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Beyond Relative Geometry: Metric-Aware Geometry Perception for Robotics
arXiv:2608.27497v1 Announce Type: new Abstract: Recent embodied models increasingly leverage geometric representations to improve spatial reasoning and robotic manipulation. However, existing reconstruction methods only reconstruct relative geometry with arbitrary scales, causing predicted object dimensions and spatial distances to vary across scenes, viewpoints, and input configurations. This inconsistency prevents geometric perception from being directly aligned with robotic actions defined on the real-world scale. To address this limitation, we propose Metric-Aware Geometry Perception (MAGP), an end-to-end, plug-and-play framework for metric geometry reconstruction that can be seamlessly integrated into robotic policies. At its core, Metric Scale Equivariant Augmentation encourages the model to reconstruct metric geometry from camera parameters and depth observations, ensuring that the reconstructed geometry follows the metric scale specified by observations. Flexible Metric Conditi
Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap
arXiv:2608.27512v1 Announce Type: new Abstract: Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluation, this workflow creates a structural validation--deployment gap: because quantization is a many-to-one mapping over parameter space, source-precision certification does not guarantee behavioral equivalence in the deployed configuration. We formalize this gap through Quantization Behavioral Equivalence Classes (QBECs) and prove that QBEC membership does not imply behavioral equivalence, providing a theoretical basis for quantization-triggered backdoor attacks. Building on a three-stage adversarial fine-tuning framework, we embed latent malicious payloads into models that satisfy the source-precision checks used in our evaluation, yet activate targeted adversarial behavior upon INT8 or 4-bit
Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality
arXiv:2608.27545v1 Announce Type: new Abstract: This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moisture assessment in a greenhouse environment. The robotic system comprised an unmanned ground vehicle and a robotic manipulator equipped with cameras, governed by kinematic models for navigation and manipulator control. Fourteen distinct plants were inspected across two experiments utilizing VR teleoperation, guided by a set of pre-specified research questions and hypotheses. In the leaf inspection experiments, cycle completion times varied from 3.3 to 8.0 s, and plant-based disease detection was achieved up to 88% accuracy; diseased-spot detection improved numerically in the second experiment, though this change was not statistically significant (p=0.378). For soil moisture assessment, the experiments achieved successful determination of watering needs in up to 64.3% of plants (9 of 14), with consistent success o
Marginal Coverage Credit Reduces Redundant Exploration in Parallel State-Entropy Optimization
arXiv:2608.27507v1 Announce Type: new Abstract: Policy Gradient for Parallel State Entropy maximization (PGPSE) expands state-space coverage by training independently parameterized policies in replicated copies of the same environment. However, its pooled team-entropy score measures only collective exploration and cannot identify policies that contribute non-redundant coverage. We introduce Marginal Coverage Credit for PGPSE (MCC-PGPSE), which combines leave-one-policy-out coverage with state-owner specialization to estimate policy-specific credit. MCC-PGPSE preserves PGPSE's pooled objective and redistributes non-negative auxiliary intrinsic rewards according to these credits without changing their total mass. This redistribution is designed to discourage redundant visitation and promote complementary coverage. We evaluated MCC-PGPSE in controlled environments, seven public discrete-state benchmarks, and representative Room and Maze settings from the original PGPSE protocol. Across al
Stop Building Things Nobody Asked For
The biggest advantage AI gives you is speed. The biggest mistake is using that speed to build the wrong thing.Continue reading on Medium »
ChatGPT Ads Are Coming to India: What Changes for Free Users?
OpenAI is bringing advertising to ChatGPT s Free and Go plans in India, marking a notable shift in how one of the world s most widely used Continue reading on Medium »
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The U.S. is building barriers around drones and robots, but China has scale to get around them
The U.S. is shutting out more foreign-made drones and robots. China’s scale means the global competition may simply move elsewhere.
Grindr wants to be the everything app for gay men; investors are still deciding whether it can pull it off
George Arison is done letting Wall Street's "Grindr discount" go unchallenged — in a wide-ranging Q A, the CEO walks us through how AI, a controversial $350-plus EDGE tier, and a bet on healthcare and long-distance matchmaking are turning Grindr into the "gayborhood in your pocket" he's been promising since 2022.
7 Best AI Study Tools Every CS Student Should Try in 2026
Study smarter, prepare faster, and learn technical concepts more effectively with these 7 AI-powered tools.Continue reading on AI Engineering Compass »
Why 90% of Suno AI Songs Sound Like a Robot in an Elevator
One year ago, I was that person.Continue reading on Medium »
The AI Agent Is the New Entity in Commerce
There is a new entity in commerce that never existed before:Continue reading on Medium »