Search: moe - ai.jp.net

infrastructure #llm 📝 BlogAnalyzed: Jan 16, 2026 16:01

Open Source AI Community: Powering Huge Language Models on Modest Hardware

Published:Jan 16, 2026 11:57

•

1 min read

•

r/LocalLLaMA

Analysis

The open-source AI community is truly remarkable! Developers are achieving incredible feats, like running massive language models on older, resource-constrained hardware. This kind of innovation democratizes access to powerful AI, opening doors for everyone to experiment and explore.

Key Takeaways

•Open-source projects like llama.cpp and vllm are enabling efficient running of large language models.
•Users are successfully running models with 30B parameters on systems with limited VRAM (4GB).
•Sufficient system memory and MoE (Mixture of Experts) architectures are key to good performance.

Reference

“I'm able to run huge models on my weak ass pc from 10 years ago relatively fast...that's fucking ridiculous and it blows my mind everytime that I'm able to run these models.”

Permalink r/LocalLLaMA

research #llm 📝 BlogAnalyzed: Jan 15, 2026 08:00

DeepSeek AI's Engram: A Novel Memory Axis for Sparse LLMs

Published:Jan 15, 2026 07:54

•

1 min read

•

MarkTechPost

Analysis

DeepSeek's Engram module addresses a critical efficiency bottleneck in large language models by introducing a conditional memory axis. This approach promises to improve performance and reduce computational cost by allowing LLMs to efficiently lookup and reuse knowledge, instead of repeatedly recomputing patterns.

Key Takeaways

•Engram is a new conditional memory module designed for Sparse LLMs.
•It aims to improve efficiency by allowing LLMs to perform knowledge lookup.
•The module works alongside existing Mixture-of-Experts (MoE) architectures.

Reference

“DeepSeek’s new Engram module targets exactly this gap by adding a conditional memory axis that works alongside MoE rather than replacing it.”

Permalink MarkTechPost

AI Research #LLMs, LoRA, Mixture of Experts, Context Switching 📝 BlogAnalyzed: Jan 3, 2026 15:36

Temporal LoRA: Dynamic Adapter Router for Context Switching in LLMs

Published:Jan 3, 2026 15:27

•

1 min read

•

r/LocalLLaMA

Analysis

This article presents an interesting experimental approach to improve multi-tasking and prevent catastrophic forgetting in language models. The core idea of Temporal LoRA, using a lightweight gating network (router) to dynamically select the appropriate LoRA adapter based on input context, is promising. The 100% accuracy achieved on GPT-2, although on a simple task, demonstrates the potential of this method. The architecture's suggestion for implementing Mixture of Experts (MoE) using LoRAs on larger local models is a valuable insight. The focus on modularity and reversibility is also a key advantage.

Key Takeaways

•Temporal LoRA introduces a dynamic adapter router for context switching in LLMs.
•Achieved 100% accuracy on GPT-2 in distinguishing between coding and literary prompts.
•Suggests a clean way to implement Mixture of Experts (MoE) using LoRAs on larger local models.
•Focuses on modularity and reversibility in learning.

Reference

“The router achieved 100% accuracy in distinguishing between coding prompts (e.g., import torch) and literary prompts (e.g., To be or not to be).”

Permalink r/LocalLLaMA

research #llm 📝 BlogAnalyzed: Jan 3, 2026 12:30

Granite 4 Small: A Viable Option for Limited VRAM Systems with Large Contexts

Published:Jan 3, 2026 11:11

•

1 min read

•

r/LocalLLaMA

Analysis

This post highlights the potential of hybrid transformer-Mamba models like Granite 4.0 Small to maintain performance with large context windows on resource-constrained hardware. The key insight is leveraging CPU for MoE experts to free up VRAM for the KV cache, enabling larger context sizes. This approach could democratize access to large context LLMs for users with older or less powerful GPUs.

Key Takeaways

•Granite 4.0 Small (32B total / 9B activated) maintains ~7 tkps with a 50k token context on a Thinkpad P15 with 8GB VRAM.
•Offloading MoE experts to CPU frees up VRAM for a larger KV cache, enabling larger context windows.
•Hybrid transformer-Mamba architecture contributes to sustained performance as context fills.

