The Heidelberg Laureate Forum, organized annually by the Heidelberg Laureate Forum Foundation, has built its reputation on a simple but powerful format: rather than a conventional conference, it is designed as a networking event where hierarchy is deliberately softened. Young researchers do not merely listen to talks; they eat with laureates, question them in small sessions and present their own work to an audience that includes some of the most decorated minds in their fields. The scientific partners of the forum are the Heidelberg Institute for Theoretical Studies and Heidelberg University, and the event is strongly supported by the award-granting institutions themselves: the Association for Computing Machinery, the International Mathematical Union and the Norwegian Academy of Science and Letters. The foundation behind it, the Klaus Tschira Stiftung, was established to promote the natural sciences, mathematics and computer science in Germany.

The technical heart of this year’s program is a series of lectures spanning the full breadth of both disciplines. Among the most anticipated is a joint lecture by Charles H. Bennett and Gilles Brassard, who together received the 2026 Turing Award for what the award citation describes as their essential role in igniting and shaping the quantum revolution in computer science and in information and communications technology. Bennett and Brassard are foundational figures in quantum cryptography and quantum information theory, fields that have moved from speculative physics to working prototypes of quantum communication networks. Their joint appearance offers attendees a rare chance to hear a first-hand account of how an entire research area was created, and how ideas about the limits of information in a quantum world became an engineering discipline.

Two of this year’s Fields Medal recipients will also take the lectern. Yu Deng will speak on critical problems in statistical physics and partial differential equations, an area where rigorous mathematical analysis meets models of collective behavior in physical systems. Jacob Tsimerman, meanwhile, will address AI safety and mathematics, a topic that sits at the intersection of number-theoretic depth and one of the most urgent practical questions of the decade. Their lectures illustrate how the Fields Medal, traditionally associated with pure mathematics, now connects directly to questions about statistical mechanics, dynamical systems and the trustworthiness of machine learning systems. Further lectures will be given by cryptographer Yael Tauman Kalai, recipient of the 2022 ACM Prize in Computing, by Shayan Oveis Gharan, the 2026 IMU Abacus Medal recipient, and by the noted mathematician Efim Zelmanov, who received the Fields Medal in 1994.

The forum’s connection to the wider world of science prizes is underscored by the annual Lindau Lecture, which signifies the close bond between the Heidelberg Laureate Forum and the Lindau Nobel Laureate Meetings. This year’s lecture will be held by Brian P. Schmidt, who received the Nobel Prize in Physics for the discovery of the accelerating expansion of the Universe through observations of distant supernovae. Schmidt will discuss the state of the Universe, examining what humanity currently understands about the cosmos and what may be learned over the coming decades. For the young mathematicians and computer scientists in the audience, the lecture is a reminder that their disciplines supply the statistical machinery, the algorithms and the computational infrastructure on which modern observational cosmology depends.

One of the most consequential sessions of the week will be an in-depth panel discussion on AI in mathematical research. The panel will be led by Tony Feng of UC Berkeley and Google DeepMind and will include Michael Harris of Columbia University, Ursula Martin, emeritus professor at the University of Oxford, Geordie Williamson of the University of Sydney, and Jacob Tsimerman, the 2026 Fields Medalist from the University of Toronto. The panelists will reflect on the consequences of rapidly evolving AI models with increased reasoning capabilities, the possibilities these systems may unlock for mathematical discovery, and the concerns they raise for the practice and culture of the discipline. The composition of the panel is itself notable: it pairs working mathematicians with a researcher embedded in an industrial AI lab, acknowledging that the frontier of machine-assisted proof and conjecture generation now runs through both universities and technology companies.

The 13th forum will also debut a first in the event’s history: an Un-Conference, a participant-driven format in which discussions focus on relevant, emerging topics shaped by the interests and expertise of the attendees themselves. Organizers describe it as a programmatic bottom-up approach that allows for dynamic and free-flowing work groups, in contrast to the fixed schedules of traditional meetings. For young researchers, this format lowers the barrier to testing half-formed ideas in front of peers and laureates alike, and it reflects a broader trend in scientific meetings toward giving early-career participants genuine agenda-setting power rather than reserving them a passive audience role.

