The rapid development of artificial intelligence (AI) has unearthed the potential to assist humans in controlling advanced technologies. Shared autonomy (SA) facilitates control by combining inputs from a human pilot and an AI copilot. In prior SA studies, the copilot is constantly active in determining the action played at each time step. This limits human autonomy that may have deleterious effects on performance. In general, the amount of helpful copilot assistance varies greatly depending on the task dynamics.

Assistive agents should make humans' lives easier. Classically, such assistance is studied through the lens of inverse reinforcement learning, where an assistive agent (e.g., a chatbot, a robot) infers a human's intention and then selects actions to help the human reach that goal. This approach requires inferring intentions, which can be difficult in high-dimensional settings. We build upon prior work that studies assistance through the lens of empowerment: an assistive agent aims to maximize the influence of the human's actions such that they exert a greater control over the environmental outcomes and can solve tasks in fewer steps. We lift the major limitation of prior work in this area--scalability to high-dimensional settings--with contrastive successor representations. We formally prove that these representations estimate a similar notion of empowerment to that studied by prior work and provide a ready-made mechanism for optimizing it. Empirically, our proposed method outperforms prior methods on synthetic benchmarks, and scales to Overcooked, a cooperative game setting. Theoretically, our work connects ideas from information theory, neuroscience, and reinforcement learning, and charts a path for representations to play a critical role in solving assistive problems.

Neural Information Processing Systems http://nips.cc/

In this work, we introduce the Assisted Learning framework for organizations to assist each other in supervised learning tasks without revealing anyorganization'salgorithm,data,oreventask.

One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on inferring the human's goal, which is challenging when there are many potential goals or when the set of candidate goals is difficult to identify. We propose a new paradigm for assistance by instead increasing thehuman's ability tocontroltheir environment, and formalize this approach byaugmenting reinforcement learning withhuman empowerment.

Sudan's Rapid Support Forces (RSF) have launched a series of drone attacks targeting humanitarian aid convoys and fuel trucks across North Kordofan, killing at least one person and wounding several others, officials and medical organisations said. The North Kordofan state government condemned Friday's strikes on a convoy linked to the World Food Programme (WFP), urging the international community and United Nations bodies to impose sanctions on the RSF paramilitary group's leadership. The attacks occurred along the key road connecting the state capital, el-Obeid, with Kosti in neighbouring White Nile state. Fighting between the government-aligned Sudanese Armed Forces (SAF) and the RSF has intensified across the Kordofan region since October 2025 after el-Fasher fell to the RSF, where the group committed atrocities - a "crime scene" according to the UN. According to the UN Office for the Coordination of Humanitarian Affairs (OCHA), the first strike at dawn hit three trucks in Er-Rahad.