brain/harvest/architectures.jsonl

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{"type": "node", "id": "paper-2205.14135", "kind": "paper", "label": "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness", "url": "https://arxiv.org/abs/2205.14135", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2307.08691", "kind": "paper", "label": "FlashAttention-2: Faster Attention with Better Parallelism", "url": "https://arxiv.org/abs/2307.08691", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2407.08608", "kind": "paper", "label": "FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision", "url": "https://arxiv.org/abs/2407.08608", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2310.01889", "kind": "paper", "label": "Ring Attention with Blockwise Transformers for Near-Infinite Context", "url": "https://arxiv.org/abs/2310.01889", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2311.09431", "kind": "paper", "label": "Striped Attention: Faster Ring Attention for Causal Transformers", "url": "https://arxiv.org/abs/2311.09431", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2405.04434", "kind": "paper", "label": "DeepSeek-V2 (Multi-head Latent Attention)", "url": "https://arxiv.org/abs/2405.04434", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2502.14837", "kind": "paper", "label": "Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer LLM", "url": "https://arxiv.org/abs/2502.14837", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2502.11089", "kind": "paper", "label": "Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention", "url": "https://arxiv.org/abs/2502.11089", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2004.05150", "kind": "paper", "label": "Longformer: The Long-Document Transformer", "url": "https://arxiv.org/abs/2004.05150", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2001.04451", "kind": "paper", "label": "Reformer: The Efficient Transformer", "url": "https://arxiv.org/abs/2001.04451", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2009.14794", "kind": "paper", "label": "Rethinking Attention with Performers", "url": "https://arxiv.org/abs/2009.14794", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-1911.02150", "kind": "paper", "label": "Fast Transformer Decoding: Multi-Query Attention", "url": "https://arxiv.org/abs/1911.02150", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2305.13245", "kind": "paper", "label": "GQA: Generalized Multi-Query Transformer Models", "url": "https://arxiv.org/abs/2305.13245", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2410.05258", "kind": "paper", "label": "Differential Transformer", "url": "https://arxiv.org/abs/2410.05258", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2406.16747", "kind": "paper", "label": "Sparser is Faster and Less is More: Efficient Sparse Attention", "url": "https://arxiv.org/abs/2406.16747", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2502.18137", "kind": "paper", "label": "SpargeAttention: Training-free Sparse Attention", "url": "https://arxiv.org/abs/2502.18137", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2404.07143", "kind": "paper", "label": "Infini-attention: Efficient Infinite Context Transformers", "url": "https://arxiv.org/abs/2404.07143", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2405.05254", "kind": "paper", "label": "You Only Cache Once: Decoder-Decoder Architectures (YOCO)", "url": "https://arxiv.org/abs/2405.05254", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2103.03206", "kind": "paper", "label": "Perceiver: General Perception with Iterative Attention", "url": "https://arxiv.org/abs/2103.03206", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2107.14795", "kind": "paper", "label": "Perceiver IO: A General Architecture for Structured I/O", "url": "https://arxiv.org/abs/2107.14795", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2312.06635", "kind": "paper", "label": "Gated Linear Attention Transformers with Hardware-Efficient Training", "url": "https://arxiv.org/abs/2312.06635", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-2402.19427", "kind": "paper", "label": "Griffin: Mixing Gated Linear Recurrences with Local Attention", "url": "https://arxiv.org/abs/2402.19427", "source": "arxiv", "axis": "attention", "layer": 0}
