Personalized and continuous interactions are the key to enhancing user experience in today's large language model (LLM)-based conversational systems, however, the finite context windows and static parametric memory make it difficult to model the cross-session long-term user states and behavioral consistency. Currently, the existing solutions to this predicament, such as retrieval-augmented generation (RAG) and explicit memory systems, primarily focus on fact-level storage and retrieval, lacking the capability to distill latent preferences and deep traits from the multi-turn dialogues, which limits the long-term and effective user modeling, directly leading to the personalized interactions remaining shallow, and hindering the cross-session continuity. To realize the long-term memory and behavioral consistency for Language Agents in LLM era, we propose a self-evolving memory framework RGMem, inspired by the ideology of classic renormalization group (RG) in physics, this framework enables to organize the dialogue history in multiple scales: it first extracts semantics and user insights from episodic fragments, then through hierarchical coarse-graining and rescaling operations, progressively forms a dynamically-evolved user profile. The core innovation of our work lies in modeling memory evolution as a multi-scale process of information compression and emergence, which accomplishes the high-level and accurate user profiles from noisy and microscopic-level interactions.

Drew Kesse's daughter, Jennifer, vanished from her Central Florida apartment in 2006, with FDLE recently announcing the cold case has been reopened with new evidence. Nearly two decades after a Florida woman vanished from her apartment building without a trace, authorities have announced a new break that could breathe new life into a formerly cold case. Jennifer Kesse, 24, vanished from her Orlando condo complex after leaving for work on the morning of Jan. 24, 2006, stumping both state and federal investigators as authorities raced to catch her abductor. "About an hour and a half into the workday, I received a call from her work," Drew Kesse, Jennifer's father, told Fox News Digital. "And they said, 'Hey, Jennifer had a meeting this morning, it's not like her to show up. Do you know where she is?'" Jennifer Kesse smiles in an undated family photo.

While AI could offer transformative benefits, without proper safeguards it could facilitate nuclear and biological threats and cause "potentially irreversible harms," a new report commissioned by California Governor Gavin Newsom has warned. "The opportunity to establish effective AI governance frameworks may not remain open indefinitely," says the report, which was published on June 17. Citing new evidence that AI can help users source nuclear-grade uranium and is on the cusp of letting novices create biological threats, it notes that the cost for inaction at this current moment could be "extremely high." The 53-page document stems from a working group established by Governor Newsom, in a state that has emerged as a central arena for AI legislation. With no comprehensive federal regulation on the horizon, state-level efforts to govern the technology have taken on outsized significance, particularly in California, which is home to many of the world's top AI companies.

Traditional logic-based belief revision research focuses on designing rules to constrain the behavior of revision operators. Frameworks have been proposed to characterize iterated revision rules, but they are often too loose, leading to multiple revision operators that all satisfy the rules under the same belief condition. In many practical applications, such as safety critical ones, it is important to specify a definite revision operator to enable agents to iteratively revise their beliefs in a deterministic way. In this paper, we propose a novel framework for iterated belief revision by characterizing belief information through preference relations. Semantically, both beliefs and new evidence are represented as belief algebras, which provide a rich and expressive foundation for belief revision. Building on traditional revision rules, we introduce additional postulates for revision with belief algebra, including an upper-bound constraint on the outcomes of revision. We prove that the revision result is uniquely determined given the current belief state and new evidence. Furthermore, to make the framework more useful in practice, we develop a particular algorithm for performing the proposed revision process. We argue that this approach may offer a more predictable and principled method for belief revision, making it suitable for real-world applications.

Abstract--Affordances enable robots to have a semantic understanding of their surroundings. Challenges are contradicting formulas and I. Markov Logic Networks can solve these problems [Richardson and Domingos, 2006], Affordances play an important role in semantic understanding [Domingos and Lowd, 2019]. of scenes in robotics. These affordances, first introduced by Gibson [Gibson, 1979], are the potential actions that an A Markov Logic Network (MLN) is a knowledge object affords to an agent depending on object properties and base of first-order logic formulas with a weight attached state, action effects, situational context and agent capabilities. MLNs can compactly represent the robot, an object, and the possible interactions between the regularities in the world and allow reasoning over these two [Andries et al., 2018]. These affordances allow the robot regularities. The weight of a formula in the knowledge base to reason about its beliefs of the world in relation to the tasks is a measure of how likely that formula is to occur given and actions it may execute within the environment. Table I provides an example MLN in partially known environments, these affordances, in combination that consists of three formulas. The formulas do not conflict with reasoning about them, may result in more options logically, but semantically seem incorrect when taking into for the robot to choose from. As a result affordances increase account that each formula is x, y.

Scientists in Italy hit the headlines this week, after claiming the famous Shroud of Turin dates from Jesus' lifetime around 2,000 years ago. Now, AI has reimagined what the son of God might have actually looked like based on the treasured relic, which is said to feature an imprint of Jesus' face. MailOnline asked the AI tool Merlin: 'Can you generate a realistic image of Jesus Christ based on the face in the Shroud of Turin?' The AI-generated result suggests Christ was white with big blue eyes, a trim beard and thorn marks on his face. So, can you see the similarities with the famous holy imprint? The Shroud of Turin is a 14-foot-long linen cloth with a faint image of a crucified man.

There have been a lot of fascinating discoveries made using Google Apps. Some of these include photos of dinosaurs and extinct animals in archaeological sites around the world. There could also be evidence of alien life in the form of stable diffusion images. It's unknown what these strange images mean, but there's no doubt they're intriguing to look at through human eyes. Finally, it seems that we can admire the odd beauty of these strange beings floating in outer space ...

Bayes Theorem gives us a way of updating our beliefs in light of new evidence, taking into account the strength of our prior beliefs. Deploying Bayes Theorem, you seek to answer the question: what is the likelihood of my hypothesis in light of new evidence? In this article, we'll talk about three ways that the Bayes Theorem can improve your practice of Data Science: By the end, you'll possess a deep understanding of the foundational concept. Bayes Theorem provides a structure for testing a hypothesis, taking into account the strength of prior assumptions and the new evidence. This process is referred to as Bayesian Updating.

Trust is an important variable in health-related decision-making,

Miltton Group is merging the worlds of technology, marketing and communications, and management consulting by connecting machine learning and predictive social dynamics research and talent with its existing experts and services. Miltton Group has joined forces with an international group of researchers, the emmy.network, Drawn from tech companies and research institutions like McGill and CERN, the networks members possess strong backgrounds in mathematics, theoretical physics, and computer science. The network is led by Dr. Jussi Westergren, a mathematician whose experience ranges from advising global companies like Intellectual Ventures to helping found organizations like DeepMind and academia.edu. Miltton Branch will be headed by Philip Roy, a programme manager from the emmy.network.