# TinyGiantALM: A Compact Audio-Language Model for Intent-Aware Reasoning under Resource Constraints Source: [TinyGiantALM: A Compact Audio-Language Model for Intent-Aware Reasoning under Resource Constraints]() Collection index: [llms.txt]() Data updated: 2026-06-24T05:00:22.884Z Authors: Vinh-Thuan Ly Published: 2026-06-07T02:50:24.000Z ## Abstract Current advancements in Audio Reasoning rely on massive Large Audio-Language Models (LALMs), hindering deployment in resource-constrained environments. We introduce TinyGiantALM, a compact 1.5B efficiency-oriented alternative. Instead of brute-force scaling, we propose an Instruction-Aware Feature Refinement framework using a Query-guided Projector and Semantic Gating to filter acoustic signals based on user intent. On the MMAR benchmark, TinyGiantALM achieves 46.4% zero-shot accuracy, significantly outperforming 7B-13B baselines. While a reasoning gap in logical narrative remains versus 30B+ models and certain trade-offs exist in overly dense or spatial scenes, our approach notably surpasses models up to 8x larger in disentangling mixed-modality environments. These findings demonstrate that architectural precision offers a tangible pathway to secure robust perception capabilities on edge-friendly scales. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multimedia Audio Reasoning on MMAR Single Model Track](): Multimedia Audio Reasoning; MMAR Single Model Track; Rubrics Score: 23.77. - [Audio Reasoning on MMAR](): Audio Reasoning; MMAR; Average Accuracy: 46.4. ## Original sources and implementation [Original paper]() [PDF]()