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Your AI Coding Agent Doesn’t Need to Be Smarter. It Needs Clear Instructions.
What I learned from building GiftLink with AIContinue reading on Medium »
Surviving the paper deluge: a one-year study in learning from demonstration
With the explosion of robotics research, staying current in fields like Learning from Demonstration (LfD) is a monumental challenge. Is AI the solution to the “paper deluge,” or is it part of the problem? Read the article preview below to learn more! Download the full paper: Surviving the Paper Deluge. Authors: Aude Billard, Renaud Detry, [ ]
Why Your Business Needs AEO Services in Mohali for Better AI Search Visibility
Why Your Business Needs AEO Services in Mohali for Better AI Search VisibilityContinue reading on Medium »
Why Trust Is Becoming the Most Important Feature of AI
Despite rapid advances in artificial intelligence, one factor continues to shape its adoption: trust.Continue reading on Medium »
Why Most Business Websites Lose Customers (And How AI Can Fix It)
Every business owner wants more website visitors. They invest in SEO, run Google Ads, post on social media, and spend time creating Continue reading on Medium »
The “Aesthetic Paralysis” Paradox: Why Beautiful Marketing is Killing Your Conversions
Why your most beautiful campaigns are actually costing you sales.Continue reading on DataDrivenInvestor »
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Do Models Fake Alignment Without Clear Consequences?
arXiv:2607.24758v1 Announce Type: new Abstract: Large language models are capable of recognizing evaluation contexts and altering their behavior to reflect evaluator expectations rather than typical deployment behaviors, a phenomenon known as alignment faking. The reasons why models fake alignment are not fully understood, however. Canonical examples of alignment faking have taken place in scenarios that explicitly connect evaluation to consequences for the model, such as retraining the model or delaying its deployment. However, recent work by Sheshadri et al. has suggested that mechanistic motivations for alignment faking may vary across models and be more complex than previously considered. To investigate whether consequence-linking information is necessary for alignment faking, we placed 15 models in a scenario testing their willingness to violate a corporate network access policy to help a user with a pro-social request. Nine models were found to produce significant compliance gaps
Egocentric Station Holding of Robotic Fish in Unknown Turbulent Background Flow
arXiv:2607.24860v1 Announce Type: new Abstract: Approaching a target position and holding station in flowing water is a fundamental and critical capability for robotic fish operating in natural aquatic environments. Despite decades of advances in enhancing swimming efficiency and maneuverability, this capability remains underdeveloped, largely owing to the insufficiently characterized, highly nonlinear fluid-structure interactions inherent to freely swimming robotic fish in flows. To bridge this gap, we propose the SWiFT framework, a Swimming With Flow Toolbox that enables the efficient exploration of an egocentric station-holding policy for a body and/or caudal fin (BCF) robotic fish in unknown and turbulent background flows via reinforcement learning (RL). Our SWiFT integrates a free-swimming flow-tank experimental setup with a highly efficient, physically consistent computational fluid dynamics (CFD)-based simulator and a systematic sim-to-real transfer pipeline. The resulting polic
Beyond Memory: A Templated Substrate for Heterogeneous Collaborative Knowledge Work with LLM Agents
arXiv:2607.24759v1 Announce Type: new Abstract: Research projects, educational efforts, and adjacent knowledge work accumulate findings, decisions, and reasoning that future collaborators rarely recover. The parts most useful to that work, including dead ends and walked-back claims, are routinely excluded from publications and shared code; future researchers re-attempt the same failures because no record survives. LLM coding agents are common participants but hold no persistent memory across sessions, and retrieval-augmented generation over raw sources does not compound. The llm-wiki pattern (Karpathy, 2026; tonbi, 2026) addresses this by inserting an LLM-maintained, interlinked wiki between raw sources and the agent. We present llm-wiki-memory-template, a reusable, agent-aware instantiation, and argue it is a substrate for heterogeneous collaborative knowledge work along three axes (multi-human, multi-AI-agent, multi-domain) with each axis supported by a distinct architectural element
Steeringless Drifting: Differential-Torque Control of a Four-Wheel Independently Driven Vehicle
arXiv:2607.24863v1 Announce Type: new Abstract: Control methods for emerging vehicle chassis architectures are important for autonomous driving near handling limits. Unlike conventional drift control, which relies on mechanical steering and rear-tire saturation, a steering-free four-wheel independently driven (4WID) vehicle can generate direct yaw moment through differential wheel torques. This paper proposes a differential-torque drift control method for such a vehicle. A double-track vehicle model incorporating four-wheel differential actuation is established, based on which a drift-equilibrium calculation method and a closed-loop drift controller are developed. The proposed approach is validated through simulations and experiments on a 1:10-scale vehicle. The results show that the vehicle can achieve steady circular drifting with a sideslip angle of approximately 20$^\circ$ and perform figure-eight drift tracking. This study demonstrates the feasibility of drift control using only d
The 9x PM Is Real. It’s Just Not the One You’re Picturing.
AI did not replace project managers. It quietly split us into two groups and the distance between them widens every quarter.Continue reading on Medium »