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PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
arXiv:2607.20653v1 Announce Type: new Abstract: Predicting how deformable objects evolve under robotic manipulation is a longstanding challenge. Existing approaches typically rely on per-object optimization to fit material parameters, which can be slow and cannot generalize, while end-to-end learned alternatives extrapolate poorly and often violate basic physical structure. We present PhysCoRe, a physics-corrected residual world model that couples a differentiable Material Point Method (MPM) simulator with two feed-forward neural networks. A material refinement module, Material from Motion (MfM), infers per-particle elasticity from visual observations, grounding the simulator in object-specific physics. A residual correction module, Residual from Dynamics (RfD), learns the discrepancy and predicts corrections to the simulator's internal dynamics, absorbing systematic biases that the analytical model cannot capture. This design also supports online material identification on novel objec
Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts
arXiv:2607.20462v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output with watermarking. Yet, most watermarks are evaluated on general-purpose benchmarks, leaving domains like medicine, where small token-level perturbations can result in significant semantic changes, underexplored. In this work, we present the first rigorous study of how LLM watermarks affect medical performance, benchmarking 5 watermarking schemes across 11 LLMs and 7 VLMs on various tasks spanning unimodal and multimodal clinical reasoning. Importantly, we complement existing evaluations by introducing a human-expert-validated pipeline for systematically auditing medical reasoning quality, terminological precision, and induced hallucinations. Our results reveal that watermarking can induce substantial degradation across multiple failure modes, including lexical corruption, hallucinated te
PhantomFill: When the Form Demands an Answer, Language Models Invent One
arXiv:2607.20492v1 Announce Type: new Abstract: Language models in production do not write prose. They fill forms: JSON fields, function arguments, extraction templates. We show that the form itself causes hallucination. We ask thirteen models the same question about the same input and change only the answer format. The inputs are built so the question cannot be answered: a viral post showing 12,400 likes but no visible replies, a support ticket whose call was never transcribed. In free text, GPT-5.5 answers honestly. It says there is no reply data, 98% of the time. Given a required JSON field for sentiment, the same model invents an answer 40 times out of 40. It fabricates the mood of crowds it never saw and quotes customers it never heard. The pattern holds with force. Required fields drive fabrication to 100% in ten of thirteen models. An explicit "insufficient evidence" option rescues only the frontier: all nine open-weight models ignore it. A direct instruction, do not infer senti
Everyone Talks About AI Replacing Jobs. Few Talk About the Skills That Still Matter
Why learning how to think, adapt, and solve problems may be more valuable than chasing every new AI tool.Continue reading on Medium »
When Not to Use AI in Architecture Decisions
The structural limits that make AI useful in some architecture work and actively misleading in others.Continue reading on Medium »
Congress Wants to Give You a Copyright on Your Own Face
A few months ago a friend sent me a voice note that was not from the person it claimed to be. It was a founder we both follow, someone Continue reading on Level Up Coding »
Aurelian Heritage Intelligent Alliance Office: Exploring the Impact of Artificial Intelligence
Artificial intelligence is reshaping the way organizations analyze information, deliver educational resources, and support informed Continue reading on Medium »
How AI guardrails are impeding the work of offensive cybersecurity researchers
We spoke with several cybersecurity researchers, who look for unknown vulnerabilities and develop tools to exploit them, about how OpenAI’s and Anthropic’s guardrails affect their work.
Meet the judges who will crown Australia’s next breakout startup
TechCrunch Startup Battlefield is coming to Australia — and we're partnering with Stripe to find the country's most exciting early-stage startups.
How I Built & Launched an AI SaaS App in 24 Hours Using Gemini 2.5 Flash, Supabase, and Stripe
A complete technical breakdown of FridgeToFeast from prompt engineering and Row Level Security to hands-free voice cooking and Stripe Continue reading on Medium »
Tech Career Advice in the AI Era:
What I Tell High Schoolers, College Students, and My Own Fifteen-Year-Old About Building a Career in Tech and AIContinue reading on Medium »
Microsoft launches new in-house AI models it says cut costs up to 89% versus OpenAI
Microsoft AI released two new in-house models into public preview on Wednesday — MAI-Image-2.5-Pro, its highest-fidelity image generator to date, and MAI-Voice-2-Flash, a speech model built for high-volume enterprise workloads — while publishing production data that amounts to the company s most aggressive argument yet that it can power its own products without leaning on OpenAI s frontier models.The announcement, made by Microsoft AI s Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft s homegrown models are no longer research projects. They are production infrastructure serving millions of users. Each of these enhancements is a step toward the same goal: Microsoft products, powe