China figured out how to sell EVs. Now it has to bury their batteries. As early electric cars age out, hundreds of thousands of used batteries are flooding the market, fueling a gray recycling economy even as Beijing and big manufacturers scramble to build a more orderly system. In August 2025, Wang Lei decided it was finally time to say goodbye to his electric vehicle. Wang, who is 39, had bought the car in 2016, when EVs still felt experimental in Beijing. It was a compact Chinese brand.

This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells and supported by computer simulations. The model decomposes self-replication into core components, each of which is executed by a single machine constructed from a set of basic block types. Key functionalities such as sorting, copying, and building, are demonstrated. The model provides valuable insights into the constraints of self-replicating systems. The discussion also addresses the spatial and timing behavior of the system, as well as its efficiency and complexity. This work provides a foundational framework for future studies on self-replicating mechanisms and their information-processing applications.

Notably, both methods are based on correspondences between the embedding representations of tokens across the two models. It was hypothesized that a recycler trained to map embeddings from Ms to Mt can also be used to map prompts.

Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model behavior when concatenated to the input text. However, these learned prompts are tightly coupled to a given frozen model -- if the model is updated, corresponding new prompts need to be obtained. In this work, we propose and investigate several approaches to "Prompt Recycling'" where a prompt trained on a source model is transformed to work with the new target model. Our methods do not rely on supervised pairs of prompts, task-specific data, or training updates with the target model, which would be just as costly as re-tuning prompts with the target model from scratch. We show that recycling between models is possible (our best settings are able to successfully recycle $88.9\%$ of prompts, producing a prompt that out-performs baselines), but significant performance headroom remains, requiring improved recycling techniques.

When China restricted the importation of recyclable waste products in 2018, many western companies turned to robotic technologies to strengthen their processing capabilities. "The ban exposed how vulnerable the current infrastructure for recycling is," says Chris Wirth, vice-president of marketing and business development for AMP Robotics, a Denver-based industrial recycling artificial intelligence company. To recycle in a cost-effective, comprehensive and safe way, goods must be broken down into their constituent commodities to be sold on, in a process that has been likened to "unscrambling an egg". Roboticists think that computer vision, neural networks and modular robotics can enable a more intelligent, flexible approach to recycling. AI-enabled robotics can identify items based on visual cues such as logos, colour, shape and texture, sorting them and taking them apart.

Roughly 20 km away from Hong Kong's slick, densely packed urban center lies the New Territories -- a suburban mishmash of rugged hills and scruffy villages, soaring new housing developments and vacant lots. This is where over half of the territory's 7.2 million people live. It could also be the resting place for your old PC or printer. Up to 20% of all U.S. electronic waste may be ending up in Hong Kong. Not in some scrapyard in the developing world, picked over by haggard children and wheezing laborers, but in the backyard of one of the world's most sophisticated financial capitals.

Apple AAPL 0.41 % introduced a piece of technology recently that will likely never be used by any consumer. Instead, it kind of cleans up after them: a robot that breaks down iPhones for recycling. The arrival of Liam--a 29-armed robot capable of taking apart 1.2 million iPhones a year--speaks to a big challenge facing tech manufacturers today. Even as they strive to entice consumers to ditch their existing devices for the next new thing, companies must figure out what to do with the growing numbers of devices that are destined for the scrapheap as a result. "We think as much now about the recycling and end of life of products as the design of products itself," says Lisa Jackson, Apple's vice president of environment, policy and social initiatives.

Somewhere in a Cupertino warehouse, a giant labors with robotic precision, its 29 arms singularly focused on one thing: an iPhone. But instead of putting pieces together, this robot is pulling pieces apart. It disassembles iPhones at the rate of one handset every 11 seconds--less time than it takes you to fish your phone out of an overcrowded bag. Apple calls the machine Liam. The project was kept secret for three years, says Mashable deputy tech editor Samantha Murphy Kelly, who was allowed a sneak preview of Liam in action.