#Computational Efficiency
Latest news articles tagged with "Computational Efficiency". Follow the timeline of events, related topics, and entities.
Articles (30)
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🇺🇸 Multi-objective Evolutionary Merging Enables Efficient Reasoning Models
[USA]
arXiv:2604.06465v1 Announce Type: cross Abstract: Reasoning models have demonstrated remarkable capabilities in solving complex problems by leveraging long chains of thought. However, this more delib...
Related: #Artificial Intelligence, #Algorithmic Research -
🇺🇸 FLeX: Fourier-based Low-rank EXpansion for multilingual transfer
[USA]
arXiv:2604.06253v1 Announce Type: cross Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large languag...
Related: #Artificial Intelligence, #Software Engineering -
🇺🇸 Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models
[USA]
arXiv:2604.06912v1 Announce Type: cross Abstract: MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global re...
Related: #Artificial Intelligence, #Computer Vision -
🇺🇸 Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
[USA]
arXiv:2506.08898v4 Announce Type: replace Abstract: Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs)...
Related: #Optimization Algorithms -
🇺🇸 VideoAtlas: Navigating Long-Form Video in Logarithmic Compute
[USA]
arXiv:2603.17948v1 Announce Type: cross Abstract: Extending language models to video introduces two challenges: representation, where existing methods rely on lossy approximations, and long-context, ...
Related: #Video Analysis -
🇺🇸 The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle
[USA]
arXiv:2603.17433v1 Announce Type: cross Abstract: Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-context t...
Related: #AI Architecture -
🇺🇸 FlashSampling: Fast and Memory-Efficient Exact Sampling
[USA]
arXiv:2603.15854v1 Announce Type: cross Abstract: Sampling from a categorical distribution is mathematically simple, but in large-vocabulary decoding, it often triggers extra memory traffic and extra...
Related: #Sampling Algorithms -
🇺🇸 Accelerating Suffix Jailbreak attacks with Prefix-Shared KV-cache
[USA]
arXiv:2603.13420v1 Announce Type: cross Abstract: Suffix jailbreak attacks serve as a systematic method for red-teaming Large Language Models (LLMs) but suffer from prohibitive computational costs, a...
Related: #AI Security -
🇺🇸 TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting
[USA]
arXiv:2603.11352v1 Announce Type: new Abstract: Transformer-based time series foundation models face a fundamental trade-off in choice of tokenization: point-wise embeddings preserve temporal fidelit...
Related: #Time Series Forecasting -
🇺🇸 Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability
[USA]
arXiv:2603.12038v1 Announce Type: cross Abstract: Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent ...
Related: #Inference Acceleration -
🇺🇸 RedFuser: An Automatic Operator Fusion Framework for Cascaded Reductions on AI Accelerators
[USA]
arXiv:2603.10026v1 Announce Type: cross Abstract: Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has been...
Related: #AI Optimization -
🇺🇸 Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation
[USA]
arXiv:2603.08957v1 Announce Type: cross Abstract: A \emph{tensor-relational} computation is a relational computation where individual tuples carry vectors, matrices, or higher-dimensional arrays. An ...
Related: #Data Science -
🇺🇸 ECHO: Encoding Communities via High-order Operators
[USA]
arXiv:2602.22446v1 Announce Type: cross Abstract: Community detection in attributed networks faces a fundamental divide: topological algorithms ignore semantic features, while Graph Neural Networks (...
Related: #Machine Learning Innovation, #Network Analysis -
🇺🇸 Mitigating Legibility Tax with Decoupled Prover-Verifier Games
[USA]
arXiv:2602.23248v1 Announce Type: new Abstract: As large language models become increasingly capable, it is critical that their outputs can be easily checked by less capable systems. Prover-verifier ...
Related: #Artificial Intelligence, #Model Verification -
🇺🇸 S2O: Early Stopping for Sparse Attention via Online Permutation
[USA]
arXiv:2602.22575v1 Announce Type: cross Abstract: Attention scales quadratically with sequence length, fundamentally limiting long-context inference. Existing block-granularity sparsification can red...
Related: #Machine Learning Optimization, #Attention Mechanisms -
🇺🇸 ArchAgent: Agentic AI-driven Computer Architecture Discovery
[USA]
arXiv:2602.22425v1 Announce Type: new Abstract: Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems ...
Related: #AI in Hardware Design, #Automated Architecture Discovery -
🇺🇸 From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
[USA]
arXiv:2602.22492v1 Announce Type: cross Abstract: In this work, we study scaling limits of shallow Bayesian neural networks (BNNs) via their connection to Gaussian processes (GPs), with an emphasis o...