Reference

“due to being a hybrid transformer+mamba model, it stays fast as context fills”

Permalink r/LocalLLaMA

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 07:00

Prime Intellect Unveils Recursive Language Models (RLM): Paradigm shift allows AI to manage own context and solve long-horizon tasks

Published:Jan 2, 2026 10:33

•

1 min read

•

r/singularity

Analysis

This article reports on the unveiling of Recursive Language Models (RLMs) by Prime Intellect, a new approach to handling long-context tasks in LLMs. The core innovation is treating input data as a dynamic environment, avoiding information loss associated with traditional context windows. Key breakthroughs include Context Folding, Extreme Efficiency, and Long-Horizon Agency. The release of INTELLECT-3, an open-source MoE model, further emphasizes transparency and accessibility. The article highlights a significant advancement in AI's ability to manage and process information, potentially leading to more efficient and capable AI systems.

Key Takeaways

•RLMs treat long prompts as dynamic environments, avoiding context rot.
•Context Folding delegates tasks to sub-LLMs and Python scripts.
•RLMs demonstrate extreme efficiency, outperforming standard models on long-context tasks.
•The system can maintain coherence over long-horizon tasks.
•INTELLECT-3, an open-source MoE model, is released alongside the research.

Reference

“The physical and digital architecture of the global "brain" officially hit a new gear.”

Permalink r/singularity

Research Paper #Materials Science, Thermoelectrics, 2D Materials 🔬 ResearchAnalyzed: Jan 3, 2026 06:20

Ultralow Thermal Conductivity of Monolayer SnTe2

Published:Dec 31, 2025 16:00

•

1 min read

•

ArXiv

Analysis

This paper investigates the thermal properties of monolayer tin telluride (SnTe2), a 2D metallic material. The research is significant because it identifies the microscopic origins of its ultralow lattice thermal conductivity, making it promising for thermoelectric applications. The study uses first-principles calculations to analyze the material's stability, electronic structure, and phonon dispersion. The findings highlight the role of heavy Te atoms, weak Sn-Te bonding, and flat acoustic branches in suppressing phonon-mediated heat transport. The paper also explores the material's optical properties, suggesting potential for optoelectronic applications.

Key Takeaways

•Monolayer SnTe2 exhibits ultralow lattice thermal conductivity.
•The low thermal conductivity is attributed to the material's atomic structure and bonding.
•The material shows potential for thermoelectric and optoelectronic applications.

Reference

“The paper highlights that the heavy mass of Te atoms, weak Sn-Te bonding, and flat acoustic branches are key factors contributing to the ultralow lattice thermal conductivity.”

Permalink ArXiv

Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 06:26

Compute-Accuracy Trade-offs in Open-Source LLMs

Published:Dec 31, 2025 10:51

•

1 min read

•

ArXiv

Analysis

This paper addresses a crucial aspect often overlooked in LLM research: the computational cost of achieving high accuracy, especially in reasoning tasks. It moves beyond simply reporting accuracy scores and provides a practical perspective relevant to real-world applications by analyzing the Pareto frontiers of different LLMs. The identification of MoE architectures as efficient and the observation of diminishing returns on compute are particularly valuable insights.

Key Takeaways

•Evaluates open-source LLMs considering both accuracy and computational cost.
•Identifies Mixture of Experts (MoE) architecture as a strong candidate for balancing performance and efficiency.
•Highlights a saturation point where increased compute yields diminishing accuracy gains.

Reference

“The paper demonstrates that there is a saturation point for inference-time compute. Beyond a certain threshold, accuracy gains diminish.”

Permalink ArXiv

Research Paper #AI Data Centers, Waste-to-Energy, Cooling Efficiency, Grid Resilience 🔬 ResearchAnalyzed: Jan 3, 2026 08:48

Waste-to-Energy for AI Data Centers: Cooling and Grid Resilience

Published:Dec 31, 2025 07:32

•

1 min read

•

ArXiv

Analysis

This paper addresses the growing challenge of AI data center expansion, specifically the constraints imposed by electricity and cooling capacity. It proposes an innovative solution by integrating Waste-to-Energy (WtE) with AI data centers, treating cooling as a core energy service. The study's significance lies in its focus on thermoeconomic optimization, providing a framework for assessing the feasibility of WtE-AIDC coupling in urban environments, especially under grid stress. The paper's value is in its practical application, offering siting-ready feasibility conditions and a computable prototype for evaluating the Levelized Cost of Computing (LCOC) and ESG valuation.

