arXiv:2502.09018v2 Announce Type: replace Abstract: Concept bottleneck models (CBMs) are inherently interpretable and intervenable neural network models, which explain their final label prediction by the intermediate prediction of high-level semantic concepts. However, they require target task training to learn input-to-concept…
arXiv:2604.02353v1 Announce Type: new Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents’ decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with…
arXiv:2510.06649v2 Announce Type: replace Abstract: The Forward-Forward (FF) Algorithm is a recently proposed learning procedure for neural networks that employs two forward passes instead of the traditional forward and backward passes used in backpropagation. However, FF remains largely confined to…
arXiv:2604.02355v1 Announce Type: new Abstract: Combining Chain-of-Thought (CoT) with Reinforcement Learning (RL) improves text-to-image (T2I) generation, yet the underlying interaction between CoT’s exploration and RL’s optimization remains unclear. We present a systematic entropy-based analysis that yields three key insights: (1)…
arXiv:2512.12744v2 Announce Type: replace Abstract: Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet existing approaches exhibit severe accuracy degradation at high sparsity. We show that this failure stems from…
arXiv:2604.02378v1 Announce Type: new Abstract: Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and…
arXiv:2602.09987v4 Announce Type: replace Abstract: Influence functions are commonly used to attribute model behavior to training documents. We explore the reverse: crafting training data that induces model behavior. Our framework, Infusion, uses scalable influence-function approximations to compute small perturbations to…
arXiv:2604.02393v1 Announce Type: new Abstract: Vanishing gradient and overfitting are two of the most extensively studied problems in the literature about machine learning. However, they are frequently considered in some asymptotic setting, which obscure the underlying dynamical mechanisms responsible for…
arXiv:2603.05433v5 Announce Type: replace Abstract: Reasoning models think out loud, but much of what they say is noise. We introduce CRISP (Compressed Reasoning via Iterative Self-Policy Distillation), a method that teaches models to reason more concisely by distilling their own…
arXiv:2604.02430v1 Announce Type: new Abstract: In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI…
