Search-based software engineering (SBSE), which integrates metaheuristic search techniques with software engineering, has been an active area of research for about 25 years. It has been applied to solve numerous problems across the entire software engineering lifecycle and has demonstrated its versatility in multiple domains. With recent advances in AI, particularly the emergence of foundation models (FMs) such as large language models (LLMs), the evolution of SBSE alongside these models remains undetermined. In this window of opportunity, we present a research roadmap that articulates the current landscape of SBSE in relation to FMs, identifies open challenges, and outlines potential research directions to advance SBSE through its integration and interplay with FMs. Specifically, we analyze five core aspects: leveraging FMs for SBSE design, applying FMs to complement SBSE in SE problems, employing SBSE to address FM challenges, adapting SBSE practices for FMs tailored to SE activities, and exploring the synergistic potential between SBSE and FMs. Furthermore, we present a forward-thinking perspective that envisions the future of SBSE in the era of FMs, highlighting promising research opportunities to address challenges in emerging domains.

Large language models (LLMs) have been used in many zero-shot learning problems, with their strong generalization ability. Recently, adopting LLMs in text-attributed graphs (TAGs) has drawn increasing attention. However, the adoption of LLMs faces two major challenges: limited information on graph structure and unreliable responses. LLMs struggle with text attributes isolated from the graph topology. Worse still, they yield unreliable predictions due to both information insufficiency and the inherent weakness of LLMs (e.g., hallucination). Towards this end, this paper proposes a novel method named Dynamic Text Bundling Supervision (DENSE) that queries LLMs with bundles of texts to obtain bundle-level labels and uses these labels to supervise graph neural networks. Specifically, we sample a set of bundles, each containing a set of nodes with corresponding texts of close proximity. We then query LLMs with the bundled texts to obtain the label of each bundle. Subsequently, the bundle labels are used to supervise the optimization of graph neural networks, and the bundles are further refined to exclude noisy items. To justify our design, we also provide theoretical analysis of the proposed method. Extensive experiments across ten datasets validate the effectiveness of the proposed method.

When you purchase through links in our articles, we may earn a small commission. You can insert yourself into AI videos with OpenAI's new Sora 2 model There's also a new Sora app that's made for creating, remixing, and sharing AI-generated videos. OpenAI is now launching Sora 2, according to a recent announcement post . Sora 2 is the next generation of the company's AI video and audio generator, promising more realistic, physically accurate, and controllable results. Unlike previous models, which often "cheated" with physics, Sora 2 can generate more believable simulations.

OpenAI's new short-form video app, Sora, seems to have all the ingredients of a viral hit. Just hours after the app's launch on Tuesday, memes created using its AI video-generation technology were already spreading to other social networks--including, for example, a video of OpenAI CEO Sam Altman rapping from the inside of a toilet bowl. Sora's launch--complete with a TikTok style "for you" page--was something of an about-face for Altman, who had previously described social media feeds as "an example of misaligned AI," whose algorithms "are incredible at getting you to keep scrolling." Altman was quick to distance OpenAI from suggestions that it had caved to the temptation to create what he called an AI-powered "slop feed." He wrote: "The team has put great care and thought into trying to figure out how to make a delightful product that doesn't fall into that trap, and has come up with a number of promising ideas."

We thank the reviewers for their helpful comments. Reviewers noted that Grover generates "extremely credible" articles (R2) and that due We appreciate this point and will revisit the word choice. We haven't seen the model We believe that our "novel way to guide generation" makes Grover novel, not just an Indeed, GPT(2), BERT, XLnet, and Grover share the same backbone but learn from different objectives. What is given to the turkers? For overall trustworthiness for instance, we asked "Does the article read like it comes "It takes a thief to catch a thief"?

The following lemma justifies item 3 in Assumption 1. Consider the following two cases: 1. Density function of the policy is smooth, i.e. We then show how Theorem 4 implies Theorem 1. Assumption 3. F or all x X, there exist constants such that the following hold 1. F or all x, we have null A Now we proceed to prove the main theorem. Then, given the above convergence result on the gradient norm, we proceed to prove the convergence of NAC in terms of the function value.

EV tax credits are dead in the US. Federal EV tax credits in the US officially came to an end yesterday. Those credits, expanded and extended in the 2022 Inflation Reduction Act, gave drivers up to $7,500 toward the purchase of a new electric vehicle. They've been a major force in cutting the up-front costs of EVs, pushing more people toward purchasing them and giving automakers confidence that demand would be strong. The tax credits' demise comes at a time when battery-electric vehicles still make up a small percentage of new vehicle sales in the country. This article is from The Spark, MIT Technology Review's weekly climate newsletter.

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Japan's Digital Agency to cooperate with OpenAI on administrative tools The Digital Agency will enable its employees to use OpenAI's cutting-edge large language model-based AI tools for their work. The Digital Agency said Thursday that it will cooperate with OpenAI to fully use artificial intelligence technology in administrative work and service. As part of the initiative, the agency will enable its employees to use OpenAI's cutting-edge large language model-based AI tools for their work. It is also considering joint development with the U.S. company of a generative AI app for administrative use. The agency plans to provide its employees with access to generative AI tools and encourage other government agencies to adopt these services starting as early as fiscal year 2026.

Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input.