Router for Domain Experts
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Related Articles from SNS
Less is MoE: Trimming Experts in Domain-Specialist Language Models
arXiv:2606.05538v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models achieve strong performance through conditional computation, but their large parameter footprint poses deployment challenges. Prior MoE compression approaches catastrophically fail when evaluated on general-purpose benchmarks beyond commonsense reasoning.
IR3DE: A Linear Router for Large Language Models
Announce Type: new Abstract: Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LLMs. With the ever-growing list of available LLMs, inference routers are being proposed to select the most appropriate LLM for each prompt. However, existing routing methods either optimize cost across weak-to-strong generalist LLMs or require substantial training to support...
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably introduce irrelevant information that may mislead generation for complex reasoning. Inspired by the conditional computation of mixture of experts (MoE), where a router sparsely selects specialized experts alongside shared ones for each input, we propose \textbf{M}ixture...
DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection
arXiv:2511.01192v2 Announce Type: replace Abstract: Detecting machine-generated text has become a critical challenge amid the rapid advancement of LLMs, yet existing detectors degrade severely under domain shift. Through systematic pilot studies, we trace this vulnerability to two fundamental flaws in current generalization strategies, namely the incomplete preservation of domain-specific knowledge during multi-domain training and the misalignment between knowledge retrieval and the...
Anatomy of a high-performance EP kernel
Anatomy of a high-performance EP kernel Large language models are large. Because they’re large, we need lots of GPUs to run them. It would be nice if LLM inference were ‘embarrassingly parallel’ and we could just always compute independent things on different GPUs.
Triaging Threats to Specialized Guardrails
arXiv:2605.30693v1 Announce Type: new Abstract: Building robust safety guardrails is essential for deploying Large Language Models across diverse real-world applications. However, this goal remains challenging because safety risks span heterogeneous threat domains, while existing datasets cover only fragmented risk subsets and rely on inconsistent taxonomies. Consequently, it remains unclear whether current guardrails can generalize beyond narrow evaluation settings.
MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry
Announce Type: new Abstract: Robust inertial odometry is essential for various carriers when external sensing is unreliable. Learning-based methods reduce integration drift by capturing local motion priors, but these methods often remain tied to a particular carrier, limiting generalization across heterogeneous platforms. We present MosaicIMU, a carrier-conditioned Mixture-of-Experts (MoE) pretraining-and-adaptation framework for generalizable neural inertial odometry.
Adaptive Minds: Empowering Agents with LoRA-as-Tools
Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke. We hypothesize that, when adapters are trained to provide strong domain-specific gains and are exposed with clear metadata, a base model can reliably route queries to the appropriate expert, effectively aggregating the benefits of many specialized adapters within a single framework. We introduce Adaptive Minds, a...
Why your VPN keeps getting blocked and the simple fix
You fire up your VPN, connect to a server and pull up the streaming service or website you were trying to reach. A few seconds later, you see the dreaded message: blocked. Then you switch servers.
Why your VPN keeps getting blocked and the simple fix
You fire up your VPN, connect to a server and pull up the streaming service or website you were trying to reach. A few seconds later, you see the dreaded message: blocked. Then you switch servers.