Archive 10.06.2026

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Robots learn to anticipate chaos, but still fail to read a decidedly human signal

Cornell researchers are investigating the potential for using artificial intelligence to give robots social intelligence—the ability to read facial cues, anticipate the needs of those around them, and function within society. The new study tested the ability of vision language models (VLMs)—AI systems that can interpret and generate both visual information and language—to predict whether a tense scenario in a short video would end well or badly, such as a toddler carrying an overly full mug of coffee.

Autonomous drone can deliver life jackets to people that fall overboard

It's a race against the clock when someone falls overboard: People's chances of being found before they drown from exhaustion or freeze to death dwindle by the minute. Rescue efforts are often hampered by the time it takes a vessel at full throttle to halt so a rescue boat can be deployed and start searching for the person, who is by now far from the ship.

Robotic arm inspired by octopus uses tactile sensors in suction cups for autonomous underwater grasping

The oceans hide some of the most sophisticated solutions nature has ever developed and are an inexhaustible source of inspiration for the robotics of the future. The Bioinspired Soft Robotics research unit, coordinated by Barbara Mazzolai, associate director for robotics at the Istituto Italiano di Tecnologia (IIT—Italian Institute of Technology), has developed an octopus-inspired soft robotic arm that, thanks to the technology embedded in its artificial suction cups, is capable of sensing contact, estimating the intensity and direction of the applied force, and grasping objects autonomously, even in complex environments such as underwater settings.

Robotic arm inspired by octopus uses tactile sensors in suction cups for autonomous underwater grasping

The oceans hide some of the most sophisticated solutions nature has ever developed and are an inexhaustible source of inspiration for the robotics of the future. The Bioinspired Soft Robotics research unit, coordinated by Barbara Mazzolai, associate director for robotics at the Istituto Italiano di Tecnologia (IIT—Italian Institute of Technology), has developed an octopus-inspired soft robotic arm that, thanks to the technology embedded in its artificial suction cups, is capable of sensing contact, estimating the intensity and direction of the applied force, and grasping objects autonomously, even in complex environments such as underwater settings.

Congratulations to the #AAMAS2026 best paper award winners

The AAMAS 2026 best paper awards were presented at the 25th International Conference on Autonomous Agents and Multiagent Systems, which took place from 25-29 May 2025 in Paphos, Cyprus. The winners and nominees in the three categories (best paper, best student paper, best blue sky paper) are as follows:


Best Paper Award

Winner

  • Developing Guidelines for Human-LLM Agent Teams: A Multi-Stakeholder Lens, Mireia Yurrita, Davide Dell’Anna, Pradeep K. Murukannaiah, Catholijn M Jonker, and Pinar Yolum

Nominees

  • UNCAP: Uncertainty-Guided Neurosymbolic Planning Using Natural Language Communication for Cooperative Autonomous Vehicles, Neel P. Bhatt, Po-han Li, Kushagra Gupta, Rohan Siva, Daniel Milan, Alexander Todd Hogue, Sandeep P. Chinchali, David Fridovich-Keil, Zhangyang Wang, and Ufuk Topcu
  • Ratio-Based Signaling for Source-Victim Separation in Swarm Fault Detection, Longyin Cui
  • The Impossibility of Strategyproof Rank Aggregation, Manuel Eberl and Patrick Lederer
  • Generalized Per-Agent Advantage Estimation for Multi-Agent Policy Optimization, Seongmin Kim, Giseung Park, Woojun Kim, Jiwon Jeon, Seungyul Han, and Youngchul Sung
  • R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memory, Maoyuan Li, Zhongsheng Wang, Haoyuan Li, and Jiamou Liu
  • Defection at First Sight: Learning Partner Selection in Optional Social Dilemmas without Prior Information, Benedict Russell, Chin-wing Leung, and Paolo Turrini
  • Characterizing Consensuses in Belief Flow Networks, Nicolas Schwind, Gauvain Bourgne, and Katsumi Inoue
  • Grassroots Federation: Fair Democratic Governance at Scale, Nimrod Talmon and Ehud Shapiro
  • Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning, Yohai Trabelsi, Guojun Xiong, Fentabil Getnet, Stéphane Verguet, and Milind Tambe
  • MeCo: Enhancing LLM-Empowered Multi-Robot Collaboration via Similar Task Memoization, Baiqing Wang, Helei Cui, Bo Zhang, Xiaolong Zheng, Bin Guo, and Zhiwen Yu

