Papers with training-free inference framework
MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference (2026.acl-long)
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| Challenge: | Existing methods for expert parallelism inference suffer from a significant efficiency bottleneck . existing methods fail to address information heterogeneity and modality dynamics . |
| Approach: | They propose a training-free inference framework that scales experts without training . they propose an Entropy-Weighted Load mechanism to quantify the semantic value of visual tokens . |
| Outcome: | Experiments show that MACS outperforms existing methods on multimodal benchmarks. |
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)
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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu
| Challenge: | Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework . |
| Approach: | They propose a training-free inference framework that simulates a metacognitive self-correction process. |
| Outcome: | The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE. |
Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations (2026.acl-long)
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Wentao Hu, Yanbo Zhai, Xiaohui Hu, Mingkuan Zhao, Shanhong yu, Xue Liu, Kaidong Yu, Shuangyong Song, Xuelong Li
| Challenge: | Sparse Mixture-of-Experts models are vulnerable to hallucinations, authors say . static Top-k routing leaves "specialist experts" under-prioritized for specific tokens . |
| Approach: | They propose a training-free inference framework to awaken dormant experts . they propose 'counterfactual routing' to shift computational resources from syntax-dominant to knowledge-intensive layers . |
| Outcome: | Experiments show that CoR improves factual accuracy by 3.1% without increasing the inference budget. |