“NVIDIA AI Open-Sourced KVzap: A SOTA KV Cache Pruning Method that Delivers near-Lossless 2x-4x Compression.”

“NVIDIA AI Open-Sourced KVzap: A SOTA KV Cache Pruning Method that Delivers near-Lossless 2x-4x Compression.”

“As context lengths move into tens and hundreds of thousands of tokens, the key value cache in transformer decoders becomes a primary deployment bottleneck.”

“Are you pruning your neural networks? "Delete parameters with small weights!" or "Gradients..."”

“It targets one concrete goal, make it easy to compare block level, layer level and weight level pruning methods under a consistent training and evaluation stack on both GPUs and […]”

“I know basics of pruning for deep learning models. However, I don't know how to do it for larger models. Sharing your knowledge and resources will guide me, thanks”

“Certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures.”

“Utilizing 2:4 sparsity combined with quantization on $4096 imes 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines.”

“The paper presents the first resource-adaptive distributed bilevel optimization framework with a second-order free hypergradient estimator.”

“The paper introduces 'incremental certificate learning' to maximize work in sound linear relaxation and invoke exact piecewise-linear reasoning only when relaxations become inconclusive.”

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“The BR$k$NN-Light algorithm uses rapid verification and pruning strategies based on geometric constraints, along with an optimized range search technique, to speed up the process of identifying the R$k$NNs for each query.”

“The method significantly improves convergence and generation quality even after pruning 85% of the training data, and achieves state-of-the-art performance across downstream tasks.”

“The authors propose constraining the RL objective to a dynamically-pruned ``safe'' vocabulary that excludes the extreme tail.”

“The paper demonstrates that the target density (rho) of parameters can be achieved in FL, under data and client participation heterogeneity, with minimal loss in statistical performance.”

“FANG outperforms FLAP and OBC by 1.5%--8.5% in average accuracy under 30% and 40% sparsity.”

“Our method achieves an average bitrate reduction of 8% compared to the baseline approach.”

“OrchANN outperforms four baselines including DiskANN, Starling, SPANN, and PipeANN in both QPS and latency while reducing SSD accesses. Furthermore, OrchANN delivers up to 17.2x higher QPS and 25.0x lower latency than competing systems without sacrificing accuracy.”

“The paper proposes novel neighbor-aware token reduction methods based on Hilbert curve reordering, which explicitly preserves the neighbor structure in a 2D space using 1D sequential representations.”

“The approach can reduce up to 80% of visual tokens while maintaining performance in long context settings.”

“Instruction-following capabilities improve substantially (+46% to +75% in IFEval for Llama-3.2-1B and 3B models).”

“Selective TTS improves insight quality under a fixed compute budget, increasing mean scores from 61.64 to 65.86 while reducing variance.”

“Sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium.”

“In NOMA, a network is treated as a managed memory buffer. Growing capacity is a language primitive.”

“Pruning $8$--$16$ attention sublayers yields up to $1.30\times$ higher inference throughput while keeping average zero-shot accuracy within $2\%$ of the unpruned baseline.”

“SHRP achieves 93% of the original model accuracy while reducing parameters by 48 percent.”

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“Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents.”

“To address these limitations, we propose M$^3$KG-RAG, a Multi-hop Multimodal Knowledge Graph-enhanced RAG that retrieves query-aligned audio-visual knowledge from MMKGs, improving reasoning depth and answer faithfulness in MLLMs.”

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“The paper introduces Hessian-Guided Efficient Dynamic Attention and Token Pruning in Vision Transformer (HEART-VIT).”

“The paper likely details the methodology, experimental setup, results, and comparison with existing methods.”

“The paper focuses on debiasing importance and promoting structural diversity in the token selection process.”

“The article's core concept revolves around optimizing autonomous driving datasets by removing unnecessary data points.”

“The research is available on ArXiv.”

“The paper focuses on trajectory-driven expert pruning.”

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“The research focuses on composable, unconditional security.”

“A 28nm 0.22 μJ/token memory-compute-intensity-aware CNN-Transformer accelerator is presented.”

“The study utilizes Online Semi-Decentralized ST-GNNs.”

“PruneX is a hierarchical communication-efficient system.”

“OPTIMA utilizes Quadratic Programming Reconstruction for LLM pruning.”

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“The article is based on a paper submitted to ArXiv.”

“The research focuses on accelerating online video understanding.”

“The article's context indicates it's from ArXiv, implying a peer-reviewed research paper.”

“SparseSwaps likely offers a new approach to mask refinement within the LLM pruning process.”

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“The article's core innovation lies in its use of Lie groups and quantum geometric dual representations for pruning.”

“The research focuses on Federated Skin Lesion Classification on Edge Devices.”