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Seedream 4.0: Toward Next-generation Multimodal Image Generation
Paper • 2509.20427 • Published • 82 -
Tree Search for LLM Agent Reinforcement Learning
Paper • 2509.21240 • Published • 92 -
SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
Paper • 2510.06917 • Published • 34 -
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Paper • 2510.04618 • Published • 128
Collections
Discover the best community collections!
Collections including paper arxiv:2601.00417
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Deep Delta Learning
Paper • 2601.00417 • Published • 30 -
mHC: Manifold-Constrained Hyper-Connections
Paper • 2512.24880 • Published • 259 -
VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
Paper • 2512.14531 • Published • 13 -
Stronger Normalization-Free Transformers
Paper • 2512.10938 • Published • 19
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Forgetting Transformer: Softmax Attention with a Forget Gate
Paper • 2503.02130 • Published • 32 -
L^2M: Mutual Information Scaling Law for Long-Context Language Modeling
Paper • 2503.04725 • Published • 21 -
Transformers without Normalization
Paper • 2503.10622 • Published • 170 -
I-Con: A Unifying Framework for Representation Learning
Paper • 2504.16929 • Published • 30
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Nuclear Norm Regularization for Deep Learning
Paper • 2405.14544 • Published • 1 -
Token embeddings violate the manifold hypothesis
Paper • 2504.01002 • Published • 1 -
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
Paper • 2403.10476 • Published • 1 -
ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning
Paper • 2504.00254 • Published • 1
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Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
Paper • 2401.02994 • Published • 52 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 60 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 24 -
BlackMamba: Mixture of Experts for State-Space Models
Paper • 2402.01771 • Published • 25
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Seedream 4.0: Toward Next-generation Multimodal Image Generation
Paper • 2509.20427 • Published • 82 -
Tree Search for LLM Agent Reinforcement Learning
Paper • 2509.21240 • Published • 92 -
SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
Paper • 2510.06917 • Published • 34 -
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Paper • 2510.04618 • Published • 128
-
Deep Delta Learning
Paper • 2601.00417 • Published • 30 -
mHC: Manifold-Constrained Hyper-Connections
Paper • 2512.24880 • Published • 259 -
VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
Paper • 2512.14531 • Published • 13 -
Stronger Normalization-Free Transformers
Paper • 2512.10938 • Published • 19
-
Nuclear Norm Regularization for Deep Learning
Paper • 2405.14544 • Published • 1 -
Token embeddings violate the manifold hypothesis
Paper • 2504.01002 • Published • 1 -
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
Paper • 2403.10476 • Published • 1 -
ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning
Paper • 2504.00254 • Published • 1
-
Forgetting Transformer: Softmax Attention with a Forget Gate
Paper • 2503.02130 • Published • 32 -
L^2M: Mutual Information Scaling Law for Long-Context Language Modeling
Paper • 2503.04725 • Published • 21 -
Transformers without Normalization
Paper • 2503.10622 • Published • 170 -
I-Con: A Unifying Framework for Representation Learning
Paper • 2504.16929 • Published • 30
-
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
Paper • 2401.02994 • Published • 52 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 60 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 24 -
BlackMamba: Mixture of Experts for State-Space Models
Paper • 2402.01771 • Published • 25