Key Takeaways

•Proposes an integrated Waste-to-Energy-AI Data Center configuration to address cooling and grid constraints.
•Focuses on energy-grade matching to utilize low-grade thermal output for cooling.
•Provides a framework for assessing the thermoeconomic feasibility of the integrated system.
•Offers siting-ready feasibility conditions and a computable prototype for LCOC and ESG valuation.

Reference

“The central mechanism is energy-grade matching: low-grade WtE thermal output drives absorption cooling to deliver chilled service, thereby displacing baseline cooling electricity.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 15:42

Joint Data Selection for LLM Pre-training

Published:Dec 30, 2025 14:38

•

1 min read

•

ArXiv

Analysis

This paper addresses the challenge of efficiently selecting high-quality and diverse data for pre-training large language models (LLMs) at a massive scale. The authors propose DATAMASK, a policy gradient-based framework that jointly optimizes quality and diversity metrics, overcoming the computational limitations of existing methods. The significance lies in its ability to improve both training efficiency and model performance by selecting a more effective subset of data from extremely large datasets. The 98.9% reduction in selection time compared to greedy algorithms is a key contribution, enabling the application of joint learning to trillion-token datasets.

Key Takeaways

•DATAMASK is a novel framework for joint data selection in LLM pre-training.
•It uses policy gradient-based optimization to efficiently select data based on quality and diversity metrics.
•Significantly reduces selection time compared to greedy algorithms.
•Achieves performance improvements on various LLM architectures.

Reference

“DATAMASK achieves significant improvements of 3.2% on a 1.5B dense model and 1.9% on a 7B MoE model.”

Permalink ArXiv

Research Paper #Large Language Models (LLMs), MoE, Training Infrastructure, Parallelization 🔬 ResearchAnalyzed: Jan 3, 2026 15:53

TeleChat3-MoE Training Report Overview

Published:Dec 30, 2025 11:42

•

1 min read

•

ArXiv

Analysis

This paper details the infrastructure and optimization techniques used to train large-scale Mixture-of-Experts (MoE) language models, specifically TeleChat3-MoE. It highlights advancements in accuracy verification, performance optimization (pipeline scheduling, data scheduling, communication), and parallelization frameworks. The focus is on achieving efficient and scalable training on Ascend NPU clusters, crucial for developing frontier-sized language models.

Key Takeaways

•Focus on infrastructure for training large MoE models.
•Details on accuracy verification and performance optimization techniques.
•Emphasis on efficient scaling on Ascend NPU clusters.
•Highlights advancements in parallelization frameworks.

Reference

“The paper introduces a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training, hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion.”

Permalink ArXiv

Research Paper #AI Security, LLMs, MoE 🔬 ResearchAnalyzed: Jan 3, 2026 15:57

RepetitionCurse: DoS Attacks on MoE LLMs

Published:Dec 30, 2025 05:24

•

1 min read

•

ArXiv

Analysis

This paper highlights a critical vulnerability in Mixture-of-Experts (MoE) large language models (LLMs). It demonstrates how adversarial inputs can exploit the routing mechanism, leading to severe load imbalance and denial-of-service (DoS) conditions. The research is significant because it reveals a practical attack vector that can significantly degrade the performance and availability of deployed MoE models, impacting service-level agreements. The proposed RepetitionCurse method offers a simple, black-box approach to trigger this vulnerability, making it a concerning threat.

Key Takeaways

•MoE LLMs are vulnerable to DoS attacks due to routing imbalances.
•Adversarial prompts can force all tokens to be routed to a small subset of experts.
•RepetitionCurse is a simple, black-box method to exploit this vulnerability.
•The attack significantly increases inference latency and degrades service availability.

Reference

“Out-of-distribution prompts can manipulate the routing strategy such that all tokens are consistently routed to the same set of top-$k$ experts, which creates computational bottlenecks.”

