Traffic sensor data exhibit strong temporal and cross-sensor dependencies, yet long gaps erase both a sensor's local history and the joint observations needed to learn its relationships with other sensors. We introduce MoHL, a mixture of hypergraph Laplacians that learns group-wise relational structure directly from in...
Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing the parameter count. However, this reuse makes them particularly sensitive to post-training quantization (PTQ). We systematically study PTQ in LoopLMs and identify three sources of...
Rui Fang, Hsi-Wen Chen, Chia-Ming Cheng et al.· 0 citations
Concept Activation Vectors (CAV) and the Testing with CAVs (TCAV) method are fundamental tools for concept-based explainability in deep learning, yet their practical utility is limited by the variability across different samples of concept and non-concept examples. We analyze this stochastic nature of CAVs, and identif...
Ekkehard Schnoor, Jawher Said, Malik Tiomoko et al.· 0 citations
Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. Under these semantics, LLM decoding is blocked until each function call completes, resulting in increasing end-to-end latency. In this work, we introduce AsyncFC, a pure e...
Guangyu Feng, Huanzhi Mao, Prabal Dutta et al.· 0 citations
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Discrete MeanFlow is introduced, a theoretical finite-state formulation that replaces point motion with probability-mass transport and validate the identity on exact finite-state Markov chains, demonstrating that the learned kernel recovers the analytical ground truth with high precision.
Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed et al.· arXiv.org· 2 citations
In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated to the model and reward density where it is most informative. We identify a reward-density principle: sparse sequence-level reward is most useful on models that can explore and discover better behavior...
Yuanda Xu, Hejian Sang, Zhengze Zhou et al.· 0 citations
Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed for continuous control. Meanwhile, generative policies usually rely heavily on offline datasets and offline-to online RL is itself challenging, as the policy must impr...
Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong Ju· 0 citations
We propose a multimodal latent diffusion model that jointly synthesizes volumetric magnetic resonance imaging (MRI) and tabular clinical data within a shared latent space via cross-attention. This approach enables coherent joint representation learning of MRI and tabular modalities for generative modeling. Our model ut...
Daniel Mensing, Jan Kapar, Jochen G. Hirsch et al.· 0 citations
The discount factor in reinforcement learning controls both the effective planning horizon and the strength of bootstrapping, yet most deep RL methods use a single fixed value across all states. State-dependent discounting is a natural way to adapt temporal credit assignment to local state structure, but naively learni...
Yaomin Wang, Jianting Pan, Ran Tian et al.· 0 citations
This work proposes Selective Successor Eligibility Traces (S-trace), an critic-free eligibility trace method that enables more effective credit assignment and establishes an upper bound on its discrepancy from the raw eligibility traces-based policy gradient estimator.
Chao-Li Mou, Xin-Ning Chen, Zhan Zhuang et al.· 5 citations
Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood. We evaluate four parsers---the Biaffine LSTM, Stack-Pointer Network, AfroXLMR-large, and RemBERT---across twelve typologica...
Kevin Guan, Happy Buzaaba, Christiane Fellbaum· 0 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
