AGENTIC-SYSTEMSApril 3, 20261 min read18 views

Self-Routing: Parameter-Free Expert Routing from Hidden States

Researchers propose Self-Routing, a parameter-free mechanism for Mixture-of-Experts (MoE) that uses hidden state subspaces for expert assignment, achieving competitive results and better load balancing without a dedicated router.

Computer Science > Artificial Intelligence

Title:Self-Routing: Parameter-Free Expert Routing from Hidden States

Abstract:Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely on a learned router to map hidden states to expert assignments. In this work, we ask whether a dedicated learned router is strictly necessary in the MoE settings we study. We propose Self-Routing, a parameter-free routing mechanism that uses a designated subspace of the token hidden state directly as expert logits, eliminating the router projection entirely while leaving the rest of the MoE layer unchanged. We evaluate Self-Routing on GPT-2-scale language modeling and ImageNet-1K classification by comparing it against a standard learned router, random-routing baselines, and dense non-MoE baselines. Our results show that Self-Routing remains competitive with the learned-router baseline while removing all dedicated routing parameters, and yields more balanced expert utilization, with about 17 % higher average normalized routing entropy and no explicit load-balancing loss. On ImageNet-1K with DeiT-S/16, Self-Routing also slightly improves over the corresponding learned-router MoE. These findings suggest that effective MoE routing can emerge from the hidden representation itself without requiring a separate learned router module.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

As LLMs tackle longer tasks, retaining complete interaction histories becomes contextually and computationally expensive. The ABBEL framework addresses this by isolating summaries into natural-language 'belief states' and supervising them via belief grading, recovering accuracy while significantly cutting memory usage.

agentic-systems

DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding

Researchers have introduced DC-Leap, a training-free framework that accelerates inference in Diffusion Large Language Models (dLLMs) by up to 105x when combined with KV-Cache while maintaining generation quality.

agentic-systems

Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

A new benchmark evaluates five leading large language models on multi-sensor physical hazard assessments, revealing a critical vulnerability when evaluating combined sensor risks below individual thresholds. Despite near-perfect accuracy on single-sensor breaches, all models failed to issue precautionary signals for multi-sensor hazards.

agentic-systems

Benchmarking the Personalization Capabilities of Large Language Models

A new study introduces SDR-Bench and SDR-Arena to benchmark the personalization capabilities of LLMs in two-party persuasion scenarios, revealing a personalization plateau among frontier models.

agentic-systems

VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

Researchers have introduced VeriSimpl, a novel framework that leverages LLMs and simplification-based verification to accurately translate natural language descriptions into executable optimization formulations.

agentic-systems

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

Researchers introduced SonicSampler, a unified suite of tile-aware Triton kernels designed to accelerate LLM sampling and speculative verification. By fusing the entire pipeline into a single CUDA Graph-compatible kernel, SonicSampler achieves up to 16x speedups over state-of-the-art baselines.