Permalink ArXiv

Paper #Computer Vision, Geo-localisation, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 18:24

Learnable Query Aggregation for Cross-view Geo-localisation

Published:Dec 30, 2025 01:51

•

1 min read

•

ArXiv

Analysis

This paper addresses the challenging problem of cross-view geo-localisation, which is crucial for applications like autonomous navigation and robotics. The core contribution lies in the novel aggregation module that uses a Mixture-of-Experts (MoE) routing mechanism within a cross-attention framework. This allows for adaptive processing of heterogeneous input domains, improving the matching of query images with a large-scale database despite significant viewpoint discrepancies. The use of DINOv2 and a multi-scale channel reallocation module further enhances the system's performance. The paper's focus on efficiency (fewer trained parameters) is also a significant advantage.

Key Takeaways

•Proposes a novel CVGL system to address viewpoint discrepancies.
•Employs DINOv2 backbone and a multi-scale channel reallocation module.
•Introduces a MoE-based aggregation module for adaptive feature processing.
•Achieves competitive performance with fewer parameters.

Reference

“The paper proposes an improved aggregation module that integrates a Mixture-of-Experts (MoE) routing into the feature aggregation process.”

Permalink ArXiv

Research Paper #Robotics, Humanoid Locomotion, Audio-Driven Animation 🔬 ResearchAnalyzed: Jan 3, 2026 16:02

Audio-Driven Expressive Humanoid Locomotion

Published:Dec 29, 2025 17:59

•

1 min read

•

ArXiv

Analysis

This paper addresses a significant limitation in humanoid robotics: the lack of expressive, improvisational movement in response to audio. The proposed RoboPerform framework offers a novel, retargeting-free approach to generate music-driven dance and speech-driven gestures directly from audio, bypassing the inefficiencies of motion reconstruction. This direct audio-to-locomotion approach promises lower latency, higher fidelity, and more natural-looking robot movements, potentially opening up new possibilities for human-robot interaction and entertainment.

Key Takeaways

•Proposes RoboPerform, a novel framework for direct audio-to-locomotion.
•Eliminates the need for explicit motion reconstruction, reducing latency and improving fidelity.
•Enables humanoid robots to perform music-driven dance and speech-driven gestures.
•Employs a ResMoE teacher policy and a diffusion-based student policy for audio style injection.

Reference

“RoboPerform, the first unified audio-to-locomotion framework that can directly generate music-driven dance and speech-driven co-speech gestures from audio.”

Permalink ArXiv

Research Paper #Machine Learning, Deep Learning, Mixture of Experts, Model Adaptation 🔬 ResearchAnalyzed: Jan 3, 2026 18:48

Dynamic Subspace Composition for Efficient Adaptation in MoE Models

Published:Dec 29, 2025 13:11

•

1 min read

•

ArXiv

Analysis

This paper addresses the challenges of representation collapse and gradient instability in Mixture of Experts (MoE) models, which are crucial for scaling model capacity. The proposed Dynamic Subspace Composition (DSC) framework offers a more efficient and stable approach to adapting model weights compared to standard methods like Mixture-of-LoRAs. The use of a shared basis bank and sparse expansion reduces parameter complexity and memory traffic, making it potentially more scalable. The paper's focus on theoretical guarantees (worst-case bounds) through regularization and spectral constraints is also a strong point.

Key Takeaways

•Proposes Dynamic Subspace Composition (DSC) to address issues in MoE models.
•DSC uses a shared basis bank and sparse expansion for efficient adaptation.
•Reduces parameter complexity and memory traffic compared to methods like Mixture-of-LoRAs.
•Employs regularization and spectral constraints for theoretical guarantees.

Reference

“DSC models the weight update as a residual trajectory within a Star-Shaped Domain, employing a Magnitude-Gated Simplex Interpolation to ensure continuity at the identity.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 18:49

Improving Mixture-of-Experts with Expert-Router Coupling

Published:Dec 29, 2025 13:03

•

1 min read

•

ArXiv

Analysis

This paper addresses a key limitation in Mixture-of-Experts (MoE) models: the misalignment between the router's decisions and the experts' capabilities. The proposed Expert-Router Coupling (ERC) loss offers a computationally efficient method to tightly couple the router and experts, leading to improved performance and providing insights into expert specialization. The fixed computational cost, independent of batch size, is a significant advantage over previous methods.

Key Takeaways

•Proposes a novel Expert-Router Coupling (ERC) loss to improve MoE models.
•ERC loss tightly couples the router's decisions with expert capabilities.
•Computationally efficient, with a fixed cost independent of batch size.
•Demonstrates improved performance on MoE-LLMs ranging from 3B to 15B parameters.
•Provides flexible control and tracking of expert specialization levels.

