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
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: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
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
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
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
ACE introduces a training‑free, calibration‑free framework for adaptive expert skipping in Mixture‑of‑Experts LLMs. It combines a Global Spectral Proxy that estimates global transformation capacity with a Router‑Conditioned Refinement that builds expert‑specific direction prototypes, enabling the model to skip low‑contribution experts while always keeping the top‑1 expert. Offline computation of expert statistics leaves only lightweight table lookups during inference, and experiments on three MoE‑based LLMs show ACE outperforms static and dynamic baselines, especially at high skipping ratios.
By Zukang Xu, Zhixiong Zhao, Xing Hu, Jiangyong Yu, Houji Wen, Jun Li, Zhe Jiang, Dawei Yang
The paper investigates whether a sparse dictionary can reuse the routing decisions already made by a mixture‑of‑experts (MoE) model. Using the NR‑SAE approach, each routed expert is given a small dictionary that is opened only when the frozen router selects that expert. Experiments on Gemma 4 26B‑A4B show that the expert‑aligned autoencoder explains 51.8 % of the variance in router‑weighted expert outputs, outperforming several control variants and suggesting that the router already encodes useful information that a dictionary would otherwise need to learn.
By Andrew Bistras, Marty Johnson
EgoP$^{2}$C is a new framework that internalizes a plan‑perceive‑compose workflow within a single vision‑language model for spatial reasoning in egocentric video. It trains specialized Planner and Perception teachers via Dual‑LoRA adapters, then fine‑tunes a mixture‑of‑experts student under their supervision, using a segment‑level multi‑teacher on‑policy distillation to guide evidence‑seeking, stopping, and answer composition. The resulting student models, built on Qwen3‑VL‑8B and SenseNova‑SI‑1.3‑8B, outperform their base models by 9.2 and 7.5 percentage points on three benchmarks, while cutting inference FLOPs by about 66% and speeding up inference 1.29–1.49×.
By Chaoyu Li, Arka Sadhu, Yuliang Li, Hyo Jin Kim, Yuning Chai, Pooyan Fazli
The paper introduces Humanity's Sixth Sense (HSS), a benchmark designed to evaluate intuitive visual reasoning in multimodal large language models. HSS covers a wide range of image and video inputs, organizes them under a structured taxonomy, and pairs each with human‑written prompts that probe implicit temporal, spatial, social, and abstract cues that humans can infer at a glance. Current state‑of‑the‑art models, including GPT‑6‑astra, achieve far lower accuracy than humans (53.6 % vs. 93.1 %), highlighting a significant gap in intuitive visual understanding.
By Xingang Guo, Jing Gu, Brian Jang, Renxiong Wang, Utkarsh Tyagi, Daniel Quigley, Steven Li, David Yan, Daniel Yue Zhang, Darvin Yi, Forrest Huang, HiJae Kim, Tianyi Zhang, Jared Lichtarge, Jihua Huang, Le Xue, Manan Tomar, Qiuyi Richard Zhang, Ruofei Yu, Seth Neel, Yaning Hu, Marcella Valentine, Xinzhe Jiang, Daniel Evans, Chenguang Wang, Dustin Tran, Tong Zhao, Yinfei Yang, Yunzhong He
PerceptRL enhances visual perception in multimodal large language models by employing reinforcement learning that transforms dense image captions into verified QA pairs with rule‑based rewards. It uses a multi‑agent pipeline to link correlated errors, combine unrelated errors, and target high‑error domains, while a specialized multi‑turn inspection process corrects spatial inaccuracies. Across seven benchmarks, PerceptRL improves average accuracy to 71.6%, outperforming both the base model with tools and the leading evaluated VLM by significant margins.
By Tongkun Guan, Zhibo Yang, Mingkun Yang, Haocheng Wang, Wei Shen, Xiaokang Yang
EgoPathBench is a new dataset and benchmark that tests zero‑shot egocentric waypoint decision‑making in vision‑language models. Each task presents an egocentric RGB image, a natural‑language goal, and numbered visible waypoints, and models must return traversable candidates or an ordered route. The benchmark evaluates candidate feasibility, edge legality, and goal arrival under point‑agent or embodied geometry, covering 31,852 training, 1,345 validation, and 1,111 benchmark questions.
"whyItMatters":"The benchmark reveals that current VLMs perform poorly on integrated navigation tasks, highlighting a gap in spatial intelligence that can be addressed by fine‑tuning with the released training data."
By Yang Zhao, Zhuo Chen, Xubo Yang