TASA improves task-aware mixed-precision LLM quantization by searching calibration data mixtures via gradient-trace alignment and aggregating perplexity plus reasoning sensitivity signals, enabling 3.5-bit models to match or beat 4-bit baselines with over 20-point gains on GSM8K.
Layer-wise quantization: A pragmatic and effective method for quantizing llms beyond integer bit- levels
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Empirical analysis identifies task-interfering layers in VLMs and proposes TaLo, a test-time method to bypass them for improved performance without training.
LAQuant improves long-decoding accuracy on quantized reasoning models like Qwen3-4B by 15pp on AIME25 via layer-wise lookahead loss, achieving 3.42x speedup over FP16.
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.
citing papers explorer
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Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization
TASA improves task-aware mixed-precision LLM quantization by searching calibration data mixtures via gradient-trace alignment and aggregating perplexity plus reasoning sensitivity signals, enabling 3.5-bit models to match or beat 4-bit baselines with over 20-point gains on GSM8K.
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Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models
Empirical analysis identifies task-interfering layers in VLMs and proposes TaLo, a test-time method to bypass them for improved performance without training.
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LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss
LAQuant improves long-decoding accuracy on quantized reasoning models like Qwen3-4B by 15pp on AIME25 via layer-wise lookahead loss, achieving 3.42x speedup over FP16.
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LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.
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Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.