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01Import AI
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now New benchmark shows how AI models lack intuitive visual reasoning: …Humanity’s Sixth Sense highlights some shortcomings of current AI systems… AI startups Elorian and Scale AI have built and released Humanity’s Sixth Sense, a benchmark that “measures how a model interprets and reasons through information a visual scene implies but doesn’t explicitly show.” Some examples of the types of task in HSS are a picture of a bookcase and being asked whether it can fit more books, or a photo of horses pulling a plough and a question of whether the chains bear the plough’s drag weight. HSS contains 522 questions that cover 288 images and 234 short video clips (totalling 17.6 hours of video). The questions fall into four categories: Temporal & causal dynamics: “What happened before the scene, and what happens next.” Physical & spatial logic: “What fits, what’s in reach, and what someone else can see from where they are standing.” Social understanding : “Intent, attention, belonging.” Abstract & contextual inference : “The patterns and rules a scene implies.” Results: The human baseline for this is not perfect, but is very high, with people scoring aggregate pass@1 of 93.1, broken down across four categories: Image (94.1), video (91.9), time (97), and space (94.9). By comparison, the best scoring AI system, GPT 6 Astra, gets pass@1 of 53.6, across image (55.6), video (51.1), time (58.2), and space (53.6). Interestingly, Chinese open weight models do poorly here, with GLM-5.3-flash getting a pass@1 of 26.8, and Kimi K3 getting pass@1 of 25.5. “The two most common single mistakes were (1) missing a detail that decides the answer (21% of failures) and (2) misidentifying an object, person, or role (20%),” they write. Why this matters - HSS tells us whether world models are emergent: One way of looking at HSS is highlighting deficiencies in current systems, but a more interesting way is highlighting how adept today’s frontier LLMs are at doing tasks which many people have long assumed would require sophisticated, different AI systems to solve. For instance, many people have claimed that true visual reasoning will rely on developing spatially aware 2D and 3D world models, then using that for reaso…
AIhub · Research news and interviews
By Melissa Lee , Monash University In August, artificial intelligence company OpenAI announced ten advances in mathematics and computer science made with their as-yet unreleased model Astra. The discoveries cover a wide range of mathematical fields, including geometry, cryptography and coding theory. These ten are just the latest in a rapidly growing list of mathematical breakthroughs made by generative artificial intelligence (AI) systems. OpenAI accompanied the announcement with a statement on “responsibility to the mathematical community”. Here, the company acknowledges concerns about attribution, accountability for the correctness of AI-assisted results, and the changing nature of mathematical discovery. The statement reflects a growing understanding that the remarkable technical advances due to AI are only part of a bigger story about what mathematics means, and what its future will look like. AI is raising big questions Large language models (LLMs) such as ChatGPT and Claude are creating disruptions in all parts of the mathematical community. University departments are grappling with the ethical implications of these tools in their research and teaching. Editorial boards of many peer-reviewed journals are being inundated with AI-written papers and must decide how to assess and disclose the use of generative AI. The arXiv preprint repository has seen a sharp increase in mathematical submissions in recent months. Math papers uploaded to @arxiv per month. Jan 1992 to Jul 2026. Source: https://t.co/nrqjhJBtij pic.twitter.com/pmHvtbYK2X — Pablo Groisman (@pgroisma) August 3, 2026 Funding agencies are starting to develop policies governing acceptable use of AI in applications and assessment. Broader philosophical questions about how and why mathematics is done in the first place are hard to avoid. If an LLM can discover a proof, construct an example, or formulate a new question, is there anything uniquely valuable about human mathematical creativity? Is mathematics mostly about producing new theorems, or is it about developing understanding? When a machine contributes to a discovery, who should get the credit? Growing concern – but little consensus Among working mathematicians, there is little consensus about the answers. Some have genuine anxiety about the future of the discipline. One mathematician wrote that the advance of AI-assisted mathematics had tri…
