Erdős asked whether, for every fixed r ≥ 3, there is a constant cr > r−r such that, for every ε > 0, every sufficiently large r-uniform hypergraph on N vertices with at least (1+ε)(N/r)r edges contains a subgraph on m → ∞ vertices with at least crmr edges. We give counterexamples for every r ≥ 16. The base construction is a sequence of explicit 16-uniform hypergraphs Gn whose unnormalized Lagrangians satisfy 16−16(1 + 1/(4(14n)14)) ≤ λ(Gn) ≤ 16−16 + 1/(15! n). Blow-ups give the counterexample for r = 16, and adjoining common vertices to every edge lifts it to all larger uniformities. Thus Problem 1075, as a statement for all r ≥ 3, has a negative answer. The endpoint question remains open for 3 ≤ r ≤ 15, including the case 2/9 for 3-graphs. Concretely: for every γ > r−r there are ε > 0 and arbitrarily large r-uniform hypergraphs H with e(H) ≥ (1+ε)(v(H)/r)r and e(H[S]) < γ|S|r for every nonempty S ⊆ V(H) — a conclusion stronger than the formulation of the problem. Context. With the usual density normalization the threshold (N/r)r corresponds to αr = r!/rr, and Erdős proved that every number in [0, αr) is a jump for r-graphs; Problem 1075 is the endpoint question at αr. Frankl and Rödl disproved the earlier jumping constant conjecture; Frankl, Peng, Rödl and Talbot showed (5/2)r!/rr is a non-jump; Peng gave the lifting theorem to larger uniformities; Yan and Peng brought this to (54/25)r!/rr. Shaw recently proved that 2r!/rr is a non-jump for r ≥ 4 and that no smaller non-jump follows from his finite-pattern formulation of the Frankl–Rödl method; Liu and Mubayi then proved 4/9 is a non-jump for 3-graphs by a construction outside that framework. The construction here is likewise not an instance of Shaw's finite-pattern criterion, since its distinguished links follow a matching and a shifted matching around a cycle and the proof controls the full Lagrangian of a growing sequence, so Shaw's Theorem 4.3 does not apply. Status. Preprint, not yet refereed. As of 5 September 2026, Erdős Problem 1075 is listed as open on erdosproblems.com, with no proof claims submitted. Declaration of generative AI and AI-assisted technologies. GPT-6 Astra was used to generate the mathematical proofs and draft the manuscript. GPT-5.6 Sol and Claude Opus 5 were used only for editorial review of the exposition. GPT-6 Astra was run in a research environment containing earlier results produced by GPT-5.6 Sol and Claude Opus 5, but those earlier results did not contribute to the final mathematical arguments. The author reviewed the final manuscript and takes full responsibility for its content.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026