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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 »
Practical ways SMEs are starting to use AI
Accountingbpo Pte Ltd Client Newsletter Originally published on 6th March 2026Continue reading on Medium »
LearnVector – Andrew Ng's AI company building one‑to‑one learning experiences
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I Let an AI Agent Run My CI/CD Pipeline for a Month. Here’s What Actually Changed.
Agentic DevOps is the biggest shift in software delivery since we all moved to the cloud and most teams are still treating AI like a fancy Continue reading on Medium »
From the Processor Wars to the AI Race: Why History Repeats Itself (and How to Avoid the Crash)
The lessons of obsolescence and corporate chaos from the 90s are the instruction manual that modern AI is ignoring.Continue reading on Medium »
Cyera agrees to acquire Oasis Security for $1B to safeguard proliferating AI agents
The deal is Cyera's third acquisition this year.
Wendy Scott: AI Won’t Replace L&D Professionals
Using AI To Build Training Better FasterContinue reading on AI Center — Grow Earn with AI »
Deep dive into AI caching strategies — from application to model provider
Caching is the most important optimization in production LLM applications. A typical agent call chain system prompt, tool schemas Continue reading on Medium »