Reference

“The ERC loss enforces two constraints: (1) Each expert must exhibit higher activation for its own proxy token than for the proxy tokens of any other expert. (2) Each proxy token must elicit stronger activation from its corresponding expert than from any other expert.”

Permalink ArXiv

Research Paper #Particle Physics / Beyond Standard Model 🔬 ResearchAnalyzed: Jan 3, 2026 18:54

MoEDAL-MAPP for Long-Lived Particle Detection: A Mini-Review

Published:Dec 29, 2025 11:24

•

1 min read

•

ArXiv

Analysis

This mini-review highlights the unique advantages of the MoEDAL-MAPP experiment in searching for long-lived, charged particles beyond the Standard Model. It emphasizes MoEDAL's complementarity to ATLAS and CMS, particularly for slow-moving particles and those with intermediate electric charges, despite its lower luminosity.

Key Takeaways

•MoEDAL-MAPP is designed to detect long-lived, charged particles.
•It offers a complementary approach to ATLAS and CMS.
•It is particularly sensitive to slow-moving particles and those with intermediate charges.
•Its advantage lies in its passive, background-free detection method.

Reference

“MoEDAL's passive, background-free detection methodology offers a unique advantage.”

Permalink ArXiv

Paper #Computer Vision 🔬 ResearchAnalyzed: Jan 3, 2026 16:09

YOLO-Master: Adaptive Computation for Real-time Object Detection

Published:Dec 29, 2025 07:54

•

1 min read

•

ArXiv

Analysis

This paper introduces YOLO-Master, a novel YOLO-like framework that improves real-time object detection by dynamically allocating computational resources based on scene complexity. The use of an Efficient Sparse Mixture-of-Experts (ES-MoE) block and a dynamic routing network allows for more efficient processing, especially in challenging scenes, while maintaining real-time performance. The results demonstrate improved accuracy and speed compared to existing YOLO-based models.

Key Takeaways

•Proposes YOLO-Master, a novel YOLO-like framework for real-time object detection.
•Employs an Efficient Sparse Mixture-of-Experts (ES-MoE) block for adaptive computation.
•Achieves improved accuracy and speed, especially in challenging scenes.
•Outperforms existing YOLO-based models on benchmarks like MS COCO.

Reference

“YOLO-Master achieves 42.4% AP with 1.62ms latency, outperforming YOLOv13-N by +0.8% mAP and 17.8% faster inference.”

Permalink ArXiv

Paper #Federated Learning, Mixture-of-Experts, AI 🔬 ResearchAnalyzed: Jan 3, 2026 19:16

FLEX-MoE: Federated Mixture-of-Experts for Resource-Constrained FL

Published:Dec 28, 2025 20:32

•

1 min read

•

ArXiv

Analysis

This paper addresses the challenges of deploying Mixture-of-Experts (MoE) models in federated learning (FL) environments, specifically focusing on resource constraints and data heterogeneity. The key contribution is FLEX-MoE, a framework that optimizes expert assignment and load balancing to improve performance in FL settings where clients have limited resources and data distributions are non-IID. The paper's significance lies in its practical approach to enabling large-scale, conditional computation models on edge devices.

Key Takeaways

•Addresses resource constraints and data heterogeneity in Federated Learning (FL) for MoE models.
•Proposes FLEX-MoE, a framework for optimized expert assignment and load balancing.
•Employs client-expert fitness scores and an optimization-based algorithm.
•Aims to improve performance and maintain balanced expert utilization in FL settings.

Reference

“FLEX-MoE introduces client-expert fitness scores that quantify the expert suitability for local datasets through training feedback, and employs an optimization-based algorithm to maximize client-expert specialization while enforcing balanced expert utilization system-wide.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 10:00

Xiaomi MiMo v2 Flash Claims Claude-Level Coding at 2.5% Cost, Documentation a Mess

Published:Dec 28, 2025 09:28

•

1 min read

•

r/ArtificialInteligence

Analysis

This post discusses the initial experiences of a user testing Xiaomi's MiMo v2 Flash, a 309B MoE model claiming Claude Sonnet 4.5 level coding abilities at a fraction of the cost. The user found the documentation, primarily in Chinese, difficult to navigate even with translation. Integration with common coding tools was lacking, requiring a workaround using VSCode Copilot and OpenRouter. While the speed was impressive, the code quality was inconsistent, raising concerns about potential overpromising and eval optimization. The user's experience highlights the gap between claimed performance and real-world usability, particularly regarding documentation and tool integration.

