ARGUS delivers always-on, sub-2% overhead, multi-layer tracing plus 3700× kernel compression and progressive diagnosis that localizes fail-slow faults in 10k-GPU LLM training clusters.
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Hunyuan$large: An open$source moe model with 52 billion activated parameters by tencent
16 Pith papers cite this work. Polarity classification is still indexing.
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A large-scale empirical study across tokenizers and diffusion backbones identifies Velocity Irreducible Variance (VIV) as one of the most stable predictors of latent diffusion generation quality.
BBCritic reframes GUI critique as continuous semantic alignment via contrastive learning in an affordance space, outperforming larger binary SOTA models on a new four-level hierarchical benchmark without extra annotations.
TunerDiT adds event-partitioned masking and cross-event prompt fusion to diffusion transformers for training-free multi-event video generation, with gains scaling by event count on a new Meve benchmark.
MaterEval generates paired informed and blind evaluations as preference signals to improve small open-source LLMs on high-entropy alloy assessment, approaching closed-source performance without external retrieval.
SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
Comprehensive profiling of expert selection in frontier MoE models reveals temporal and spatial patterns that enable 6.6x speedup on wafer-scale GPUs and 1.25x on existing systems via targeted optimizations.
A model-independent upper bound on generalization gap is established that depends solely on the Rényi entropy of the data-generating distribution for histogram-determined algorithms such as ERM.
VBench-2.0 is a benchmark suite that automatically evaluates video generative models on five dimensions of intrinsic faithfulness: Human Fidelity, Controllability, Creativity, Physics, and Commonsense using VLMs, LLMs, and anomaly detection methods.
ResilPhase accelerates DiT inference via stable ODE macro-trajectory extrapolation with derivative-free barycentric Lagrange extrapolator and bounded phase mapping, claiming SOTA fidelity at high acceleration ratios on FLUX.1-dev and HunyuanVideo.
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
ARGUS uses a Prosecutor-Defender-Umpire multi-agent setup plus RAG and chain-of-thought rewards to adapt ad policy enforcement to new regulations using minimal fresh labels.
Orthogonal growth recycles pre-trained MoE checkpoints via layer copying and noisy expert duplication, delivering 10.6% higher accuracy than training from scratch with equivalent extra compute.
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.
Healink is a multi-agent framework using memory, relational databases, and constraint-based RAG to produce traceable post-discharge care responses that outperformed physician baselines in expert evaluations on 485 real cases.
A 7B-parameter domain-specific image captioning model for ICT, trained in three stages on synthesized and annotated data, outperforms 32B-parameter general models on BLEU and expert accuracy metrics.
citing papers explorer
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ARGUS: Production-Scale Tracing and Performance Diagnosis for over 10,000-GPU Clusters
ARGUS delivers always-on, sub-2% overhead, multi-layer tracing plus 3700× kernel compression and progressive diagnosis that localizes fail-slow faults in 10k-GPU LLM training clusters.
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Diffusing in the Right Space: A Systematic Study of Latent Diffusability
A large-scale empirical study across tokenizers and diffusion backbones identifies Velocity Irreducible Variance (VIV) as one of the most stable predictors of latent diffusion generation quality.
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Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment
BBCritic reframes GUI critique as continuous semantic alignment via contrastive learning in an affordance space, outperforming larger binary SOTA models on a new four-level hierarchical benchmark without extra annotations.
-
TunerDiT: Training-free Progressive Steering of Diffusion Transformer for Multi-Event Video Generation
TunerDiT adds event-partitioned masking and cross-event prompt fusion to diffusion transformers for training-free multi-event video generation, with gains scaling by event count on a new Meve benchmark.
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From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
MaterEval generates paired informed and blind evaluations as preference signals to improve small open-source LLMs on high-entropy alloy assessment, approaching closed-source performance without external retrieval.
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SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
-
Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
Comprehensive profiling of expert selection in frontier MoE models reveals temporal and spatial patterns that enable 6.6x speedup on wafer-scale GPUs and 1.25x on existing systems via targeted optimizations.
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Overfitting has a limitation: a model-independent generalization gap bound based on R\'enyi entropy
A model-independent upper bound on generalization gap is established that depends solely on the Rényi entropy of the data-generating distribution for histogram-determined algorithms such as ERM.
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VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
VBench-2.0 is a benchmark suite that automatically evaluates video generative models on five dimensions of intrinsic faithfulness: Human Fidelity, Controllability, Creativity, Physics, and Commonsense using VLMs, LLMs, and anomaly detection methods.
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ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration
ResilPhase accelerates DiT inference via stable ODE macro-trajectory extrapolation with derivative-free barycentric Lagrange extrapolator and bounded phase mapping, claiming SOTA fidelity at high acceleration ratios on FLUX.1-dev and HunyuanVideo.
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CogniRoute: Learning to Route Social Evidence in Omni-Modal Models
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
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ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring
ARGUS uses a Prosecutor-Defender-Umpire multi-agent setup plus RAG and chain-of-thought rewards to adapt ad policy enforcement to new regulations using minimal fresh labels.
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Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
Orthogonal growth recycles pre-trained MoE checkpoints via layer copying and noisy expert duplication, delivering 10.6% higher accuracy than training from scratch with equivalent extra compute.
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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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Bridging the Post-discharge Gap: A Traceable Multi-agent Framework for Safe and Continuous Care
Healink is a multi-agent framework using memory, relational databases, and constraint-based RAG to produce traceable post-discharge care responses that outperformed physician baselines in expert evaluations on 485 real cases.
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Multi-Modal LLM based Image Captioning in ICT: Bridging the Gap Between General and Industry Domain
A 7B-parameter domain-specific image captioning model for ICT, trained in three stages on synthesized and annotated data, outperforms 32B-parameter general models on BLEU and expert accuracy metrics.