The 200 young researchers selected for this year’s forum were chosen from a global applicant pool and represent what organizers call some of the brightest young minds in mathematics and computer science. Their own science will be on display throughout the week: 20 participants will present their research in the Next Gen Session, and a further 30 will showcase cutting-edge projects as posters. These sessions are not peripheral additions to the program; they are where the laureates in attendance can scout the directions in which the fields are moving, from theoretical computer science and cryptography to applied machine learning and pure mathematics. The forum’s design treats the exchange as bidirectional, with established prizewinners and emerging researchers each gaining something from the contact.

Access to the forum extends far beyond the lecture halls in Heidelberg. Lectures and panel discussions will be livestreamed on the HLF website and made available afterwards on the forum’s YouTube channel. A team of bloggers will report on various program points on the HLFF Blog, and attendees’ experiences can be followed across social media platforms under the hashtag #HLF26. The event will also be accompanied by episodes of the HLFF Vlog, published on the HLF website and YouTube channel, offering behind-the-scenes glimpses of the program, speakers and attendees. Journalists covering the event will have access to a repository of official photographs via the forum’s Flickr page. Journalists wishing to attend in person can apply for accreditation until September 9 through the HLF’s online registration tool, registering as a non-travel grant journalist and submitting the accreditation application.

As the 13th edition opens, the Heidelberg Laureate Forum once more demonstrates why it has become a fixture of the scientific calendar. In a single week, the quantum information pioneers who redefined what computation means, Fields Medalists probing statistical physics and the mathematics of AI safety, a Nobel laureate surveying the fate of the cosmos, and a panel weighing what reasoning machines will do to mathematical research will all share one stage with 200 early-career scientists. The forum’s blend of formal lectures, panel debates, poster sessions and participant-driven formats is built to accelerate exactly the kind of cross-generational, cross-disciplinary exchange that neither journals nor virtual seminars replicate well. For the young researchers attending, the week in Heidelberg may shape collaborations and careers for decades; for the wider scientific community, the livestreamed discussions offer a public window onto where mathematics and computer science are heading next.

Subject of Research: The 13th Heidelberg Laureate Forum, a networking meeting between mathematics and computer science laureates and young researchers

Article Title: The 13th Heidelberg Laureate Forum begins September 13

Article References: The 13th Heidelberg Laureate Forum begins September 13. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Heidelberg Laureate Forum, Abel Prize, Turing Award, Fields Medal, IMU Abacus Medal, ACM Prize in Computing, mathematics, computer science, quantum computing, artificial intelligence, young researchers, Nevanlinna Prize

The focal point of the week was the September 15 panel discussion titled “AI in Mathematical Research,” moderated by Tony Feng of UC Berkeley and Google DeepMind. The panelists included Fields Medalists Peter Scholze and Jacob Tsimerman, along with Michael Harris of Columbia University and Geordie Williamson of the University of Sydney. Rather than presenting a united front, the panel surfaced sharply divergent positions on how the mathematical community should respond to the rapid encroachment of machine intelligence into a discipline that has, for centuries, prized human insight, proof and understanding above all else. The onstage discussion, organizers noted, became the starting point for countless conversations among participants throughout the remainder of the week.

Scholze delivered one of the most pointed assessments of the entire Forum. “AI wants to accelerate everything, and I don’t see any inherent value in acceleration at all,” he said. His argument cut against the prevailing narrative that faster discovery is automatically better discovery. Scholze contended that even in the absence of AI, mathematics is already advancing so quickly that genuinely understanding its progress has become a bottleneck in its own right. In a field where a single landmark proof, such as his own work on perfectoid spaces, can take the community years to absorb and verify, the prospect of machine-generated conjectures and proofs arriving at an even greater pace raises an uncomfortable possibility: a mathematics that grows faster than any human, or even the collective of humans, can comprehend it.

Jessica Fintzen and Albrecht Schmidt, Scientific Co-Chairs of the Heidelberg Laureate Forum Foundation, reflected on the range of views the panel exposed. According to the co-chairs, the discussion spanned everything from embracing the use of AI, to cautioning against supporting companies whose values might not be aligned with those of the mathematical community, to trying to pinpoint what mathematics should really be about. They emphasized that the onstage debate addressed many questions that the mathematical and broader community must face, and that it served as a catalyst for conversations among HLF participants throughout the week. That framing captured the spirit of the Forum itself: not a search for consensus, but a structured confrontation with a technological shift whose consequences no one in the room claimed to fully predict.

