What AI topic do you want me to break down in detail next weekend? • Astra safety & containment • The SVP Chain agent-to-agent economy • Energy & data center bottlenecks • Open-weight models catching up Drop your pick in the comments 💬 #AgenticAI# #SVPChain# #AIResearch# #CryptoAI# #TechDiscussio#

⏳ The belief that "people used to be more moral" can fade after just 22 minutes talking to an AI that only knows text written before 1930. Title: Time Machine Experiments: Using Historically-Bounded AI for Inquiry into the Human Mind URL: 🧠 Overview This study tests whether the "illusion of moral decline" — the bias that past societies were more moral than today's — can be reduced by talking with Talkie, an AI trained only on pre-1930 text. 🎯 The problem it solves Archives can't answer new questions, and living witnesses have their memories distorted by knowledge of what came after. Until now there was no way to actually experience a "raw" historical perspective. 🛠 Method Instead of prompting a modern model to roleplay a historical persona (which leaks modern knowledge), the researchers used a model whose training data itself stops at 1930. 240 participants were randomly split between the 1930-AI and GPT-5.5 conditions and discussed morally contested sentence completions with their assigned model. 📊 Results The 100-year moral-decline score improved from -0.80 to +0.07 in the 1930-AI group (d=0.56), with almost no change in the control. Participants revised their view of the past downward rather than inflating the present, and also reported significantly deeper reflection. #BehavioralScience# #AIResearch#

🔥 @TencentHunyuan Hy4 preview just cited a Zhihu post with a surprisingly simple challenge to DeepSeek’s mHC: What if the doubly stochastic matrix is overcomplicating things—and Identity actually works better? Today, let’s revisit the cited post, “Your DeepSeek mHC May Not Need the ‘m’,” by Zhihu contributor 涮月亮的谪仙人. After training Qwen3 1.7B and 8B dense models from scratch on 150B tokens, the experiments found: 💡 Identity HC > mHC > mHC-lite > orthogonal mHC In other words: simply setting H_res = Identity beat the Sinkhorn-Knopp constrained version. Here’s why 👇 1️⃣ What does mHC actually change? Standard Transformers have one residual stream. Hyper-Connections (HC) expand it to multiple parallel streams: 🔹 H_pre reads from the streams 🔹 H_post writes back to them 🔹 H_res mixes information between them DeepSeek’s mHC constrains H_res to a doubly stochastic matrix via Sinkhorn-Knopp, helping preserve norms and stabilize propagation. But the experiments suggest a simpler question: Do we need H_res mixing at all? 2️⃣ mHC seems to learn something close to Identity anyway For a single layer, the learned H_res is already close to Identity: diagonal ≈ 0.96, off-diagonal ≈ 0.01 But multiply H_res across many layers, and it gradually collapses toward a uniform 0.25 matrix. So each layer may look almost like Identity, while their cumulative effect becomes uniform mixing. The simplest fix? Just set H_res = I. 3️⃣ Identity preserves stream semantics With Identity, each residual stream stays where it is: Stream 0 stays Stream 0. Stream 1 stays Stream 1. No repeated reshuffling, no cumulative mixing, and Iᴸ = I. H_pre and H_post also no longer need to track where each stream has been repeatedly moved—they simply learn where to read and where to write. 4️⃣ But cross-stream communication still happens Setting H_res = I does not isolate the streams. The projection that generates H_pre and H_post already sees all residual streams, making H_pre input-dependent. So information can still be dynamically aggregated across streams before Attention/MLP and written back afterward. H_res isn’t the only mechanism for cross-stream interaction. 5️⃣ Why might Sinkhorn hurt? Repeated products of positive doubly stochastic matrices tend toward uniform mixing. In the Qwen3-1.7B experiment, after 56 HC modules, the minimum singular value of the accumulated H_res product reached just: 9.2 × 10⁻¹⁸ By ~10 layers, the four streams were already approaching the same 0.25 uniform mixture. Sinkhorn also comes with extra cost: 20 iterations, backward recomputation, extra parameters, and approximation error. Identity has none of these—and preserves the residual signal exactly. 6️⃣ More sophisticated alternatives didn’t win either The experiments also tested mHC-lite, softmax-weighted convex combinations, and orthogonal variants using Cayley/Givens transforms. The observed ranking remained: Identity HC > mHC > mHC-lite > orthogonal mHC The simplest design won. 💡 The takeaway DeepSeek’s mHC uses sophisticated manifold constraints to stabilize Hyper-Connections. But these experiments suggest that H_res itself may not need to be learned or mixed at all. Sometimes the best manifold constraint is the most boring one: H_res = I. Or, as the original post puts it: Maybe DeepSeek’s mHC doesn’t need the “m.” 😆 👉 Read the full Zhihu post for the training curves, mathematical analysis, H_res visualizations, and implementation details: #DeepSeek# #mHC# #Hunyuan# #Tencent# #LLM# #Transformer# #AIResearch# #AI#