{"type": "node", "id": "paper-1701.06538", "kind": "paper", "label": "Outrageously Large Neural Networks: Sparsely-Gated Mixture-of-Experts Layer", "url": "https://arxiv.org/abs/1701.06538", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2101.03961", "kind": "paper", "label": "Switch Transformers: Scaling to Trillion Parameter Models", "url": "https://arxiv.org/abs/2101.03961", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2006.16668", "kind": "paper", "label": "GShard: Scaling Giant Models with Conditional Computation", "url": "https://arxiv.org/abs/2006.16668", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2202.08906", "kind": "paper", "label": "ST-MoE: Designing Stable and Transferable Sparse Expert Models", "url": "https://arxiv.org/abs/2202.08906", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2211.15841", "kind": "paper", "label": "MegaBlocks: Efficient Sparse Training with Mixture-of-Experts", "url": "https://arxiv.org/abs/2211.15841", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2401.04088", "kind": "paper", "label": "Mixtral of Experts", "url": "https://arxiv.org/abs/2401.04088", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2412.19437", "kind": "paper", "label": "DeepSeek-V3 Technical Report", "url": "https://arxiv.org/abs/2412.19437", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2408.15664", "kind": "paper", "label": "Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts", "url": "https://arxiv.org/abs/2408.15664", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2409.02060", "kind": "paper", "label": "OLMoE: Open Mixture-of-Experts Language Models", "url": "https://arxiv.org/abs/2409.02060", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2305.14705", "kind": "paper", "label": "Mixture-of-Experts Meets Instruction Tuning", "url": "https://arxiv.org/abs/2305.14705", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2404.02258", "kind": "paper", "label": "Mixture-of-Depths: Dynamically allocating compute in transformers", "url": "https://arxiv.org/abs/2404.02258", "source": "arxiv", "axis": "moe", "layer": 0}
{"type": "node", "id": "paper-2205.14217", "kind": "paper", "label": "Diffusion-LM Improves Controllable Text Generation", "url": "https://arxiv.org/abs/2205.14217", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2107.03006", "kind": "paper", "label": "Structured Denoising Diffusion Models in Discrete State-Spaces (D3PM)", "url": "https://arxiv.org/abs/2107.03006", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2310.16834", "kind": "paper", "label": "Discrete Diffusion Modeling by Estimating Ratios of Data Distribution (SEDD)", "url": "https://arxiv.org/abs/2310.16834", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2406.07524", "kind": "paper", "label": "Simple and Effective Masked Diffusion Language Models (MDLM)", "url": "https://arxiv.org/abs/2406.07524", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2502.09992", "kind": "paper", "label": "LLaDA: Large Language Diffusion Models", "url": "https://arxiv.org/abs/2502.09992", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2503.09573", "kind": "paper", "label": "Block Diffusion: Interpolating Between Autoregressive and Diffusion LMs", "url": "https://arxiv.org/abs/2503.09573", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2408.11039", "kind": "paper", "label": "Transfusion: Predict the Next Token and Diffuse Images", "url": "https://arxiv.org/abs/2408.11039", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2310.17680", "kind": "paper", "label": "CodeFusion: A Pre-trained Diffusion Model for Code Generation", "url": "https://arxiv.org/abs/2310.17680", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2410.13782", "kind": "paper", "label": "DPLM-2: A Multimodal Diffusion Protein Language Model", "url": "https://arxiv.org/abs/2410.13782", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2506.05340", "kind": "paper", "label": "Exploring Diffusion Transformer Designs via Grafting", "url": "https://arxiv.org/abs/2506.05340", "source": "arxiv", "axis": "diffusion", "layer": 0}