Those questions extended well beyond the single panel. In a Spark Talk on “AI Safety and Mathematics,” Tsimerman, who received this year’s Fields Medal, argued that societies manage real risks by looking at them clearly and steering away from bad outcomes, not by ignoring them. His position framed AI safety not as a reason for disengagement but as a discipline in its own right, one in which mathematicians have a distinctive role to play. Torsten Hoefler, recipient of the 2024 ACM Prize in Computing, posed the question “Can We Build an AI Scientist?” and offered a prediction with sweeping implications: “everything that is verifiable will be automated.” For a discipline built on verification, that forecast lands with particular force, suggesting that the parts of mathematical work reducible to formal checking may eventually belong entirely to machines, leaving humans the tasks of conjecture, taste and interpretation.

Fields Medalist Martin Hairer presented “First Proof,” an initiative designed to measure how useful AI genuinely is to mathematical research by giving it the kinds of problems that talented mathematicians would actually work on. The approach addresses a persistent weakness in public claims about AI capability, where benchmarks often fail to correspond to the difficulty and texture of real research mathematics. By testing systems against problems of genuine current interest, Hairer’s framework aims to replace anecdote and hype with calibrated evidence, offering the community a way to track whether machine assistance is becoming a practical tool for working mathematicians or remains confined to well-trodden territory.

While AI dominated the conversation, the Forum also confronted a second technological threat with a very different character. Charles H. Bennett and Gilles Brassard, recipients of the 2025 ACM A.M. Turing Award for their pioneering role in the quantum revolution in computer science, delivered a joint lecture titled “The Quantum Information Revolution from Wiesner to Turing.” Brassard issued a caution that the quantum threat to today’s public-key cryptography is widely misunderstood. The danger, he explained, does not begin when a large quantum computer finally appears. Because encrypted data can be harvested now and decrypted later, adversaries need only record today’s traffic and wait. As he put it, “if a quantum computer arrives tomorrow, then my data is gone today.” The warning underscores why the migration to post-quantum cryptographic standards is treated by security researchers as an urgent present-day task rather than a distant concern.

Questions of trust in computation also ran through the program. Yael Tauman Kalai, recipient of the 2022 ACM Prize in Computing, took up a problem that grows more pressing as computation is increasingly outsourced to untrusted servers and, increasingly, to AI systems: when a massive computation is performed remotely, how can anyone be sure the result is correct? Her lecture presented succinct cryptographic proofs, compact certificates that are quick to check even for computations that are prohibitively expensive to rerun, as one answer. The technique connects directly to the week’s AI debates, since verifiable computation offers a potential safeguard for a future in which machine-generated results must be trusted at scale.

The scientific program showcased the breadth of both disciplines. Shayan Oveis Gharan, recipient of the 2026 IMU Abacus Medal, lectured on the polynomial paradigm and its applications in algorithms. Yu Deng, a 2026 Fields Medalist, gave a Spark Talk addressing critical problems in statistical physics and partial differential equations. Efim Zelmanov, Fields Medalist in 1994, opened the final day with a sweeping look at 200 years of abstract algebra. Nobel laureate Brian P. Schmidt, whose Nobel Prize in Physics recognized the discovery of the universe’s accelerating expansion, delivered this year’s Lindau Lecture, “The State of the Universe,” tracing how scientists came to understand that the cosmos is expanding. Attendees also watched livestreams marking the 100th birthday of Jean-Pierre Serre, the first recipient of the Abel Prize, including a lecture by Pierre Deligne in Serre’s honor and a talk by Serre himself from the celebrations in Paris.

