AI·DeepSearch-World is a self-distillation framework for web search agents that uses self-generated experience for training in verifiable environments.
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9 results for “sparse”
AI·Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders extract cross-seed universal features from independently trained BERT models to address dictionary learning misalignment in mechanistic interpretability.
AI·Structured pruning method for sparsely-activated MoE models removes bad experts to address full expert bank memory bottleneck while preserving inference efficiency.
AI·LongE2V leverages pre-trained video diffusion models to reconstruct, predict, and interpolate event-based videos, achieving high data efficiency and superior perceptual quality through autoregressive unrolling and adaptive context switching.
AI·Sparse Delta Memory scales the hidden state of gated linear RNNs to orders of magnitude higher capacity via sparse addressing, improving long-context recall and in-context learning while keeping compute efficient.
AI·A sparse and truncated state-vector simulator for peaked quantum circuits predicts the most probable output bit string using only nonzero amplitudes with vectorized operations and optional GPU acceleration.
Single-stream diffusion transformer with a DeepSeek-V3-style sparse MoE (128 experts, top-8 routing, 1.4B active of 13B total). Six-reward RL post-training including a physical-plausibility reward, plus an action-to-video mode that predicts robot rollouts from action and hand-pose conditions. Weights, code, and a Diffusers/SGLang stack are open under the LingBot-Video name. Two things I would pus…
A key challenge in Arabic NLP is the scarcity of dialectal data relative to Modern Standard Arabic (MSA), causing LLMs to overproduce MSA and struggle with dialectally accurate generation. From an interpretability perspective, this raises a fundamental question: where and how are dialectal features encoded within model internals, and can these representations be leveraged to improve dialect generation without fine-tuning? This study investigates two complementary inference-time approaches that …
Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key failure mode: models predict verbs via object-driven shortcuts (i.e., relying on the labeled object class) rather than temporal evidence. We argue that sparse compositional supervision and verb-object learning asymmetry can promote object-driven shortcut learning. Our analysis with proposed diagnostic metrics shows that…