{"type": "node", "id": "paper-2312.00752", "kind": "paper", "label": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", "url": "https://arxiv.org/abs/2312.00752", "source": "arxiv", "axis": "ssm", "layer": 0}
{"type": "node", "id": "paper-2405.21060", "kind": "paper", "label": "Transformers are SSMs: Structured State Space Duality (Mamba-2)", "url": "https://arxiv.org/abs/2405.21060", "source": "arxiv", "axis": "ssm", "layer": 0}
{"type": "node", "id": "paper-2403.19887", "kind": "paper", "label": "Jamba: A Hybrid Transformer-Mamba Language Model", "url": "https://arxiv.org/abs/2403.19887", "source": "arxiv", "axis": "ssm", "layer": 0}
{"type": "node", "id": "paper-2307.08621", "kind": "paper", "label": "Retentive Network: A Successor to Transformer (RetNet)", "url": "https://arxiv.org/abs/2307.08621", "source": "arxiv", "axis": "ssm", "layer": 0}
{"type": "node", "id": "paper-2404.05892", "kind": "paper", "label": "Eagle and Finch: RWKV with Matrix-Valued States", "url": "https://arxiv.org/abs/2404.05892", "source": "arxiv", "axis": "ssm", "layer": 0}
{"type": "node", "id": "paper-2404.08801", "kind": "paper", "label": "Megalodon: Efficient LLM Pretraining with Unlimited Context", "url": "https://arxiv.org/abs/2404.08801", "source": "arxiv", "axis": "ssm", "layer": 0}
{"type": "node", "id": "paper-2404.19756", "kind": "paper", "label": "KAN: Kolmogorov-Arnold Networks", "url": "https://arxiv.org/abs/2404.19756", "source": "arxiv", "axis": "kan", "layer": 0}
{"type": "node", "id": "paper-2408.10205", "kind": "paper", "label": "KAN 2.0: Kolmogorov-Arnold Networks Meet Science", "url": "https://arxiv.org/abs/2408.10205", "source": "arxiv", "axis": "kan", "layer": 0}
{"type": "node", "id": "paper-2501.00663", "kind": "paper", "label": "Titans: Learning to Memorize at Test Time", "url": "https://arxiv.org/abs/2501.00663", "source": "arxiv", "axis": "titans", "layer": 0}
{"type": "node", "id": "paper-2501.15383", "kind": "paper", "label": "Qwen2.5-1M Technical Report", "url": "https://arxiv.org/abs/2501.15383", "source": "arxiv", "axis": "titans", "layer": 0}
{"type": "node", "id": "paper-2502.20082", "kind": "paper", "label": "LongRoPE2: Near-Lossless LLM Context Window Scaling", "url": "https://arxiv.org/abs/2502.20082", "source": "arxiv", "axis": "titans", "layer": 0}
{"type": "node", "id": "paper-2308.14508", "kind": "paper", "label": "LongBench: Bilingual Multitask Benchmark for Long Context", "url": "https://arxiv.org/abs/2308.14508", "source": "arxiv", "axis": "titans", "layer": 0}
{"type": "node", "id": "paper-2404.06654", "kind": "paper", "label": "RULER: What's the Real Context Size of Your Long-Context LLMs?", "url": "https://arxiv.org/abs/2404.06654", "source": "arxiv", "axis": "titans", "layer": 0}
{"type": "node", "id": "paper-2402.17764", "kind": "paper", "label": "The Era of 1-bit LLMs: All LLMs are in 1.58 Bits (BitNet b1.58)", "url": "https://arxiv.org/abs/2402.17764", "source": "arxiv", "axis": "ternary", "layer": 0}
{"type": "node", "id": "paper-2310.11453", "kind": "paper", "label": "BitNet: Scaling 1-bit Transformers for Large Language Models", "url": "https://arxiv.org/abs/2310.11453", "source": "arxiv", "axis": "ternary", "layer": 0}
{"type": "node", "id": "paper-2504.18415", "kind": "paper", "label": "BitNet v2: Native 4-bit Activations with Hadamard Transformation", "url": "https://arxiv.org/abs/2504.18415", "source": "arxiv", "axis": "ternary", "layer": 0}
{"type": "node", "id": "paper-1710.03740", "kind": "paper", "label": "Mixed Precision Training", "url": "https://arxiv.org/abs/1710.03740", "source": "arxiv", "axis": "ternary", "layer": 0}
{"type": "node", "id": "paper-2205.13147", "kind": "paper", "label": "Matryoshka Representation Learning", "url": "https://arxiv.org/abs/2205.13147", "source": "arxiv", "axis": "matryoshka", "layer": 0}
{"type": "node", "id": "paper-2310.07707", "kind": "paper", "label": "MatFormer: Nested Transformer for Elastic Inference", "url": "https://arxiv.org/abs/2310.07707", "source": "arxiv", "axis": "matryoshka", "layer": 0}