The next generation made its own mark on the week. Dorothee Bär, Germany’s Federal Minister of Research, Technology and Space, addressed the Opening Ceremony by video, praising the Forum as a place where knowledge that cannot be learned from a paper passes in person from one generation to the next, and closing with an appeal: “Mathematics has outlived every border ever drawn around it. Please keep it that way.” The young researchers carried that spirit into the Forum’s first-ever Un-Conference on the final day, a bottom-up format for which participants had pitched and voted on discussion topics throughout the week. The results confirmed that the laureates’ concerns were widely shared: many sessions dealt with AI, including its impact on academia, how to reshape education in the age of AI, and technofascism and Silicon Valley ideology, while others took up the role of mathematics and computer science for society and the value of pure mathematics. The entire scientific program is available on the HLF’s YouTube channel, with in-depth coverage on the HLFF Blog, and journalists can register for accreditation for the 14th Forum in 2027 from March onwards. What the week made plain is that the debate over AI in mathematics is no longer a speculative exercise; it is a live argument being conducted at the very top of the discipline, and the outcome will shape how human knowledge itself is made.

Subject of Research: Debates on artificial intelligence in mathematical research at the 13th Heidelberg Laureate Forum

Article Title: Leading mathematicians and computer scientists weigh AI's growing role in mathematics at the 13th Heidelberg Laureate Forum

Article References: Leading mathematicians and computer scientists weigh AI's growing role in mathematics at the 13th Heidelberg Laureate Forum. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Heidelberg Laureate Forum, artificial intelligence, mathematics, Fields Medal, Peter Scholze, quantum cryptography, post-quantum security, verifiable computation, AI safety, Turing Award, young researchers, Un-Conference

The research team, led by education researcher Susanne de Mooij, examined the development of numeracy skills among 7,885 primary school pupils during the period between 2014 and 2017, when adaptive learning systems were first being introduced in Dutch classrooms. The design was observational and comparative: classes that used an adaptive learning system for their mathematics instruction were matched against classes that taught numeracy through conventional means, without such software. The researchers then followed the mathematical growth of both groups across multiple school years, asking a deceptively simple question. Do children who practice arithmetic with software that continuously adjusts to their ability level actually progress faster than children who do not?

The technology at the heart of the study is conceptually elegant. An adaptive learning system automatically calibrates the difficulty of each exercise to the individual pupil working through it. When a child answers correctly and consistently, the system serves up more challenging problems, pushing the child forward along their own learning curve. When a child begins to struggle, the software responds by offering simpler tasks, allowing the pupil to consolidate foundational skills before moving on. In theory, this creates a continuously optimized learning trajectory for every child in a class, something that a single teacher managing thirty pupils simultaneously can only approximate with the greatest effort.

The results showed that, on average, pupils who used the adaptive system displayed more positive growth in their mathematics performance over the years of the study compared with pupils who learned without it. In cognitive terms, the feared scenario simply did not materialize. This matters because a persistent worry has shadowed digital learning tools since their arrival: that screen-based practice is somehow less effective than working with paper and pencil, and that children exposed to it learn less deeply. De Mooij and her colleagues found no support for that fear in the domain of arithmetic. If anything, the digital environment produced a small but consistent advantage in how quickly children’s numeracy skills developed.

It is precisely the longitudinal character of the study that gives this finding its weight. According to De Mooij, most previous research into adaptive learning systems has either measured only short-term effects or been conducted on much smaller samples, leaving educators and policymakers to make decisions about expensive technology on the basis of limited evidence. Adaptive learning platforms have now been in use for almost fifteen years, and today they operate in more than half of all Dutch primary schools. That widespread adoption, she notes, has run well ahead of the scientific evaluation of the tools, a situation this study begins to correct by tracking skill development over years rather than weeks.

The researchers are careful, however, to resist any temptation toward triumphalism. The effects they measured are small, and De Mooij stresses that the tool should be understood as producing a generally positive trend rather than a dramatic transformation. There is no miracle cure here, no software that will suddenly close achievement gaps on its own. What the data do reveal is a pattern of modest, reliable benefit, the kind of incremental improvement that, multiplied across thousands of classrooms and hundreds of hours of practice, can meaningfully shape how a generation of children acquires foundational mathematical skills.

