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Related Articles from SNS
dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
arXiv:2606.04115v1 Announce Type: new Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-width uniformly across all layers is sub-optimal in terms of both performance and accuracy. This work introduces dMX, a differentiable mixed-precision quantization framework for learnable floating-point bit-width assignment. We study its application for the microscaling floating-point (MXFP)...
Ablation Study of Block Size, Weight Precision, and Scale Precision in NVFP4 Inference for Low-Power Edge-Efficient Neural Networks
Announce Type: new Abstract: Energy-efficient edge inference requires reducing arithmetic cost, memory traffic, and hardware overhead. This paper presents an ablation-focused study of NVFP4 LUT-based inference for edge-efficient neural networks. The proposed NVLUT framework combines 4-bit NVFP4 activations, two-level scaling, LUT-based mantissa computation, voltage-scaled storage, and selective ECC protection.
Floating-point autotuning with customized precisions
arXiv:2606.08339v1 Announce Type: new Abstract: Reduced-precision arithmetic offers significant opportunities to improve performance, memory usage, and energy efficiency in numerical applications, provided that numerical accuracy is preserved. This work investigates automated precision tuning through customized floating-point formats with user-defined exponent and significand sizes, enabling the emulation of emerging low-precision formats and the exploration of non-standard precision...
SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models
arXiv:2602.01027v2 Announce Type: replace Abstract: Mixed-precision quantization is a promising approach for compressing large language models under tight memory budgets. However, existing mixed-precision methods typically suffer from one of two limitations: they either rely on expensive discrete optimization to determine precision allocation, or introduce hardware inefficiencies due to irregular memory layouts. We propose SFMP, a search-free and hardware-friendly mixed-precision...
Curvature-aware dynamic precision approach for physics-informed neural networks
arXiv:2606.04736v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have become a promising framework for simulating partial differential equations (PDEs) by embedding physical laws directly into neural network training. However, recent studies show that PINN optimisation is sensitive to numerical precision. Existing implementations commonly use either single precision (FP32), which is computationally efficient but prone to failure modes, or double precision (FP64),...
Hierarchical Recursive Precision for Accelerating Symmetric Linear Solves on MXUs
Announce Type: replace Abstract: Symmetric positive-definite system solvers based on Cholesky factorization are fundamental to many scientific applications, such as climate modeling. We present a portable, nested recursive mixed-precision solver designed for Matrix Processing Units (MXUs), including NVIDIA Tensor Cores (H200) and AMD Matrix Cores (MI300X), that assigns low-precision FP16 arithmetic to large off-diagonal blocks, while preserving high precision on diagonal blocks to ensure...
P-Cast Precision in FP8 Attention: Sink-Induced Collapse and the Optimality of S=2^8
arXiv:2606.06521v1 Announce Type: new Abstract: FP8 (E4M3) acceleration for attention computation offers significant throughput gains, but the 3-bit mantissa introduces precision challenges when the softmax probability matrix P is cast to FP8 before the P*V matrix multiplication. We analyze two implementation choices that affect output precision under the Attention Sink phenomenon: (1) the KV block iteration order, and (2) the static scaling factor applied to P before casting.
PaintBench: Deterministic Evaluation of Precise Visual Editing
arXiv:2606.00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To probe this challenge, we introduce PaintBench, a dynamically scalable benchmark targeting 20 fundamental precise visual editing operations across four categories: geometric transformation, structural manipulation, color change, and symbolic reasoning. Procedural generation with configurable...
Meaning in Order, Order in Meaning: Semantic R-precision for Keyphrase Evaluation
Announce Type: new Abstract: Evaluating the quality of automatically generated keyphrases remains a complex challenge. Traditional metrics either rely on exact lexical matching or consider semantic similarity while ignoring prediction ranking, both of which misalign with how humans judge informativeness and relevance. We introduce Semantic R-Precision (SemR-p), a novel evaluation metric that integrates semantic similarity into the rank-aware R-Precision framework.
Internalizing Geometric Law: Learning from Solver Residuals for Precision-Critical Generation
arXiv:2606.09278v1 Announce Type: new Abstract: Large Language Models frequently hallucinate in precision-critical domains such as technical diagramming and mechanical design, where outputs must satisfy strict geometric constraints. We study open-ended geometric synthesis from natural language: translating free-form descriptions into precise constructions whose entities must simultaneously satisfy dozens of interacting constraints.