Can language models (LMs) self-refine their own responses? This question is increasingly relevant as a wide range of real-world user interactions involve refinement requests. However, prior studies have largely tested LMs' refinement abilities on verifiable tasks such as competition math or symbolic reasoning with simplified scaffolds, whereas users often pose open-ended queries and provide varying degrees of feedback on what they desire. The recent advent of reasoning models that exhibit self-reflection patterns in their chains-of-thought further motivates this question. To analyze this, we introduce RefineBench, a benchmark of 1,000 challenging problems across 11 domains paired with a checklist-based evaluation framework. We evaluate two refinement modes: (1) guided refinement, where an LM is provided natural language feedback, and (2) self-refinement, where LMs attempt to improve without guidance. In the self-refinement setting, even frontier LMs such as Gemini 2.5 Pro and GPT-5 achieve modest baseline scores of 31.3% and 29.1%, respectively, and most models fail to consistently improve across iterations (e.g., Gemini-2.5-Pro gains only +1.8%, while DeepSeek-R1 declines by -0.1%). By contrast, in guided refinement, both proprietary LMs and large open-weight LMs (>70B) can leverage targeted feedback to refine responses to near-perfect levels within five turns. These findings suggest that frontier LMs require breakthroughs to self-refine their incorrect responses, and that RefineBench provides a valuable testbed for tracking progress.

Discontinuities can be fairly arbitrary but also cause a significant impact on outcomes in larger systems. Indeed, their arbitrariness is why they have been used to infer causal relationships among variables in numerous settings. Regression discontinuity from econometrics assumes the existence of a discontinuous variable that splits the population into distinct partitions to estimate the causal effects of a given phenomenon. Here we consider the design of partitions for a given discontinuous variable to optimize a certain effect previously studied using regression discontinuity. To do so, we propose a quantization-theoretic approach to optimize the effect of interest, first learning the causal effect size of a given discontinuous variable and then applying dynamic programming for optimal quantization design of discontinuities to balance the gain and loss in that effect size. We also develop a computationally-efficient reinforcement learning algorithm for the dynamic programming formulation of optimal quantization. We demonstrate our approach by designing optimal time zone borders for counterfactuals of social capital, social mobility, and health. This is based on regression discontinuity analyses we perform on novel data, which may be of independent empirical interest.

The purpose of higher education is to contribute to the advancement of society by graduating students of all backgrounds and providing them the skills and knowledge to be successful in life. One way colleges and universities can contribute to this purpose is to promote the goal of social mobility. The data shows that elite schools are enrolling mostly students from the highest income families. The current college ranking systems are highly weighted towards wealth, rather than social mobility and the advancement of all students. Therefore, there is a need to redirect the focus of college rankings to social mobility, not for the few but for all, especially those who have been traditionally excluded.

Education was traditionally seen as an enabler of social mobility. In other words, if you were from a low-income family, you could improve your financial and social standing by getting an education. And for a while, it worked, but inequality is on the rise again. These days, college and university degrees are, at least in developed countries, a dime a dozen and you need a postgraduate qualification to get an entry-level position. The advantage of education to boost social mobility is more noticeable in developing countries where the demand for highly educated individuals outstrips the supply.

Robots may take four million British jobs in the private sector within the next decade, some business leaders believe. Those surveyed for by YouGov for the Royal Society of Arts said 15 per cent of all jobs were under threat. The most vulnerable fields are finance and accounting, transportation and distribution, manufacturing and marketing and public relations, the survey found. But the research was not all doom and gloom, noting that technological advance creates new jobs, partly because increased productivity reduces prices freeing up consumers to spend money elsewhere in the economy. The RSA added that AI and robotics will mostly automate individual tasks rather than replace whole jobs.

"Traditionally, jobs like these have been a vehicle for social mobility," Sutton Trust research manager Carl Cullinane tells The Verge. These include so-called "soft skills" like confidence, motivation, communication, and resilience. The Sutton Trust report also says that there is some reason to be optimistic about the coming wave of automation, particularly if governments can encourage people to train for STEM professions (those involving science, technology, engineering, and mathematics). "From a social mobility perspective there are two important things about the STEM sector," says Cullinane of the UK job market.

Most economists agree that advances in robotics and AI over the next few decades are likely to lead to significant job losses. But what's less often considered is how these changes could also impact social mobility. A new report from UK charity Sutton Trust explains the danger, noting that unless governments take action, the next wave of automation will dramatically increase inequality within societies, further entrenching the divide between rich and poor. The are a number of reasons for this, say the report's authors, including the ability of richer individuals to re-train for new jobs; the rising importance of "soft skills" like communication and confidence; and the reduction in the number of jobs used as "stepping stones" into professional industries. For example, the demand for paralegals and similar professions is likely to be reduced over the coming years as artificial intelligence is trained to handle more administrative tasks.

Robots and artificial intelligence will'break the social ladder' and could cost up to 15 million jobs, new research has found. A study found that the increasing automation of jobs will hit poorer workers hardest and will further set back social mobility unless urgent action is taken. The research warns that automation could create a society in which an elite, high-skilled group dominates the higher echelon of society while a lower-skilled, low-income group is left with little opportunity to climb the social ladder. Robots and artificial intelligence will'break the social ladder' and could cost up to 15 million jobs, new research has found (stock image) Robots could take jobs from human workers and potentially spark an employment crisis, the boss of John Lewis warned last month. Sir Charlie Mayfield, 50, said new technologies, such as robots, would create massive changes across industry, with British businesses potentially among the hardest hit.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. ADDO AI recently launched a pilot microinsurance program near the city, using artificial intelligence to help farmers improve their yields. Microfinance has been widely recognized as an important strategy for lifting people out of poverty.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. ADDO AI recently launched a pilot microinsurance program near the city, using artificial intelligence to help farmers improve their yields. Microfinance is widely recognized as an important strategy for lifting people out of poverty.