arXiv:2602.16829v1 Announce Type: new Abstract: When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only…
arXiv:2602.17211v1 Announce Type: cross Abstract: Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from moment constraints but require sampling via MCMC or Langevin dynamics, which typically exhibit exponential…
arXiv:2602.16833v1 Announce Type: new Abstract: Exploration remains a key bottleneck for reinforcement learning (RL) post-training of large language models (LLMs), where sparse feedback and large action spaces can lead to premature collapse into repetitive behaviors. We propose Verbalized Action Masking…
arXiv:2602.17395v1 Announce Type: cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes. Training a parametric classifier solely on image features often leads to overfitting to old…
arXiv:2602.16837v1 Announce Type: new Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. Under causal masking at infinite depth, prior theoretical analyses of attention rollout predict an inevitable collapse of…
arXiv:2602.17577v1 Announce Type: cross Abstract: Omniprediction is a learning problem that requires suboptimality bounds for each of a family of losses $mathcal{L}$ against a family of comparator predictors $mathcal{C}$. We initiate the study of omniprediction in a multiclass setting, where…
arXiv:2602.16839v1 Announce Type: new Abstract: Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency: reinforcement learning (RL) training requires long rollouts for outcome-based rewards, where autoregressive decoding dominates time and memory usage. While sliding-window…
arXiv:2403.11332v3 Announce Type: replace Abstract: We address the challenge of inferring causal effects in social network data. This results in challenges due to interference — where a unit’s outcome is affected by neighbors’ treatments — and network-induced confounding factors. While…
arXiv:2602.16842v1 Announce Type: new Abstract: We study the offline data-driven newsvendor problem with censored demand data. In contrast to prior works where demand is fully observed, we consider the setting where demand is censored at the inventory level and only…
arXiv:2602.16849v1 Announce Type: new Abstract: We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task. Our work provides a full mechanistic interpretation of the learned model and a theoretical explanation of its…