Key Takeaways

•MiMo v2 Flash claims Claude-level coding at a significantly lower cost.
•Documentation is primarily in Chinese and difficult to navigate.
•Integration with common coding tools is lacking, requiring workarounds.

Reference

“2.5% cost sounds amazing if the quality actually holds up. but right now feels like typical chinese ai company overpromising”

Permalink r/ArtificialInteligence

Research Paper #Multi-modal Sentiment Analysis, Mixture-of-Experts, Temporal Alignment, MLLM 🔬 ResearchAnalyzed: Jan 3, 2026 19:39

Text-Routed MoE Model for Multi-Modal Sentiment Analysis

Published:Dec 28, 2025 01:58

•

1 min read

•

ArXiv

Analysis

This paper introduces TEXT, a novel model for Multi-modal Sentiment Analysis (MSA) that leverages explanations from Multi-modal Large Language Models (MLLMs) and incorporates temporal alignment. The key contributions are the use of explanations, a temporal alignment block (combining Mamba and temporal cross-attention), and a text-routed sparse mixture-of-experts with gate fusion. The paper claims state-of-the-art performance across multiple datasets, demonstrating the effectiveness of the proposed approach.

Key Takeaways

•Proposes TEXT, a new model for MSA.
•Utilizes explanations from MLLMs.
•Employs a temporal alignment block.
•Achieves state-of-the-art performance on multiple datasets.

Reference

“TEXT achieves the best performance cross four datasets among all tested models, including three recently proposed approaches and three MLLMs.”

Permalink ArXiv

Research Paper #Reinforcement Learning, Distributed Systems, LLMs 🔬 ResearchAnalyzed: Jan 3, 2026 19:54

RollArt: Accelerating Agentic RL Training with Disaggregated Infrastructure

Published:Dec 27, 2025 11:14

•

1 min read

•

ArXiv

Analysis

This paper addresses the challenge of efficiently training agentic Reinforcement Learning (RL) models, which are computationally demanding and heterogeneous. It proposes RollArc, a distributed system designed to optimize throughput on disaggregated infrastructure. The core contribution lies in its three principles: hardware-affinity workload mapping, fine-grained asynchrony, and statefulness-aware computation. The paper's significance is in providing a practical solution for scaling agentic RL training, which is crucial for enabling LLMs to perform autonomous decision-making. The results demonstrate significant training time reduction and scalability, validated by training a large MoE model on a large GPU cluster.

Key Takeaways

•RollArc is a distributed system designed for efficient agentic RL training.
•It utilizes hardware-affinity workload mapping, fine-grained asynchrony, and statefulness-aware computation.
•RollArc achieves significant training time reduction compared to baseline methods.
•The system demonstrates scalability by training a large MoE model on a large GPU cluster.

Reference

“RollArc effectively improves training throughput and achieves 1.35-2.05x end-to-end training time reduction compared to monolithic and synchronous baselines.”

Permalink ArXiv

Research Paper #Computer Vision, Object Detection, Semi-Supervised Learning, Infrared Imaging 🔬 ResearchAnalyzed: Jan 3, 2026 16:27

Scalpel-SAM: Semi-Supervised Infrared Object Detection

Published:Dec 27, 2025 05:59

•

1 min read

•

ArXiv

Analysis

This paper addresses the critical problem of data scarcity in infrared small object detection (IR-SOT) by proposing a semi-supervised approach leveraging SAM (Segment Anything Model). The core contribution lies in a novel two-stage paradigm using a Hierarchical MoE Adapter to distill knowledge from SAM and transfer it to lightweight downstream models. This is significant because it tackles the high annotation cost in IR-SOT and demonstrates performance comparable to or exceeding fully supervised methods with minimal annotations.

Key Takeaways

•Addresses data scarcity in IR-SOT using a semi-supervised approach.
•Leverages SAM as a teacher model.
•Proposes a two-stage paradigm: Prior-Guided Knowledge Distillation and Deployment-Oriented Knowledge Transfer.
•Employs a Hierarchical MoE Adapter.
•Achieves performance comparable to or surpassing fully supervised methods with minimal annotations.

