing use of multi-modal data, semantic query has become more and more demanded in data management systems, which is an important way to access and analyze multi-modal data. As unstructured data, most information of multi-modal data (text, image, video, etc.) hides in the semantics, which cannot be accessed by traditional database queries like SQL. Given the power of Large Language Models (LLMs) in understanding semantics and processing natural language, in recent years several LLM-based semantic query systems have been proposed to support semantic querying over unstructured data. However, this rapid growth has produced a fragmented ecosystem. Applications face significant integration challenges due to (1) disparate APIs of different semantic query systems and (2) a fundamental trade-off between specialization and generality. Many semantic query systems are highly specialized, offering state-of-the-art performance within a single modality but struggling with multi-modal data. Conversely, some "all-in-one" systems handle multiple modalities but often exhibit suboptimal performance compared to their specialized counterparts in specific modalities. This paper introduces Meta Engine, a novel ``query system on query systems'', designed to resolve those aforementioned challenges. Meta Engine is a unified semantic query engine that integrates heterogeneous, specialized LLM-based query systems. Its architecture comprises five key components: (1) a Natural Language (NL) Query Parser, (2) an Operator Generator, (3) a Query Router, (4) a set of Adapters, and (5) a Result Aggregator. In the evaluation, Meta Engine consistently outperforms all baselines, yielding 3--6x higher F1 in most cases and up to ~24x on specific datasets.

cs.DB↗

arXiv2025

SCOPE: A Generative Approach for LLM Prompt Compression

A big issue in modern LLM applications is they tend to feed long context to LLM, which results in high inference cost and latency, and may exceed the context limit. Prompt compression addresses this issue by reducing the length of input context with minimum loss of generation quality, i.e, the goal of prompt compression is to shorten the LLM input while maintaining a high generation quality. To overcome these limitations, we propose SCOPE, a training-free generative prompt compression framework based on chunk-level rewriting. Unlike the existing token removal methods, our method centers at a chunking-and-summarization mechanism. Specifically, SCOPE splits a prompt into semantically coherent chunks and rewrites the chunks to be more concise. Then the chunks are reconstructed into a meaningful prompt. Additionally, we design several optimization techniques for SCOPE, effectively preserving critical information and text coherence in compression, as well as providing finer-grained control of the compression ratio. We conduct extensive evaluation on typical LLM applications like question-answering and summarization. Results show that SCOPE consistently outperforms the evaluated selective compression baselines across most settings, with particularly strong gains at high compression ratios.

cs.CL↗