The best predictions rarely come from one voice. They emerge when different perspectives meet, challenge each other, and turn collective insight into a clearer signal. This International Day of Friendship, here’s to the people who help us see what we might have missed. 🤝🔮 #InternationalDayOfFriendship# #CollectiveIntelligence#

Enter the AI economy with @NeoSoulAI 🧬 Join the campaign on @GalxeQuest to explore a self-evolving system powered by collective intelligence. Help refine intelligence in real time and earn $OUL points and $USDT rewards:

Livestreamed event from London. July 7. eToro CEO, @yoniassia, leads a show-and-tell under the theme Intelligence in Motion: human insight supercharged by AI. Learn how eToro is combining the collective intelligence of millions of investors with the power of AI.

"Technology changes, technology evolves, and we harness that technology to execute on our mission and vision faster." Catching Amy Butler, our VP Corporate Communications, on the green carpet to talk about how technology enables us to open up global markets to everyone, and why the combination of AI and collective intelligence is our edge.

🐡 One day a regulation changes and the model you depend on loses its API overnight—that reality lays bare the risk of single-vendor dependence in AI. Export controls on Fable and Mythos showed access can be cut in an instant. So rather than betting everything on one giant model, maybe the resilient blueprint for AI sovereignty is collective intelligence: many models, orchestrated to collaborate. That is the question Sakana AI poses. Its answer is Sakana Fugu: One Model to Command Them All. Fugu isn't a mere router—it's a language model trained to call various LLMs in an agent pool. You send a request to one endpoint, and Fugu decides whether to solve it directly or assemble a team of specialized models, handling selection, delegation, verification, and synthesis internally. It calls itself recursively, and because agents in the pool are swappable, it can dynamically route around restricted models—the crux of "sovereignty." It builds on Trinity and Conductor (ICLR 2026), running on learned orchestration rather than fixed workflows. 🚀 The accuracy-focused Fugu Ultra stands shoulder-to-shoulder with Fable 5 and Mythos Preview on rigorous reasoning, science, and engineering benchmarks, and on some tasks beats Gemini 3.1 Pro, Opus 4.8, and GPT 5.5. Among 500 beta users, a code reviewer found more than twenty issues where competitors flagged about three, and data-science research progressed with little human intervention. From monolithic scaling toward a collaborative ecosystem. URL: #AIAgents# #LLM#

Skills that leaders identify as key to long-term organizational performance—judgment, problem understanding, creative thinking, and more—are the ones that they consider most at risk due to AI. When this skill attrition occurs across thousands of people simultaneously, the business's collective intelligence quietly degrades. This is “distributed de-skilling”—a collective erosion of human skills that undermines organizational intelligence and resilience over time. Here are six strategies to mitigate de-skilling risk:

Can test-time scaling work for diffusion language models? In our #ICML2026# paper "UnMaskFork," we show that having multiple masked diffusion language models collaborate on a single answer improves performance on coding and math tasks. Blog: Test-time scaling is an actively researched technique that boosts LLM performance by using inference-time compute, for example, by having a model think longer or repeatedly refine its answers. This allows us to enhance performance simply by increasing computation during inference without relying on additional training, giving us the flexibility to balance compute costs and performance based on the specific use case. Unlike standard LLMs that generate text left-to-right, masked diffusion language models (MDLMs) generate text by gradually filling in a fully masked sequence. MDLMs can generate multiple parts of a sequence in parallel, offering potential speed-ups, and they can generate flexibly while seeing the entire sequence at once. This makes them an actively studied new paradigm in language modeling. We found that the standard LLM approach of "raising the temperature to increase randomness and generate diverse answers" does not work well for MDLMs like Dream-Coder. Instead of relying on this randomness, our proposed method, UnMaskFork (UMF), creates diversity through "model switching." Multiple MDLMs share the task of unmasking a single answer, and we use Monte Carlo Tree Search to search for a promising sequence in which different models handle different stages. Each model picks up where the others left off, filling in the parts it is most confident about. This collaborative approach allows us to explore diverse answers while maintaining generation quality, consistently outperforming existing test-time scaling methods on coding benchmarks and scaling effectively on math as well. Test-time scaling is also crucial for advancing MDLMs, and our work shows that UMF can sidestep the difficulties specific to them. UMF requires no additional training or changes to the models; it works simply by combining pre-trained models at inference time. This allows us to leverage the diversity of diffusion language models trained on different data and with different methods to improve performance. We believe the value of UMF will only grow as more diverse MDLMs emerge. This work is part of our broader research into "collective intelligence of AI," alongside methods like AB-MCTS and Sakana Fugu that have multiple LLMs collaborate. We'll continue pursuing research that turns model diversity into a source of strength. For details of the algorithm and illustrative examples showing how this collaboration works, please see our blog and paper. Paper: 🐟