arXiv Computation and Language
3d ago

Multi-View Latent MoE: Letting Every Router Shape Expert Computation

The paper introduces Multi-View Latent MoE, a sparse mixture‑of‑experts architecture that uses multiple independent routers to generate richer selection signals and to modulate expert computation. By aggregating sigmoid scores from these routers, the model selects experts that are strongly favored by one or several routers and mixes their relative scores into each expert’s input, thereby reusing routing preferences for both selection and computation. Experiments on a 24‑layer 10B‑A1B model trained on 708 B tokens show a 1.18‑point improvement in nine‑task downstream mean over the baseline, with the Multi‑Router submodule alone providing a 0.6‑point gain and significantly better alignment between router scores and expert utility. whyItMatters":"The approach demonstrates that leveraging multiple routers can both improve expert selection quality and maintain computational efficiency, leading to measurable downstream performance gains on large‑scale language models."

By Zelin Zhao, Junyuan Shang, Tianchi Ma, Shuohuan Wang, Dianhai Yu
arXiv Computation and Language
Oct 28

PRISM-BENCH: Diagnosing Grounded and Faithful Multimodal Reasoning Beyond Final-Answer Accuracy

PRISM-BENCH is a new benchmark comprising 1,000 expert‑curated vision‑language questions across five under‑represented reasoning categories, each paired with an idealized reasoning trace validated by a second annotator. It introduces diagnostics beyond final‑answer accuracy: a perception probe that attributes about 85% of frontier‑model failures to misperception, and trace alignment that predicts answer correctness with a macro AUROC of 0.66. The benchmark also presents CORE, an efficiency‑aware metric that reorders 24.2% of model pairs relative to accuracy while remaining uncorrelated with response length, revealing that recent gains are driven by recognition rather than spatial grounding.

By Vansh Bhatia, Mehdi Rezagholizadeh, Parsa Ashrafi Fashi, Igor Gilitschenski, Vikram Appia, Emad Barsoum
arXiv AI
Jun 16

ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering

arXiv:2606. 14772v1 Announce Type: cross Abstract: Aerial Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (UAVs) to actively perceive the environment and answer natural language questions.

By Wenhao Lu, Zhengqiu Zhu, Xiaofeng Wang, Xiaoran Zhang, Yatai Ji, Yong Zhao, Yue Hu, Yingzhen Nie, Jinlong Zhu, Zheng Zhu
arXiv AI
Oct 7

When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Models

The paper introduces MOTIVE, a multi-view self-verification framework for vision-language models that evaluates candidate answers from complementary perspectives and learns a reliability score aligned with correctness. By using this score to decide whether to accept an answer or trigger history-guided rethinking, MOTIVE improves reliability and efficiency compared to existing self-verification and self-correction methods. Extensive experiments on diverse multimodal benchmarks and VLM backbones show consistent performance gains and reduced unnecessary reasoning turns.

By Ziquan Zhu, Hanruo Zhu, Si-Yuan Lu, Morris Yu-Chao Huang, Yicheng Lin, Wei Han, Tianlong Chen, Mingyuan Wu, Hanchao Yu, Gaojie Jin, Lu Liu, Bo Sun, Tianjin Huang
arXiv AI
Aug 19

Beyond the Trace: Coupling an Interpretable Reasoning-State Readout to Native MoE Routing

The paper introduces a two‑level readout for mixture‑of‑experts reasoning models. First, it compresses the model’s internal reasoning states into a 64‑dimensional semantic frame (J64) that reveals process dynamics beyond the emitted trace. Second, it reconstructs this frame from native expert‑routing statistics (R64), achieving high correlation and preserving most predictive gains while enabling low‑overhead, test‑time decision making.

By Kang Chen, Sihan Zhao, Yixin Cao, Yugang Jiang
arXiv AI
2d ago

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.

By Shengjie Wang, Mohsen Fayyaz, Nanyun Peng