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[2602.13595] The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning
Summary
This paper explores the limitations of neural scaling laws in AI, revealing a 'quantization trap' where reducing numerical precision can paradoxically increase energy consumption and degrade reasoning accuracy in multi-hop tasks.
Why It Matters
Understanding the quantization trap is crucial for AI development, as it challenges the prevailing notion that lower precision always leads to better efficiency. This insight can reshape approaches to AI model optimization, particularly in complex reasoning tasks.
Key Takeaways
- Reducing numerical precision can lead to increased energy consumption.
- The quantization trap is particularly pronounced in multi-hop reasoning tasks.
- Hardware overhead and dequantization latency are significant factors in this phenomenon.
- The traditional 'smaller-is-better' heuristic may be counterproductive.
- Understanding these limitations is essential for future AI advancements.
Computer Science > Artificial Intelligence arXiv:2602.13595 (cs) [Submitted on 14 Feb 2026] Title:The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning Authors:Henry Han, Xiyang Liu, Xiaodong Wang, Fei Han, Xiaodong Li View a PDF of the paper titled The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning, by Henry Han and 4 other authors View PDF HTML (experimental) Abstract:Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency and energy profile (E proportional to bits). In this paper, we demonstrate that this scaling law breaks in the context of multi-hop reasoning. We reveal a 'quantization trap' where reducing precision from 16-bit to 8/4-bit paradoxically increases more net energy consumption while degrading reasoning accuracy. We provide a rigorous theoretical decomposition that attributes this failure to hardware casting overhead, the hidden latency cost of dequantization kernels, which becomes a dominant bottleneck in sequential reasoning chains, as well as to a sequential energy amortization failure. As a result, scaling law breaking is unavoidable in practice. Our findings suggest that the industry's "smaller-is-better" heuristic is mathematically counterproductive for complex reasoning tasks. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.13595 [cs.AI] (or arXiv:2602.13595v1 [cs.AI] for this version) ht...