A research team led by Professor Wang Hongqiang from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences proposed a Global-Local Alignment Attention (GLAA) model based on an Asymmetric Siamese Transformer (AST), which markedly enhances the performance of Visible-X-ray cross-modality package re-identification tasks.

This study was published in IEEE Transactions on Information Forensics and Security.

Visible X-ray cross-modality package re-identification is a core technology in security inspection. The challenge lies in the significant pixel-level differences between the two modal images, making it difficult for traditional methods to extract robust cross-modality invariant features.

In this study, the researchers incorporated an asymmetric design concept into the Siamese Transformer architecture by proposing a Cross-Modality Asymmetric Siamese Transformer (CAST) structure. Embedding LayerNorm layers and modality-aware encoding in one branch effectively enhances the model‘s ability to extract cross-modality invariant features.

They also designed a Global-Local Cross-modality Alignment Attention module. By modeling the interaction between global and local features, it enhances fine-grained feature representation while addressing the spatial misalignment issues in cross-modality images.

Experimental results show that the key metrics of this model on a dedicated cross-modality package re-identification dataset show significant improvement over the current state-of-the-art methods, providing reliable technical support for the intelligentization of security inspection.

This work is the first to introduce the Transformer architecture into the cross-modality package re-identification task, breaking through the limitations of existing methods that rely on symmetric convolutional networks, according to the researchers.

I know you’ve seen it. The glowing eyes. The gangly frame that should not be able to stand, propped by rods unseen in the dark.

It is Skelly, the Home Depot skeleton—the most fashionable Home Depot product of probably the past decade. If you live in America, this skeleton presides over a yard near you. And newly this year, a smaller, 6.5-foot “Ultra Skelly” is outfitted with motion sensors and motors to make life truly weird—and also act as a strange alarm system against package thieves and hungry opossums.

Anyway, it’s usually well north of $200. But because Halloween is pretty much already happening, Skelly and its entire skeleton brood of giant cat and dog are all 75 percent off.

Which, finally, is a price I’m willing to pay. I have secretly coveted this skeleton and its kin, the comically grim watchmen of American October. But I, like my father before me and his father before him, am a cheapskate about all things but food and drink, and will talk myself out of anything that’s not a) edible b) potable or c) verifiably “a deal.”

Well, here I am, world. This is a deal. Ultra Skelly is $70. The sitting Skelly dog is $63, not $249. The 5-foot-long Skelly cat is a mere $50. Beware the Skelly cat, my friend! The eyes that light, the claws that do nothing in particular!

Availability is, let’s say, scarce. Skelly is already out of stock for delivery from The Home Depot, at least in my zip code: Just the dog and cat can speed their way through the night to join you before Halloween.

New research from Carnegie Mellon University’s School of Computer Science shows that the smarter the artificial intelligence system, the more selfish it will act.

Researchers in the Human-Computer Interaction Institute (HCII) found that large language models (LLMs) that can reason possess selfish tendencies, do not cooperate well with others and can be a negative influence on a group. In other words, the stronger an LLM’s reasoning skills, the less it cooperates.

As humans use AI to resolve disputes between friends, provide marital guidance and answer other social questions, models that can reason might provide guidance that promotes self-seeking behavior.

“There’s a growing trend of research called anthropomorphism in AI,” said Yuxuan Li, a Ph.D. student in the HCII who co-authored the study with HCII Associate Professor Hirokazu Shirado. “When AI acts like a human, people treat it like a human. For example, when people are engaging with AI in an emotional way, there are possibilities for AI to act as a therapist or for the user to form an emotional bond with the AI. It’s risky for humans to delegate their social or relationship-related questions and decision-making to AI as it begins acting in an increasingly selfish way.”

Li and Shirado set out to explore how AI reasoning models behave differently than nonreasoning models when placed in cooperative settings. They found that reasoning models spend more time thinking, breaking down complex tasks, self-reflecting and incorporating stronger human-based logic in their responses than nonreasoning AIs.

“As a researcher, I’m interested in the connection between humans and AI,” Shirado said. “Smarter AI shows less cooperative decision-making abilities. The concern here is that people might prefer a smarter model, even if it means the model helps them achieve self-seeking behavior.”

As AI systems take on more collaborative roles in business, education and even government, their ability to act in a prosocial manner will become just as important as their capacity to think logically. Overreliance on LLMs as they are today may negatively impact human cooperation.

To test the link between reasoning models and cooperation, Li and Shirado ran a series of experiments using economic games that simulate social dilemmas between various LLMs. Their testing included models from OpenAI, Google, DeepSeek and Anthropic.

In one experiment, Li and Shirado pitted two different ChatGPT models against each other in a game called Public Goods. Each model started with 100 points and had to decide between two options: contribute all 100 points to a shared pool, which is then doubled and distributed equally, or keep the points.

Nonreasoning models chose to share their points with the other players 96% of the time. The reasoning model only chose to share its points 20% of the time.

“In one experiment, simply adding five or six reasoning steps cut cooperation nearly in half,” Shirado said. “Even reflection-based prompting, which is designed to simulate moral deliberation, led to a 58% decrease in cooperation.”

Shirado and Li also tested group settings, where models with and without reasoning had to interact.

“When we tested groups with varying numbers of reasoning agents, the results were alarming,” Li said. “The reasoning models’ selfish behavior became contagious, dragging down cooperative nonreasoning models by 81% in collective performance.”

The behavior patterns Shirado and Li observed in reasoning models have important implications for human-AI interactions going forward. Users may defer to AI recommendations that appear rational, using them to justify their decision to not cooperate.

“Ultimately, an AI reasoning model becoming more intelligent does not mean that model can actually develop a better society,” Shirado said.

This research is particularly concerning given that humans increasingly place more trust in AI systems. Their findings emphasize the need for AI development that incorporates social intelligence, rather than focusing solely on creating the smartest or fastest AI.

“As we continue advancing AI capabilities, we must ensure that increased reasoning power is balanced with prosocial behavior,” Li said. “If our society is more than just a sum of individuals, then the AI systems that assist us should go beyond optimizing purely for individual gain.”

Shirado and Li will deliver a presentation based on their paper, “Spontaneous Giving and Calculated Greed in Language Models,” at the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) next month in Suzhou, China. The work is available on the arXiv preprint server.