# Task-Aware Reduction for Scalable LLM-Database Systems

arXiv: https://arxiv.org/abs/2510.11813 · HTML: https://mlsanity.com/paper/2510.11813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Accelerating LLM Inference with Staged Speculative Decoding](https://mlsanity.com/paper/2308.04623/) (2023)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection](https://mlsanity.com/paper/2309.01189/) (2023)
- [Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression](https://mlsanity.com/paper/1910.00409/) (2019)
- [Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations](https://mlsanity.com/paper/2403.08151/) (2024)
- [KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2412.05547/) (2024)
- [A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis](https://mlsanity.com/paper/2110.12489/) (2021)
- [Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis](https://mlsanity.com/paper/2409.13561/) (2024)
- [Ontology-Constrained Generation of Domain-Specific Clinical Summaries](https://mlsanity.com/paper/2411.15666/) (2024)
- [Striving for data-model efficiency: Identifying data externalities on group performance](https://mlsanity.com/paper/2211.06348/) (2022)
- [Logging Practices with Mobile Analytics: An Empirical Study on Firebase](https://mlsanity.com/paper/2104.02513/) (2021)
- [CI/CD Configuration Practices in Open-Source Android Apps: An Empirical Study](https://mlsanity.com/paper/2411.06077/) (2024)

## Built on by (most cited first, 1 of 1)

- [LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysis](https://mlsanity.com/paper/2601.20148/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly applied to data-intensive workflows, from database querying to developer observability. Yet the effectiveness of these systems is constrained by the volume, verbosity, and noise of real-world text-rich data such as logs, telemetry, and monitoring streams. Feeding such data directly into LLMs is costly, environmentally unsustainable, and often misaligned with task objectives. Parallel efforts in LLM efficiency have focused on model- or architecture-level optimizations, but the challenge of reducing upstream input verbosity remains underexplored. In this paper, we argue for treating the token budget of an LLM as an attention budget and elevating task-aware text reduction as a first-class design principle for language -- data systems. We position input-side reduction not as compression, but as attention allocation: prioritizing information most relevant to downstream tasks. We outline open research challenges for building benchmarks, designing adaptive reduction pipelines, and integrating token-budget--aware preprocessing into database and retrieval systems. Our vision is to channel scarce attention resources toward meaningful signals in noisy, data-intensive workflows, enabling scalable, accurate, and sustainable LLM--data integration.