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Vision Language Models (VLMs) excel on visual benchmarks but fail systematically on tasks requiring abstract reasoning. Existing benchmarks document this failure but cannot say \emph{why} it happens or which cognitive capability is missing. We close this gap by adopting the Relational Match-to-Sample (RMTS) paradigm fr...
Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, and leverage it for downstream prediction. However, prior work demonstrated that LLMs struggle to use representations learned in context on a graph tracking task, where...
Multimodal Probabilistic Fusion (MMPF), a training-free auditing framework that treats each modality as an expert, estimates a task-label distribution from local neighborhood agreement and global prototype similarity, and then fuses modalities with predictive-entropy weighting in a product of experts is proposed.
Recent work on looped language models suggests that many reasoning problems benefit from greater computational depth rather than from additional independent parameters. Existing studies, however, focus almost exclusively on Transformer backbones, leaving open whether this principle also applies to state-space language...
A training-free adaptive pruning method designed specifically for batched inference in LRMs, built on two components: periodic top-k selection over the aggregated importance scores, unaffected by the shift that aggregation induces in the activation distribution, and based on the observation that important neurons re-fi...
A taxonomy of CoT is proposed consisting of Explicit CoT, which outputs all operations without aggregation, Composed CoT, which combines multiple operations into a single step, and Implicit CoT, which omits intermediate operations.
This study establishes a reliable approach of data generation, training, and benchmarking, paving the way toward further bootstrapping the quality of many-to-many translation for programming languages.
Readout Feedback (RoFB), a test-time intervention that converts intermediate predictions into token-wise pairwise coupling forces injected into the latent dynamics, is introduced, suggesting that closed-loop steering of latent dynamics can serve as a complementary inference-time control mechanism for recurrent reasonin...
Looped Mamba and Looped Hybrid Mamba-Transformer architectures, which repeatedly apply a shared Mamba block to introduce explicit finite-depth recurrent computation, are investigated and adaptive exit-state selection improves downstream performance at intermediate depths, while actual inference-time savings require add...
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