arXiv:2608. 04232v1 Announce Type: new Abstract: Biological systems must regulate competing needs under limited perceptual bandwidth, where sharpening one estimate costs the capacity to sharpen the others.
By St John Grimbly, Nicolas Kuske, Evert A. Boonstra, Bruce A. Bassett, Charel van Hoof, Rowan Hodson, Benjamin Rosman, Ryan Smith, Mark Solms, Jonathan P. Shock
The paper presents a framework that uses saliency maps to create hierarchical attention profiles, tracking how deep reinforcement learning agents allocate attention over time. By comparing these attention trajectories across different conditions and linking them to behavioral metrics, the study reveals algorithm‑specific biases, unintended reward‑driven strategies, and overfitting to redundant sensory inputs. Experiments on Atari 2600 games, custom Pong environments, and biomechanical visuomotor simulations demonstrate that these attention patterns correspond to measurable behavioral differences, establishing attention trajectories as a diagnostic tool beyond traditional performance metrics.
By Charlotte Beylier, Hannah Selder, Arthur Fleig, Simon M. Hofmann, Nico Scherf
The paper introduces Risk‑Sensitive Satisficing (RS), a bandit learning rule that evaluates actions based on their past frequency and their surplus or shortfall relative to an aspiration level. RS can be reformulated as Attention Competitive Bayes (ACB), a Bayesian‑like estimator where the aspiration level serves as a prior mean and the limited number of trials limits how strongly evidence can override this prior. ACB predicts that unchosen actions gradually revert toward the aspiration level (pseudo‑forgetting) and that when the aspiration is unattainable, exploration never fully settles, reducing regret in simulations.
By Moto Shinriki, Yu Kono, Tatsuji Takahashi
Temporal credit assignment is central to both biological and artificial intelligence, yet its interaction with non-linear function approximation is poorly understood. We identify a systematic failure mode in deep reinforcement learning (RL) termed Trace-Mediated Peak Bias (TMPB).
Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise from different causes. One possibility is that th...
arXiv:2510. 13554v2 Announce Type: replace-cross Abstract: The reasoning pattern of Large language models (LLMs) remains opaque, and reinforcement learning (RL) typically applies uniform credit across an entire generation, blurring the distinction between pivotal and routine steps.
By Yang Li, Zhichen Dong, Yuhan Sun, Weixun Wang, Shaopan Xiong, Yijia Luo, Jiashun Liu, Han Lu, Jiamang Wang, Wenbo Su, Bo Zheng, Junchi Yan