{"type": "node", "id": "paper-2404.05961", "kind": "paper", "label": "LLM2Vec: LLMs Are Secretly Powerful Text Encoders", "url": "https://arxiv.org/abs/2404.05961", "source": "arxiv", "axis": "matryoshka", "layer": 0}
{"type": "node", "id": "paper-2507.10524", "kind": "paper", "label": "Mixture-of-Recursions: Learning Dynamic Recursive Depths", "url": "https://arxiv.org/abs/2507.10524", "source": "arxiv", "axis": "mor", "layer": 0}
{"type": "node", "id": "paper-2410.20672", "kind": "paper", "label": "Relaxed Recursive Transformers: Parameter Sharing with Layer-wise LoRA", "url": "https://arxiv.org/abs/2410.20672", "source": "arxiv", "axis": "mor", "layer": 0}
{"type": "node", "id": "paper-2211.17192", "kind": "paper", "label": "Fast Inference from Transformers via Speculative Decoding", "url": "https://arxiv.org/abs/2211.17192", "source": "arxiv", "axis": "specdecode", "layer": 0}
{"type": "node", "id": "paper-2302.01318", "kind": "paper", "label": "Accelerating Large Language Model Decoding with Speculative Sampling", "url": "https://arxiv.org/abs/2302.01318", "source": "arxiv", "axis": "specdecode", "layer": 0}
{"type": "node", "id": "paper-2401.10774", "kind": "paper", "label": "Medusa: LLM Inference Acceleration with Multiple Decoding Heads", "url": "https://arxiv.org/abs/2401.10774", "source": "arxiv", "axis": "specdecode", "layer": 0}
{"type": "node", "id": "paper-2401.15077", "kind": "paper", "label": "EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty", "url": "https://arxiv.org/abs/2401.15077", "source": "arxiv", "axis": "specdecode", "layer": 0}
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{"type": "node", "id": "paper-2408.03314", "kind": "paper", "label": "Scaling LLM Test-Time Compute Optimally", "url": "https://arxiv.org/abs/2408.03314", "source": "arxiv", "axis": "testtime", "layer": 0}
{"type": "node", "id": "paper-2502.05171", "kind": "paper", "label": "Scaling up Test-Time Compute with Latent Reasoning: Recurrent Depth", "url": "https://arxiv.org/abs/2502.05171", "source": "arxiv", "axis": "testtime", "layer": 0}
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{"type": "node", "id": "paper-2407.21787", "kind": "paper", "label": "Large Language Monkeys: Scaling Inference Compute with Repeated Sampling", "url": "https://arxiv.org/abs/2407.21787", "source": "arxiv", "axis": "testtime", "layer": 0}
{"type": "node", "id": "paper-2408.00724", "kind": "paper", "label": "Inference Scaling Laws: Compute-Optimal Inference", "url": "https://arxiv.org/abs/2408.00724", "source": "arxiv", "axis": "testtime", "layer": 0}
{"type": "node", "id": "paper-2412.14135", "kind": "paper", "label": "Scaling of Search and Learning: A Roadmap to Reproduce o1", "url": "https://arxiv.org/abs/2412.14135", "source": "arxiv", "axis": "testtime", "layer": 0}
{"type": "node", "id": "paper-2412.06769", "kind": "paper", "label": "Training LLMs to Reason in a Continuous Latent Space (Coconut)", "url": "https://arxiv.org/abs/2412.06769", "source": "arxiv", "axis": "testtime", "layer": 0}
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{"type": "node", "id": "paper-2408.06195", "kind": "paper", "label": "Mutual Reasoning Makes Smaller LLMs Stronger (rStar)", "url": "https://arxiv.org/abs/2408.06195", "source": "arxiv", "axis": "testtime", "layer": 0}
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{"type": "node", "id": "lab-google-deepmind", "kind": "lab", "label": "Google DeepMind", "url": "https://deepmind.google", "source": "web", "axis": "testtime", "layer": 1}
{"type": "node", "id": "lab-googleresearch", "kind": "lab", "label": "Google Research", "url": "https://research.google", "source": "web", "axis": "matryoshka", "layer": 1}
{"type": "node", "id": "lab-microsoft-research", "kind": "lab", "label": "Microsoft Research", "url": "https://www.microsoft.com/en-us/research", "source": "web", "axis": "ternary", "layer": 1}
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{"type": "node", "id": "lab-openai", "kind": "lab", "label": "OpenAI", "url": "https://openai.com", "source": "web", "axis": "testtime", "layer": 1}
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