Perhaps the most socially significant finding concerns where those benefits concentrate. The positive effect of the adaptive technology was particularly evident in large schools and in vulnerable schools serving many pupils from less advantaged socio-economic backgrounds. De Mooij offers a plausible mechanism: in such settings, there is often greater variation between pupils in their starting levels and learning paces. A conventional whole-class lesson must aim somewhere near the middle of that distribution, leaving the weakest and strongest learners underserved. Adaptive technology, by contrast, can tailor instruction to each individual child’s needs, and the wider the spread of abilities in a classroom, the more valuable that tailoring becomes. In this sense, the technology appears to function as an equity instrument, delivering its largest gains precisely where educational disadvantage is most concentrated.

This framing leads directly to the study’s central message about the role of the teacher. The researchers describe the adaptive learning tool as something that supports teachers rather than replacing them, and they are explicit about why. A single teacher cannot adapt lesson content to thirty pupils at the same time, but with a tool of this kind, pupils’ mathematics skills can be improved while the teacher’s workload is simultaneously reduced. The software absorbs the mechanical burden of individualizing practice, freeing the professional to do the things that only a professional can do. In classrooms under pressure, that dual benefit, better outcomes and less administrative strain, may prove to be the technology’s most persuasive selling point.

Crucially, the researchers insist that the teacher always remains in control of the learning process. It is the teacher who draws up the lesson plan, who decides what pupils learn, and who oversees everything that happens in the classroom. The adaptive system, for all its algorithmic sophistication, is fed solely by the data stored within it, whereas a teacher has access to vastly richer information: a child’s mood on a difficult morning, the social dynamics of the room, the moment when a frustrated pupil needs encouragement rather than another exercise. The algorithm sees answers; the teacher sees the child. That asymmetry, the researchers suggest, is not a temporary limitation of current technology but a structural feature of what teaching actually is.

The study arrives at a moment when schools around the world are being urged, sometimes aggressively, to adopt AI-driven educational products, often on the strength of marketing claims rather than longitudinal evidence. Against that backdrop, the Dutch findings offer a template for how the technology should be evaluated: over years, at scale, with comparison groups, and with honest acknowledgment that the measured effects are small. The picture that emerges is neither utopian nor dismissive. Adaptive learning tools, deployed under the guidance of skilled teachers, can help children learn arithmetic a little faster, and they can help most in the schools that need help the most. That is a genuinely useful result, and it comes with a built-in safeguard: the evidence itself reminds us that the decisive variable in the classroom is still the human being who plans the lessons, reads the room, and decides, every day, what learning should look like.

Subject of Research: Long-term effects of AI-supported adaptive learning systems on arithmetic skills in primary school pupils

Article Title: Learning maths goes (slightly) better with AI, but teacher plays a key role

Article References: Learning maths goes (slightly) better with AI, but teacher plays a key role. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: adaptive learning, artificial intelligence, primary education, mathematics, arithmetic, numeracy, educational technology, Radboud University, longitudinal study, teachers, Learning and Instruction, Netherlands

Beyond the four top-50 subjects, the ranking revealed considerable depth across the university’s portfolio. Seven subjects in total placed among the world’s top 100, with Dentistry and Oral Sciences falling in the 51–75 band and both Economics and Political Sciences landing in the 76–100 range. Taken together, the Hebrew University had 30 subjects included in the global ranking, and 13 of those placed among the world’s top 150. In addition to its seven top-100 subjects, the university ranked in the 101–150 range in Atmospheric Science, Biological Sciences, Human Biological Sciences, Sociology, Education, and Psychology. For a single institution of roughly comparable size to many mid-tier international research universities, that breadth of high placement is a notable statistical achievement.

The Global Ranking of Academic Subjects, often referred to as GRAS and colloquially associated with the broader family of Shanghai rankings, is published annually and assesses universities across 57 disciplines spanning the natural sciences, engineering, life sciences, medical sciences, and social sciences. The 2026 edition is one of the largest subject-level assessments in the world, incorporating approximately 20,000 academic subject units drawn from around 2,000 universities in roughly 100 countries and regions. Unlike overall institutional rankings, which compress an entire university’s performance into a single number, subject rankings allow analysts to see precisely where a university’s research strengths and weaknesses lie, discipline by discipline.

