arXiv AI
From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Nov 5
Routes of Reasoning: Reading and Reshaping Test-Time Compute in MoE Models
The paper introduces a method to control reasoning behaviors in Mixture-of-Experts (MoE) language models by labeling sentence-level segments with six distinct reasoning behaviors (planning, execution, restatement, fact, reflection, and conclusion). Linear probes on MoE router logits can predict these behaviors nearly as accurately as probes on the full hidden state, enabling the authors to edit router scores during inference to reduce overthinking. This intervention cuts generated tokens by a median of 30.4% (up to 63.3%) across multiple models and benchmarks without significant loss in accuracy, and often improves it.
arXiv AI
Aug 5
Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility
arXiv:2608. 04001v1 Announce Type: cross Abstract: Large language models can solve substantially harder reasoning problems with more inference-time compute.
arXiv Machine Learning
Jul 31
Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
arXiv:2607. 28308v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account of their benefit: co-selected experts should contribute distinct representation directions.
Hugging Face Trending Papers
Jul 30
Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account of their benefit: co-selected experts should contribute distinct representation directions. Existing evidence often conflates route coherence, candidate quality, and candidate-by-context interaction.
Hugging Face Trending Papers
Aug 18
Beyond the Trace: Coupling an Interpretable Reasoning-State Readout to Native MoE Routing
The paper introduces a two‑level internal readout for mixture‑of‑experts reasoning models. First, it compresses the model’s reasoning states into a 64‑axis semantic frame (J64) that reveals process states not captured by the emitted trace, improving held‑out AUC by 0.096–0.135. Second, it reconstructs this frame from native expert‑routing statistics (R64), achieving high correlation with J64 and preserving most of its predictive gain while enabling low‑overhead, test‑time decision making and improved routing policies.
arXiv AI
Sep 7
When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models
The paper examines how expert pruning—removing low‑importance experts in Mixture‑of‑Experts models—fails when the router is over‑dispersed, a condition caused by aggressive load‑balancing that spreads tokens nearly uniformly across experts. In this regime, traditional importance signals from router probabilities collapse, making perplexity an unreliable predictor of downstream accuracy; for example, the lowest‑perplexity pruning on gpt‑oss‑20B harms mathematical reasoning while the highest‑perplexity pruning preserves it. To address this, the authors introduce Minimax Expert Score Allocation (MESA), a domain‑aware method that iteratively boosts scores for the most affected domain, achieving minimal worst‑case degradation across domains and outperforming baseline pruning strategies on multiple benchmarks while reducing memory usage.