Pragnesh Jay Modi Best Student Paper Award

Winner

  • Planning Ahead with RSA: Efficient Signalling in Dynamic Environments by Projecting User Awareness across Future Timesteps, Anwesha Das, John Duff, Jörg Hoffmann, and Vera Demberg

Nominees

  • Efficiently Computing Equilibria in Budget-Aggregation Games, Patrick Becker, Alexander Fries, Matthias Greger, and Erel Segal-Halevi
  • Building Large-Scale Drone Defenses from Small-Team Strategies, Grant Douglas, Stephen Franklin, Claudia Szabo, and Mingyu Guo
  • Robust Counterfactual Inference in Markov Decision Processes, Jessica Lally, Milad Kazemi, and Nicola Paoletti
  • Flow-Based Task Assignment for Large-Scale Online Multi-Agent Pickup and Delivery, Yue Zhang, Zhe Chen, Daniel Harabor, Pierre Le Bodic, and Peter J. Stuckey
  • Reputation as a Solution to Cooperation Collapse in LLM-based MASs, Siyue Ren, Wanli Fu, Xinkun Zou, Chen Shen, Yi Cai, Chu Chen, Zhen Wang, and Shuyue Hu

Blue Sky Ideas Award

Winner

  • Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments, Florent Delgrange

Nominees

  • Guiding Sociotechnical Systems toward Value-Norm Equilibrium, Nirav Ajmeri, Marina De Vos, Davide Dell’Anna, Pradeep K. Murukannaiah, Vivek Nallur, Luis Gustavo Nardin, and Munindar P. Singh
  • The Dynamic Turn in Strategy Logics, Rustam Galimullin, Maksim Gladyshev, Munyque Mittelmann, and Nima Motamed

New Microsoft AI Challenges ChatGPT et al

A long-time investor in ChatGPT, Microsoft has decided to break out on its own and compete directly with the number one player in AI chat.

Microsoft’s opening move: The release of seven AI engines – or models – that together offer AI-powered image, voice, transcription and coding.

Observes Mustafa Suleyman, CEO, Microsoft AI: “Beyond these models, we’re building a super-intelligence lab – a system and an approach we believe will define the next phase of AI.”

In other news and analysis on AI writing:

*Microsoft Looking to Deep-Six Reliance on Anthropic: In a move designed to give new Microsoft AI more prominence, the company has announced that it’s looking to phase-out promotion of Anthropic AI on its systems.

Currently, Microsoft subscribers can use AI engines like Anthropic Claude, ChatGPT while working with Microsoft tools.

But Microsoft CEO Mustafa Suleyman says Microsoft is looking to significantly cut the price of AI for its customers by offering Microsoft alternatives — rather than imported solutions like Anthropic.

*Many U.S. Firms Saying Goodbye to U.S. AI in Favor of DeepSeek: Fed-up with relatively high prices for AI from major players like ChatGPT, Gemini and Claude, increasing numbers of U.S. businesses are using China-based AI alternatives from DeepSeek.

Observes writer Craig Hale: “DeepSeek is also a popular option because of its open-source approach. Companies can download, customize and deploy DeepSeek models on their own infrastructure — which helps to reduce dependency on external providers.”

One caveat: Many Chinese AI companies include terms of service that give the Chinese Communist Party access to company data shared with Chinese AI.

