12 citations · 14 across the 14 of their papers we have counts for
17 papers
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming
Woosang Lim, Haneul Yoo, Vincent-Daniel Yun +1
Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reasoning performance to collapse o…
Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs
Vincent-Daniel Yun, Woosang Lim, Haneul Yoo +3
Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires each receiver to prefill shared con…
Forward-Free LLM Depth Pruning via Weight Redundancy
Vincent-Daniel Yun, Woosang Lim
Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states through forward passes on ca…
KV-Rescue: Recovering Reasoning Language Model KV Eviction Loss via Stepwise Interleaving
Minsoo Cheong, Woosang Lim, Vincent-Daniel Yun +1
KV-cache eviction caps the memory cost of long reasoning traces but is inherently lossy because the model decodes from a partial view of its history. Under aggressive budgets, this…
Output-Aware Rotation for INT2 KV-Cache Quantization
Vincent-Daniel Yun, Woosang Lim, Minsoo Cheong +4
The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quantization increasingly important…
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy +1
Layer pruning removes entire Transformer decoder blocks from large language models, but introduces a mismatch between the hidden state received by the next surviving layer and the…
Rethinking Layer Redundancy: Calibration Matters More Than Search in LLM Depth Pruning
Minkyu Kim, Vincent-Daniel Yun, Youngrae Kim +3
Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work typically treats layer redundancy as an inherent structural prop…
Locality-Aware Redundancy Pruning for LLM Depth Compression
Vincent-Daniel Yun, Youngrae Kim, Woosang Lim +3
Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing…
Robust Multi-Agent LLMs under Byzantine Faults
Haejoon Lee, Vincent-Daniel Yun, Dimitra Panagou +1
Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability. However, these same interactions can also introduce vulnerabilit…
Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity
Vincent-Daniel Yun, Junhyuk Jo, Sunwoo Lee
Deep neural networks achieve outstanding performance across vision and language tasks, yet their large parameter counts limit deployment in resource-constrained settings. One-shot…
MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models
Soo Yong Kim, Suin Cho, Vincent-Daniel Yun +1
Bridging clinical diagnostic reasoning with AI remains a central challenge in medical imaging. We introduce MedCLM, an automated pipeline that converts detection datasets into larg…
Insights from Gradient Dynamics: Gradient Autoscaled Normalization
Vincent-Daniel Yun
Gradient dynamics play a central role in determining the stability and generalization of deep neural networks. In this work, we provide an empirical analysis of how variance and st…
Why Does Stochastic Gradient Descent Slow Down in Low-Precision Training?
Vincent-Daniel Yun
Low-precision training has become crucial for reducing the computational and memory costs of large-scale deep learning. However, quantizing gradients introduces magnitude shrinkage…
Fast Fourier Transform-Based Spectral and Temporal Gradient Filtering for Differential Privacy
Hyeju Shin, Vincent-Daniel, Kyudan Jung +1
Differential Privacy (DP) has emerged as a key framework for protecting sensitive data in machine learning, but standard DP-SGD often suffers from significant accuracy loss due to…
Sharpness-Aware Minimization with Z-Score Gradient Filtering
Vincent-Daniel Yun
Deep neural networks achieve high performance across many domains but can still face challenges in generalization when optimization is influenced by small or noisy gradient compone…
A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications
Ahmed Magooda, Alec Helyar, Kyle Jackson +14
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automa…
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations
Deren Lei, Yaxi Li, Mengya Hu +4
Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in dev…