Methodologically, GRAS relies on internationally comparable indicators grouped into five categories: world-class faculty, world-class research output, high-quality research, research impact, and international collaboration. Within these categories, the ranking considers factors such as influential journal publications, citation impact, international academic awards, the presence of highly cited researchers, leadership roles in international academic organizations, and the extent of research collaboration across borders. This multi-indicator approach is designed to reduce the distorting effect of any single metric and to capture different dimensions of academic excellence, from the sheer volume of published work to the recognition a department’s scholars receive from the global scientific community.

For Mathematics, the Hebrew University’s 24th-place finish continues a long tradition. The discipline has historically been one of the university’s flagship fields, associated with pioneering work in logic, analysis, and theoretical computer science, and the 2026 result suggests that this legacy remains fully intact in an era of intensifying global competition. The 17th-place ranking in Communication is equally striking, reflecting a field that has grown rapidly in importance as researchers grapple with digital media, misinformation, and the transformation of public discourse. High placement in Communication signals strong citation performance and international visibility in a discipline where publication venues and scholarly networks are increasingly global.

The university’s showing in Law, at 36th, and Public Administration, at 44th, adds a professional-school dimension to its research profile. These fields are evaluated alongside the same bibliometric and reputational indicators as the natural sciences, which makes strong placements particularly meaningful, since legal scholarship and public administration research are often published in national or regional venues and in languages other than English. Achieving top-50 global status in both fields indicates that the Hebrew University’s legal and policy scholars are publishing in leading international journals, attracting citations from abroad, and participating in cross-border academic networks at rates comparable to the world’s most prominent institutions.

The presence of Dentistry and Oral Sciences in the 51–75 band, along with Economics and Political Sciences in the 76–100 range, further illustrates the breadth of the university’s research base. In the life sciences, placements in the 101–150 range for Biological Sciences and Human Biological Sciences reflect sustained output in fields ranging from molecular biology to neuroscience, while Atmospheric Science points to growing activity in climate and earth-systems research. Social science placements in Sociology, Education, and Psychology round out a profile in which nearly half of the university’s ranked subjects sit within the top 150 worldwide, a ratio that few institutions in the region can match.

University leadership framed the results as a validation of long-term investment in research quality. Prof. Tamir Sheafer, President of the Hebrew University of Jerusalem, said the results reflect both the exceptional quality and the remarkable breadth of research at the institution. Across mathematics, the life sciences, dentistry, law, communication, economics, and public policy, he noted, researchers are advancing knowledge that deepens understanding of the world and helps address its most pressing challenges. He described the international recognition as a testament to the creativity, rigor, and dedication of the academic community, and to the university’s continuing role at the forefront of research in Israel and worldwide.

The broader significance of the result extends beyond institutional pride. Subject-level rankings of this kind are closely watched by prospective graduate students, international research partners, funding agencies, and faculty recruits, all of whom use them as a proxy for the strength of specific departments. A concentration of top-50 placements in fields as varied as Mathematics, Communication, Law, and Public Administration can influence where talented early-career researchers choose to apply, where collaborative projects are initiated, and how international consortia are assembled. For Israeli academia as a whole, the result reinforces the country’s reputation for research excellence in a small national system that consistently punches above its weight in global metrics.

The 2026 edition of the ranking also illustrates how subject-level evaluation has matured into a sophisticated analytical tool. By combining publication counts in influential journals, normalized citation impact, international awards, highly cited researcher counts, organizational leadership, and collaboration measures, GRAS attempts to capture a multidimensional picture of departmental performance that a single composite score cannot provide. For the Hebrew University of Jerusalem, the 2026 results, with four subjects in the global top 50, seven in the top 100, and 13 in the top 150 out of 30 ranked subjects, provide a detailed, data-driven map of where the institution stands in the international research landscape, and a benchmark against which its future performance will be measured when the next edition of the ranking is published.

Subject of Research: Global university subject rankings evaluating research performance across academic disciplines

Article Title: Hebrew University leads Israel with four subjects among world’s top 50 in 2026 Shanghai ranking

Article References: Hebrew University leads Israel with four subjects among world’s top 50 in 2026 Shanghai ranking. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Hebrew University, Shanghai Ranking, Global Ranking of Academic Subjects, Mathematics, Communication, Law, Public Administration, Israel, university rankings, research impact, higher education, academic collaboration