Ask an AI to write a paper and it will happily report favorable numbers for experiments it never ran — this "plausible fabrication" has been the real wall for research automation. Earlier automated-research systems poured effort into standing up dedicated agent infrastructure and forcing a single clean story to completion. But somewhere in the long generation, factual integrity would fray, and unsupported claims slipped straight into the paper. Behind the flashy results, the foundation of reliability was quietly missing. Spark-to-Paper flips the framing. Instead of a new agent platform, it implements paper generation as 13 composable skills running inside Claude Code. The key is separation: context-dependent judgment goes to the model, while citation checks and LaTeX compilation go to deterministic code. Experiments fix their metrics and table structure before results are observed, blocking post-hoc adaptation. And a "Self-Refutation Loop" — endlessly revising a hypothesis the evidence keeps rejecting — is capped at seven cycles, recording failure as failure and moving on to a different direction. The results speak clearly. Citation validity hits 99.5%, above the human-preprint average, and fabrication detection climbs from 14% single-pass to 92% with the full adversarial-review stack. At roughly $8.1 and 3.2 hours per manuscript, you get a paper complete with editable figures. Rather than dressing every attempt up as success, it centers evidence integrity and the acceptance that "not every research direction works." A thought-provoking blueprint for reliability in long-horizon AI. Spark-to-Paper: End-to-End Research Paper Generation as a Composable Skill #AIResearch# #AgentSkills#

AI systems now write research papers autonomously — yet phantom references, method-code misalignment, and unreproducible scores have become endemic failures undermining scientific integrity. Title: Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence Google Cloud's Science One Framework treats verifiability as a first-class architectural constraint through the Chain-of-Evidence principle, solving the trustworthiness crisis in autonomous AI research at its root. 🔍 Highlight 1 — Chain-of-Evidence (CoE): two foundational principles Completeness: every claim carries a recorded evidence chain. Correctness: each chain genuinely supports its claim. These two principles eliminate phantom references entirely — baseline systems showed rates up to 21% — while achieving best-in-class method-code alignment across all evaluated systems. The key difference from prior work: evidence chains are constructed at claim-generation time, not retrofitted as a post-hoc check. 🏗 Highlight 2 — Three-module architecture Problem Investigator builds citation graphs from up to 100 full-text PDFs via Semantic Scholar API, grounding every reference in retrieved data rather than model memory. Discovery Engine explores parallel solution branches while keeping immutable records of all raw evaluator outputs. Paper Writer and Claim Verifier binds every factual claim to specific workspace artifacts and conservatively reconciles misalignments rather than deleting them — preserving scientific transparency. 🏆 Highlight 3 — MLE-Bench and Parameter-Golf results Across five Kaggle competitions covering medical imaging, fine-grained recognition, and 3D perception: two Gold Medals and two Silver Medals. Won the 3D Object Detection task where every baseline system failed completely. On Parameter-Golf — a live LLM training competition under strict hardware and file-size constraints — achieved state-of-the-art as of April 27, 2026, while baselines could not produce valid submissions at all. Rigor and capability don't trade off. Science One outperformed five state-of-the-art systems including AI Scientist v2, AutoResearchClaw, and DeepScientist, setting a new standard for verifiable autonomous research. #AIResearch# #AutonomousScience#

AI agents given 6 days and $3K produced two research papers, and both were rejected. The people who had spent months on those questions graded what the AI agent wrote. The failure was judgment. The main runs used Claude Opus 4.8 with extra-high reasoning on the OpenClaw scaffold, chosen after dry runs across OpenAI and Anthropic models, including an early pilot with GPT-5.3 Codex that could not handle the scaffold. Execution was never the problem. The agents ran hundreds of experiments, debugged crashing GPU pods, and compiled camera-ready LaTeX without a human touching anything. They were honest about it too, because the logs show marketable claims being retired in favor of negative results rather than any reward hacking. The failure was judgment. Round after round of automated reviews came back negative, but each response narrowed the claim and added a caveat instead of redesigning the experiment. Neither run noticed it was short on ideas rather than money, since both ended with over half of the $3K unspent. – arxiv. org/abs/2607.27191 Title: "Can AI agents conduct open-ended AI research? Early evidence from two case studies"

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or @alayastd. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

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MISTRAL AI RAISES RECORD €3B AT €21B+ VALUATION French AI startup Mistral has raised €3B in what is being described as the largest-ever equity round for a private European tech company, nearly doubling its valuation from €11.7B a year ago to more than €21B. Samsung, PSG Equity and the EU-backed Scaleup Europe Fund co-led the round, with BlackRock joining as a new investor. Existing backers including NVIDIA, ASML and Salesforce Ventures also participated. Mistral plans to use the capital to fund frontier AI research, expand its computing infrastructure and data-center footprint, and grow further across Europe, the U.S. and Asia. Source: Bloomberg