Reference

“Experiments demonstrate that with minimal annotations, our paradigm enables downstream models to achieve performance comparable to, or even surpassing, their fully supervised counterparts.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 08:31

Strix Halo Llama-bench Results (GLM-4.5-Air)

Published:Dec 27, 2025 05:16

•

1 min read

•

r/LocalLLaMA

Analysis

This post on r/LocalLLaMA shares benchmark results for the GLM-4.5-Air model running on a Strix Halo (EVO-X2) system with 128GB of RAM. The user is seeking to optimize their setup and is requesting comparisons from others. The benchmarks include various configurations of the GLM4moe 106B model with Q4_K quantization, using ROCm 7.10. The data presented includes model size, parameters, backend, number of GPU layers (ngl), threads, n_ubatch, type_k, type_v, fa, mmap, test type, and tokens per second (t/s). The user is specifically interested in optimizing for use with Cline.

Key Takeaways

•Strix Halo performance with GLM-4.5-Air is being benchmarked.
•The user is seeking optimization advice and comparative data.
•ROCm 7.10 is used as the backend for the benchmarks.

Reference

“Looking for anyone who has some benchmarks they would like to share. I am trying to optimize my EVO-X2 (Strix Halo) 128GB box using GLM-4.5-Air for use with Cline.”

Permalink r/LocalLLaMA

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 02:06

Rakuten Announces Japanese LLM 'Rakuten AI 3.0' with 700 Billion Parameters, Plans Service Deployment

Published:Dec 26, 2025 23:00

•

1 min read

•

ITmedia AI+

Analysis

Rakuten has unveiled its Japanese-focused large language model, Rakuten AI 3.0, boasting 700 billion parameters. The model utilizes a Mixture of Experts (MoE) architecture, aiming for a balance between performance and computational efficiency. It achieved high scores on the Japanese version of MT-Bench. Rakuten plans to integrate the LLM into its services with support from GENIAC. Furthermore, the company intends to release it as an open-weight model next spring, indicating a commitment to broader accessibility and potential community contributions. This move signifies Rakuten's investment in AI and its application within its ecosystem.

Key Takeaways

•Rakuten has developed a Japanese-focused LLM with 700 billion parameters.
•The model uses a Mixture of Experts (MoE) architecture for efficiency.
•Rakuten plans to deploy the LLM in its services and release it as an open-weight model.

Reference

“Rakuten AI 3.0 is expected to be integrated into Rakuten's services.”

Permalink ITmedia AI+

Research #Graphene 🔬 ResearchAnalyzed: Jan 10, 2026 07:12

Synergistic Terahertz Response in Graphene: A Novel Approach to Energy Harvesting

Published:Dec 26, 2025 15:34

•

1 min read

•

ArXiv

Analysis

The research, published on ArXiv, explores the potential of combining coherent absorption and plasmon-enhanced graphene for improved terahertz photo-thermoelectric response. This could lead to advancements in energy harvesting and high-frequency detection applications.

Key Takeaways

•Explores the use of graphene for terahertz detection and energy harvesting.
•Investigates the synergy between coherent absorption and plasmon enhancement.
•Published on ArXiv, suggesting early-stage research and potential for future development.

Reference

“The research focuses on the synergistic effect of coherent absorption and plasmon-enhanced graphene.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:33

FUSCO: Faster Data Shuffling for MoE Models

Published:Dec 26, 2025 14:16

•

1 min read

•

ArXiv

Analysis

This paper addresses a critical bottleneck in training and inference of large Mixture-of-Experts (MoE) models: inefficient data shuffling. Existing communication libraries struggle with the expert-major data layout inherent in MoE, leading to significant overhead. FUSCO offers a novel solution by fusing data transformation and communication, creating a pipelined engine that efficiently shuffles data along the communication path. This is significant because it directly tackles a performance limitation in a rapidly growing area of AI research (MoE models). The performance improvements demonstrated over existing solutions are substantial, making FUSCO a potentially important contribution to the field.

