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Transformer Geometry Observatory TGO-IV: Developmental Topology Observatory
arXiv:2608.09997v1 Announce Type: new Abstract: Transformers have had a profound impact on the world of language processing and computer vision. As efforts to answer the million-dollar question of ``How does a Transformer learn?" have been increasing, existing interpretability studies primarily analyze representations at isolated layers or the network as a whole, while the developmental evolution of individual representations and its manifolds across transformer layers remains underexplored. With this work, we aim at providing a comprehensive analysis of the evolution of representations as the representation point cloud transforms across the layers; thereby attempting to isolate layers or establish a trend which comes closer to justifying how and when raw input representations evolve into task-relevant feature representations. Thus, Transformer Geometry Observatory-TGO-IV introduces a topological framework for analysing the evolution of Transformer representations through the lens of P
Protection Levels for Vision-Based Pose Estimation
arXiv:2608.10023v1 Announce Type: new Abstract: Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-$n$-Point problem applied to an aviation setting. The algorithm covers all six degrees of freedom of the aircraft pose (position and orientation) directly. We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels. To make the results tangible, we demonstrate tradeoffs in the protection levels on
The Impact of Operational-Data Fidelity when Assessing Safety-Critical Autonomous-Vehicle Software
arXiv:2608.10025v1 Announce Type: new Abstract: For safety-critical software, data from the software's operational past (e.g. a sequence of success and failure events experienced by the software) can provide strong statistical support for reliability claims about the software. However, such data might not describe past software failure events in sufficient detail, and this might leave a reliability assessment (based on this data) unable to account for important features of past software failures. In this paper, by extending conservative Bayesian inference (CBI) techniques used in reliability assessment, we illustrate a principled statistical approach for checking the robustness of reliability claims derived from insufficiently detailed operational data. We demonstrate the extent to which insufficient detail in operational data can undermine software reliability claims in autonomous vehicle (AV) safety assessment scenarios. Reliability claims derived from insufficiently fine-grained dat
MAI-Image-2.6 Is #2, but It Hasn’t Earned Your Subscription Yet
A high Arena rank can put a model on your shortlist. It can t fill in access, price, limits, or the cost of switching.Continue reading on Medium »
How Google Flow Makes AI Video Creation Easier for Beginners
Creating professional videos can be difficult for beginners because traditional video production requires technical skills, editing Continue reading on Medium »
Automating the documentation of a mid-size chaos
How I discovered what up-to-date documentation really means.Continue reading on AI Advances »
One API Key for Every AI Model and Get started with 50 Free Credits
Chat with Claude, OpenAI, Gemini, the trending Kimi3 and more through one OpenAI-compatible API key and let smart routing pick the right Continue reading on Medium »
Saber denies replacing Rideshare Stimulator’s writers with ChatGPT
After a former lead writer claimed Saber "replaced me with ChatGPT," CEO Matthew Karch now claims, "Neither Saber nor Unigine have replaced any writers with AI," for the Rideshare "Stimulator" game announced last month, developed by Unigine. The writer, Stella Sacco, says differently, however, posting on Bluesky that "I was lead writer on this one! And Saber replaced me with ChatGPT midway through development. All the passenger voices were AI too. Either they changed direction at some point or they're not disclosing it on Steam." Saber describes Rideshare "Stimulator" as an "immersive driving simulation game that puts you behind the wheel … Read the full story at The Verge.
Muse Glimmer 30B on an M4 Pro: Local Ollama Benchmark Results
Three numbers are worth remembering:Continue reading on Medium »
Space Is Freezing. So Why Is Cooling the Hardest Part?
A deep dive into the first myth of data centers in space, and why it s cold up there, so cooling is free gets the physics exactly Continue reading on Medium »
Xbox Elite 3 prototype pad leaks with tiny built-in screen
Did a prototype Microsoft gamepad fall off the back of a truck? We can't say, but a Redditor has legit-looking photos of a prototype Xbox controller that's almost certainly the Xbox Elite Series 3. Incredibly, they claim they bought the pad for $200 on OfferUp. In May, a Brazilian regulator's leaked images revealed that Xbox Elite 3 would have new two new scroll wheels on the bottom edge, a second wireless mode to connect directly to Xbox Cloud Gaming servers, and a new "Pair Button" that can also switch between those servers and your console. Where might that pair button live? Today, we have a possible answer: it might sit right next to t … Read the full story at The Verge.
WorldClaw Agentic 3D open-world generation at scale
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