In their paper LLMasMMKG: LLM Assisted Synthetic Multi-Modal Knowledge Graph Creation For Smart City Cognitive Digital Twins, which was published in the AAAI Fall Symposium series, and introduced an approach that leverages large language models to automate the construction of synthetic multi-modal knowledge graphs specifically designed for a smart city cognitive digital twin. Here, Sukanya tells us more about cognitive digital twins, the framework they employed, and some key results. Could you start by introducing the idea of smart city cognitive digital twins and why this is an interesting area for study? Cities grow increasingly complex and interconnected, demanding sophisticated tools for management. A cognitive digital twin (CDT) serves as an AI-enabled virtual replica that models the dynamic interplay of physical and social systems, enabling simulations, predictions, and optimized operations.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Abdelrahman Sayed Sayed to chat about his work on formal verification applied to autonomous vehicles. Could you tell us a bit about where you're studying and the broad topic of your research? My PhD topic is formal verification of neural ODE (ordinary differential equations) for safety evaluation in autonomous vehicles. Could you say something about formal verification and why it's such an important topic?

Martian clay may have held water billions of years ago. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. NASA's Curiosity Mars rover took this selfie at a location nicknamed Mary Anning after a 19th century English paleontologist. This was the site of the chemical experiment uncovering diverse organic molecules on Mars, in the Glen Torridon region, which scientists believe was a site where ancient conditions would have been favorable to supporting life, if it ever was present. Breakthroughs, discoveries, and DIY tips sent six days a week.

Japanet is expanding its venture capital fund with Pegasus Tech Ventures, after early investments in firms like SpaceX, OpenAI, Anthropic and xAI showed strong growth. Japanese home shopping company Japanet is expanding its venture capital fund with San Jose-based Pegasus Tech Ventures, following the success of early bets in SpaceX, OpenAI, Anthropic and xAI. The Nagasaki-based retailer known for infomercials targeting seniors in aging Japan will allocate $200 million to the fund, up from an initial $50 million in 2021, following significant growth" in investments so far, the companies said in a statement. The fund, of which Pegasus is general partner, will focus on areas such as generative AI, robotics and space technology. Its Japan portfolio includes startup Aillis, which seeks to use artificial intelligence to analyze medical scans. Asian companies have struggled to win stakes in promising startups in Silicon Valley, hampered by a lack of personal connections and reputation for slow decision-making. Pegasus also manages startup investments on behalf of Toyota Motor-affiliate Aisin, Japanese chemical maker Denka, Taiwan's Asustek Computer and Acer and Indonesia's pharma company Kalbe Farma. Everybody wants a piece of the Silicon Valley AI action," Pegasus Chief Executive Officer Anis Uzzaman said on a video call.

Anthropic was founded in 2021 by several former employees of OpenAI. Amazon is investing an additional $5 billion in Anthropic, and may inject $20 billion more over time, a deal that deepens the companies' ties in an increasingly competitive artificial intelligence industry. Anthropic, which makes the Claude chatbot and coding tool, plans to spend more than $100 billion over the next 10 years on Amazon's cloud technologies and chips, the companies said in a statement on Monday. Amazon shares gained about 3% on the news in extended trading. Amazon was already one of Anthropic's biggest backers, with prior investments totaling $8 billion.

Conformal prediction provides finite-sample, distribution-free coverage under exchangeability, but standard constructions may lack robustness in the presence of outliers or heavy tails. We propose a robust conformal method based on a non-conformity score defined as the half-mass radius around a point, equivalently the distance to its $(\lfloor n/2\rfloor+1)$-nearest neighbour. We show that the resulting conformal regions are marginally valid for any sample size and converge in probability to a robust population central set defined through a distance-to-a-measure functional. Under mild regularity conditions, we establish exponential concentration and tail bounds that quantify the deviation between the empirical conformal region and its population counterpart. These results provide a probabilistic justification for using robust geometric scores in conformal prediction, even for heavy-tailed or multi-modal distributions.

The inverse Potts problem for estimating evolutionary single-site fields and pairwise couplings in homologous protein sequences from their single-site and pairwise amino acid frequencies observed in their multiple sequence alignment would be still one of useful methods in the studies of protein structure and evolution. Since the reproducibility of fields and couplings are the most important, the Boltzmann machine method is employed here, although it is computationally intensive. In order to reduce computational time required for the Boltzmann machine, parallel, persistent Markov chain Monte Carlo method is employed to estimate the single-site and pairwise marginal distributions in each learning step. Also, stochastic gradient descent methods are used to reduce computational time for each learning. Another problem is how to adjust the values of hyperparameters; there are two regularization parameters for evolutionary fields and couplings. The precision of contact residue pair prediction is often used to adjust the hyperparameters. However, it is not sensitive to these regularization parameters. Here, they are adjusted for the fields and couplings to satisfy a specific condition that is appropriate for protein conformations. This method has been applied to eight protein families.

Large language models (LLMs) using chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can mitigate this by withholding outputs unlikely to be correct. While most abstention methods decide to withhold outputs before or after generation, dynamic mid-generation abstention considers early termination of unpromising reasoning traces at each token position. Prior work has explored empirical variants of this idea, but principled guidance for the abstention rule remains lacking. We present a formal analysis of dynamic abstention for LLMs, modeling abstention as an explicit action within a regularized reinforcement learning framework. An abstention reward parameter controls the trade-off between compute and information. We show that abstaining when the value function falls below this reward strictly outperforms natural baselines under general conditions. We further derive a principled and efficient method to approximate the value function. Empirical results on mathematical reasoning and toxicity avoidance tasks support our theory and demonstrate improved selective accuracy over existing methods.

As search depth increases in autonomous reasoning and embodied planning, the candidate action space expands exponentially, heavily taxing computational budgets. While heuristic pruning is a common countermeasure, it operates without formal safety guarantees when surrogate models (like LLMs) exhibit systematic evaluation biases. This paper frames the node expansion process as a localized Best-Arm Identification (BAI) problem over dynamic frontiers, subject to a bounded systematic bias $L$. By inverting the Lambert W function, we establish an additive sample complexity of $\mathcal{O}((Δ-4L)^{-2})$, which indicates that safe node elimination is only feasible when the empirical reward gap exceeds $4L$. We complement this with an information-theoretic lower bound of $Ω((Δ-2L)^{-2})$ to confirm the structural limits of biased search. Subsequent evaluations on both synthetic trees and complex reasoning tasks demonstrate that adhering to this local safety boundary successfully preserves optimal trajectories while maximizing sample allocation efficiency.

Persistent homology (PH) encodes global information, such as cycles, and is thus increasingly integrated into graph neural networks (GNNs). PH methods in GNNs typically traverse an increasing sequence of subgraphs. In this work, we first expose limitations of this inclusion procedure. To remedy these shortcomings, we analyze contractions as a principled topological operation, in particular, for graph representation learning. We study the persistence of contraction sequences, which we call Contraction Homology (CH). We establish that forward PH and CH differ in expressivity. We then introduce Hourglass Persistence, a class of topological descriptors that interleave a sequence of inclusions and contractions to boost expressivity, learnability, and stability. We also study related families parametrized by two paradigms. We also discuss how our framework extends to simplicial and cellular networks. We further design efficient algorithms that are pluggable into end-to-end differentiable GNN pipelines, enabling consistent empirical improvements over many PH methods across standard real-world graph datasets. Code is available at \href{https://github.com/Aalto-QuML/Hourglass}{this https URL}.