Key Takeaways

•FUSCO is a new communication library designed for efficient data shuffling in Mixture-of-Experts (MoE) models.
•It addresses the performance bottleneck caused by inefficient data shuffling in existing communication libraries.
•FUSCO achieves significant speedups over existing solutions by fusing data transformation and communication.
•The library reduces training and inference latency in MoE tasks.

Reference

“FUSCO achieves up to 3.84x and 2.01x speedups over NCCL and DeepEP (the state-of-the-art MoE communication library), respectively.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 13:08

MiniMax M2.1 Open Source: State-of-the-Art for Real-World Development & Agents

Published:Dec 26, 2025 12:43

•

1 min read

•

r/LocalLLaMA

Analysis

This announcement highlights the open-sourcing of MiniMax M2.1, a large language model (LLM) claiming state-of-the-art performance on coding benchmarks. The model's architecture is a Mixture of Experts (MoE) with 10 billion active parameters out of a total of 230 billion. The claim of surpassing Gemini 3 Pro and Claude Sonnet 4.5 is significant, suggesting a competitive edge in coding tasks. The open-source nature allows for community scrutiny, further development, and wider accessibility, potentially accelerating progress in AI-assisted coding and agent development. However, independent verification of the benchmark claims is crucial to validate the model's true capabilities. The lack of detailed information about the training data and methodology is a limitation.

Key Takeaways

•MiniMax M2.1 is now open source, enabling wider access and community contributions.
•The model claims SOTA performance on coding benchmarks, surpassing established models.
•The MoE architecture with a large parameter count suggests a complex and potentially powerful model.

Reference

“SOTA on coding benchmarks (SWE / VIBE / Multi-SWE) • Beats Gemini 3 Pro & Claude Sonnet 4.5”

Permalink r/LocalLLaMA

Paper #AI in Healthcare 🔬 ResearchAnalyzed: Jan 3, 2026 16:36

MMCTOP: Multimodal AI for Clinical Trial Outcome Prediction

Published:Dec 26, 2025 06:56

•

1 min read

•

ArXiv

Analysis

This paper introduces MMCTOP, a novel framework for predicting clinical trial outcomes by integrating diverse biomedical data types. The use of schema-guided textualization, modality-aware representation learning, and a Mixture-of-Experts (SMoE) architecture is a significant contribution to the field. The focus on interpretability and calibrated probabilities is crucial for real-world applications in healthcare. The consistent performance improvements over baselines and the ablation studies demonstrating the impact of key components highlight the framework's effectiveness.

Reference

“”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:45

Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing

Published:Dec 21, 2025 10:27

•

1 min read

•

ArXiv

Analysis

The article likely presents a research paper on optimizing Mixture of Experts (MoE) models for serverless environments. The focus is on improving efficiency and reducing costs associated with inference. The use of serverless computing suggests a focus on scalability and pay-per-use models. The title indicates a technical contribution, likely involving novel techniques or architectures for MoE inference.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #Datasets 🔬 ResearchAnalyzed: Jan 10, 2026 09:01

Dataset Curation Challenges in Machine Learning: A Case Study on Thermoelectric Materials

Published:Dec 21, 2025 09:05

•

1 min read

•

ArXiv

Analysis

This article highlights the critical importance of high-quality datasets in ensuring the reliability of machine learning models. The case study on thermoelectric materials provides a specific, practical example of these challenges.

Key Takeaways

•Dataset quality is crucial for reliable machine learning.
•The study focuses on the specific challenges within thermoelectric materials.
•The article likely explores methods or strategies for improved dataset curation.

Reference

“The article's context revolves around dataset curation challenges in the context of thermoelectric materials.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:49

How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts

Published:Dec 21, 2025 05:37

•

1 min read

•

ArXiv

Analysis

This article, sourced from ArXiv, likely explores the optimization of Mixture-of-Experts (MoE) models. The core focus is on determining the ideal number of 'experts' within the MoE architecture to achieve optimal performance, specifically concerning semantic specialization. The research probably investigates how different numbers of experts impact the model's ability to handle diverse tasks and data distributions effectively. The title suggests a research-oriented approach, aiming to provide insights into the design and training of MoE models.

•PoseMoE is a new method for 3D human pose estimation.
•It utilizes a Mixture-of-Experts (MoE) network.
•The approach is based on monocular images.

Reference

“N/A - This is an abstract, not a news article with quotes.”

Permalink ArXiv