MultiHashFormer enables hash-based autoregression in LMs by encoding tokens as multi-hash signatures, outperforming standard Transformers at 100M-3B scales while keeping parameter count constant for multilingual expansion.
Think you have solved direct-answer question answering? try arc-da, the direct-answer ai2 reasoning challenge
10 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 10representative citing papers
VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
RUQuant uses block-wise composite orthogonal matrices from Householder reflections and Givens rotations plus a fine-tuned global reflection to achieve 99.8% full-precision accuracy at W6A6 and 97% at W4A4 for 13B LLMs in about one minute.
DITS replaces Q-value guidance in MCTS with influence scores for synthetic data synthesis in multi-agent LLM training, claiming better efficiency and performance on eight datasets.
CRAG improves RAG robustness via a retrieval quality evaluator that triggers web augmentation and a decompose-recompose filter to focus on relevant information, yielding better results on short- and long-form generation tasks.
SoftMoE replaces discrete top-k routing in MoE LLMs with a truncated soft top-k LapSum relaxation, enabling differentiable optimization, learned non-uniform expert allocation across layers under a budget constraint, and comparable or better performance with fewer activations.
DLA introduces adaptive state merging based on token information variation plus fixed-size memory management for linear attention, reporting better results than fixed-policy baselines on 16 datasets across three categories.
SEPTQ simplifies LLM post-training quantization to two steps via static global importance scoring and mask-guided column-wise weight updates, claiming superior results over baselines in low-bit settings.
Technical report announcing Ling-2.6 and Ring-2.6 models with hybrid linear attention, evolutionary CoT, and KPop RL for efficient agentic intelligence at scale.
LLM accuracy on reasoning tasks differs significantly by question type, with step-by-step reasoning accuracy often uncorrelated to final answer selection.
citing papers explorer
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MultiHashFormer: Hash-based Generative Language Models
MultiHashFormer enables hash-based autoregression in LMs by encoding tokens as multi-hash signatures, outperforming standard Transformers at 100M-3B scales while keeping parameter count constant for multilingual expansion.
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Vector Retrieval with Similarity and Diversity: How Hard Is It?
VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
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RUQuant: Towards Refining Uniform Quantization for Large Language Models
RUQuant uses block-wise composite orthogonal matrices from Householder reflections and Givens rotations plus a fine-tuned global reflection to achieve 99.8% full-precision accuracy at W6A6 and 97% at W4A4 for 13B LLMs in about one minute.
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Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search
DITS replaces Q-value guidance in MCTS with influence scores for synthetic data synthesis in multi-agent LLM training, claiming better efficiency and performance on eight datasets.
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Corrective Retrieval Augmented Generation
CRAG improves RAG robustness via a retrieval quality evaluator that triggers web augmentation and a decompose-recompose filter to focus on relevant information, yielding better results on short- and long-form generation tasks.
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SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
SoftMoE replaces discrete top-k routing in MoE LLMs with a truncated soft top-k LapSum relaxation, enabling differentiable optimization, learned non-uniform expert allocation across layers under a budget constraint, and comparable or better performance with fewer activations.
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Dynamic Linear Attention
DLA introduces adaptive state merging based on token information variation plus fixed-size memory management for linear attention, reporting better results than fixed-policy baselines on 16 datasets across three categories.
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SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
SEPTQ simplifies LLM post-training quantization to two steps via static global importance scoring and mask-guided column-wise weight updates, claiming superior results over baselines in low-bit settings.
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Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Technical report announcing Ling-2.6 and Ring-2.6 models with hybrid linear attention, evolutionary CoT, and KPop RL for efficient agentic intelligence at scale.
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Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?
LLM accuracy on reasoning tasks differs significantly by question type, with step-by-step reasoning accuracy often uncorrelated to final answer selection.