Related: #Machine Learning Theory, #Statistical Modeling, #Neural Networks -
🇺🇸 Stable Adaptive Thinking via Advantage Shaping and Length-Aware Gradient Regulation
[USA]
arXiv:2602.22556v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance through extended reasoning traces, but they often exhibit overthinking behavior for low-comp...
Related: #Machine Learning, #Artificial Intelligence -
🇺🇸 HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG
[USA]
arXiv:2602.20926v1 Announce Type: new Abstract: Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations, limiting their reliability in knowledge-intensive ta...
Related: #Artificial Intelligence, #Knowledge Retrieval -
🇺🇸 MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs
[USA]
arXiv:2602.20191v1 Announce Type: cross Abstract: Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) deployment, where an LLM can be inferred with v...
Related: #Machine Learning, #Quantization Optimization -
🇺🇸 CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference
[USA]
arXiv:2602.20732v1 Announce Type: new Abstract: Long-context LLMs demand accurate inference at low latency, yet decoding becomes primarily constrained by KV cache as context grows. Prior pruning meth...
Related: #AI Optimization, #Large Language Models -
🇺🇸 TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
[USA]
arXiv:2602.17122v1 Announce Type: cross Abstract: Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test...
Related: #Time‑Series Forecasting, #Frequency Domain Analysis, #Stationarity vs. Non‑Stationarity, #Distribution Shift Mitigation -
🇺🇸 SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework
[USA]
arXiv:2602.17330v1 Announce Type: cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise...
Related: #Machine Learning, #Adaptive Immune Repertoire Analysis, #Bias Mitigation, #Clustering Algorithms -
🇺🇸 Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
[USA]
arXiv:2602.16947v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant ba...
Related: #Graph Neural Networks, #Symbolic Machine Learning, #Expressivity Limits, #Interpretability -
🇺🇸 Accelerating Large-Scale Dataset Distillation via Exploration-Exploitation Optimization
[USA]
arXiv:2602.15277v1 Announce Type: cross Abstract: Dataset distillation compresses the original data into compact synthetic datasets, reducing training time and storage while retaining model performan...
Related: #Dataset Distillation, #Large‑Scale Machine Learning, #Optimization Techniques, #Exploration vs. Exploitation -
🇺🇸 SimpleMatch: A Simple and Strong Baseline for Semantic Correspondence
[USA]
arXiv:2601.12357v2 Announce Type: replace-cross Abstract: Recent advances in semantic correspondence have been largely driven by the use of pre-trained large-scale models. However, a limitation of th...
Related: #Computer Vision, #Artificial Intelligence -
🇺🇸 Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
[USA]
arXiv:2602.12635v1 Announce Type: cross Abstract: As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiF...
Related: #AI Hardware, #Precision Optimization -
🇺🇸 Rational Neural Networks have Expressivity Advantages
[USA]
arXiv:2602.12390v1 Announce Type: cross Abstract: We study neural networks with trainable low-degree rational activation functions and show that they are more expressive and parameter-efficient than ...
Related: #Neural Network Architecture, #Theoretical Computer Science -
🇺🇸 SLA2: Sparse-Linear Attention with Learnable Routing and QAT
[USA]
arXiv:2602.12675v1 Announce Type: cross Abstract: Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generatio...
Related: #Machine Learning, #Attention Mechanisms -
🇺🇸 Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
[USA]
arXiv:2502.07971v2 Announce Type: replace-cross Abstract: Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets. ...
Related: #Information Retrieval, #Explainable AI
Key Entities (23)
- Large language model (3 news)
- Computer vision (1 news)
- Efficiency (1 news)
- Transparency (1 news)
- Information retrieval (1 news)
- Explainable artificial intelligence (1 news)
- Unit circle (1 news)
- Neural processing unit (1 news)
- Graph neural network (1 news)
- Network analysis (1 news)
- Echo (disambiguation) (1 news)
- Transformer (deep learning) (1 news)
- Early stopping (1 news)
- Expressivity (1 news)
- Neural network (1 news)
- Approximation theory (1 news)
- Chess (disambiguation) (1 news)
- AlphaEvolve (1 news)
- Cache replacement policies (1 news)
- Gaussian process (1 news)
- Reasoning model (1 news)
- Reinforcement learning (1 news)
- Prompt engineering (1 news)
About the topic: Computational Efficiency
The topic "Computational Efficiency" aggregates 30+ news articles from various countries.