*AI Bubble Burst? Look for a Modest Correction Instead: Investors fearing that sky-high, AI-driven stock prices will lead the U.S. stock market off a cliff can take heart.

Joe Hipsky, a tech entrepreneur assures the trembling that the oft-predicted burst of the AI bubble will instead play out like a modest correction that will hurt few long-term.

Observes Hipsky: “The irony of this phase is that while the market (for AI services) may be cooling, the importance of AI is not diminishing. If anything, it is becoming more critical. The difference is that we are moving from experimentation to expectation.”

*ChatGPT’s One Billion Users: Currently, an Emphasis on Consumers: While ChatGPT’s maker OpenAI is increasing interested in attracting more business users, it’s the current the king of consumer users.

Observes writer Darius Popa: “ChatGPT owns the consumer mass market, while Claude is growing fast from a smaller base, with particular strength among developers and in coding.

“What a billion users buys OpenAI is distribution, the asset that turned earlier consumer-software winners into durable franchises.”

*ChatGPT Repackaging as ‘SuperApp’ for Business Users: In another move designed to portray itself as business-friendly, ChatGPT’s maker is creating a new look to portray the AI as a serious business tool.

Observes Crypto Briefing: “The goal is to transform ChatGPT into a ‘superapp,’ a single platform that bundles coding tools, AI agents, image generation and integrations with third-party services like Canva and Booking.com.

“Among the most notable additions is Codex, OpenAI’s coding tool, which will become a more prominent feature within the platform rather than a separate product.”

*Gemini Pulls Back on Draconian Usage Limits: Much to the relief of many Gemini users, maker Google has decided to ease-up on recently increased usage limits – which forced many users to settle for weaker Gemini AI after they’d maxed-out on usage credits for higher-end AI.

The newly reworked usage monitoring system will put a cap on how much ‘usage’ a single prompt will trigger when using Gemini 3.1 Pro, according to writer Kezia Jungco.

Plus, use of Gemini 3.1 Flash Lite – a much weaker version of Gemini AI – will now be free.

*Gartner: 40% of AI Agent Projects Will Be Deep-Sixed by Close of 2027: In another grim outlook for the ‘magic’ of AI agents, tech consultancy Gartner is predicting many test-drives of AI agents among corporate users are headed for the trash bin.

The reason: Despite promise, AI agents too often simply don’t deliver.

Writer Juras Jursenas details how that problem can be turned around in this piece.

*Major Newspaper Chain Goes All-In on AI-Generated Content: Just a few years ago, the idea of packaging AI-generated content as news was considered by nearly all news organizations as unthinkable.

Now, a major newspaper chain – McClatchy Media – has announced that AI-generated news will be the savior of its business.

Observes writer Mark Keierleber: “During a contentious, off-the-record virtual town hall last month, company executives touted a flood of AI-generated content as a key to solving their business woes — and pleaded with skeptical journalists to get on board.

“Taken together, the executives’ comments appeared to be a threat: Embrace AI or face consequences.”

*Stanford Study: Law Professors No Match for AI: New research from Stanford University finds AI is much better at the law than the professors who teach it.

Observes writer Stephanie Ashe: “In a blind evaluation of nearly 3,000 anonymized comparisons, professors rated AI responses significantly higher than answers written by other professors.”

In fact, AI’s answers to tough law questions were considered better than what law professors could come up with 75% of the time.

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Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

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The post New Microsoft AI Challenges ChatGPT et al appeared first on Robot Writers AI.

AI-designed universal coronavirus vaccine passes first human trial

Scientists have successfully tested an AI-designed universal coronavirus vaccine in humans for the first time, finding it to be safe and well tolerated. The vaccine generated immune responses against multiple coronaviruses, including SARS-CoV-2, SARS, and related bat viruses with pandemic potential. By targeting features shared across an entire virus family, it aims to provide protection even as viruses evolve.
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