推論・効率化 A
170 件中 61〜90 件目を表示
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WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
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QuantWAMs: Calibrating at the Right Granularity for World Action Models
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Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees
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Correcting What You Cannot See: Credit Assignment for Perception Distillation in Multimodal Reasoners
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Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
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Fully Inductive Cardinality Estimation
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Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
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CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance
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Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3
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Theia: Large-Scale Multimodal Captioning and Automated Validation of the Incidents1M Dataset for Data-Free Distillation
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Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory
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Operationally Guided Placement-Aware Learning for Industrial Online 3D Bin Packing
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Are AI Models Working Harder Than They Need to?AIモデルは必要以上に働いているのか？ 乗算依存の効率を問うIEEE Spectrumは、現代のAIの多くが膨大な乗算処理に依存している点に着目した記事を掲載した。生成回答から写真整理まで支えるニューラルネットワークが本当に必要な計算量で動いているのかを問い、AI推論の効率化の余地を論じている。
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AgenticASR: Refining Speech Recognition in Real-World Scenarios via an Agentic Approach
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OPLD: On-Policy Latent Distillation for Multimodal Reasoning
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Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
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MIND: Lightweight and Effective Memory Injection Defense for LLM Agents via Intent-Aware Information Bottleneck
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From Expert Reduction to Behavioral Divergence: Tracing Numerical State through Sparse MoE Inference
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Distilling Answer Set Programming Theories from Large Language Models
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GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation
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Group-Reflective Self-Distillation for Agentic Reinforcement Learning
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SemPIC: Learning Semantic Position-Independent KV Caches
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Stimulus-Evoked Network Dynamics in Human Cortical Organoids: From a Graph-Computational Framework to Repeated-Stimulation Depression
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A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes
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Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
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Flux-OPD: On-Policy Distillation with Evolving Contexts
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Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks
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Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness
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TAPO: Transition-Aware Policy Optimization for LLM Agents
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Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning