ROBOMARKET
Stay Informed
Daily coverage from 2783 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 02:00 AM (1m ago)
Auto-refresh every 2 hours
Scheduling and Routing with Degradation-Triggered Job Arrivals: An Application to Forest Firefighting with an Unmanned Aerial Vehicle Fleet
arXiv:2608.18140v1 Announce Type: new Abstract: We define an intertwined scheduling and routing problem where new jobs appear due to the degradation of the existing jobs. Specifically, once a job arrives at a potential job location, a time window begins during which the demand of the job can be fulfilled. The demand degrades within the time window, and once it surpasses a particular threshold, it triggers the arrival of new jobs. Each job location inherently possesses an initial default reward, and the presence of an unprocessed job at a location gradually reduces this default value. The overall objective is to maximize the total remaining reward. The underlying motivation of this problem aligns with the proverb ``a stitch in time saves nine," and the problem itself carries practical implications. We focus on the problem in the context of aerial forest firefighting. Each ignited area has a designated action window; delaying intervention causes the fire to grow, diminishing the area's v
Trust as a Field: A Macroscopic Representation for Vehicular Networks
arXiv:2608.18178v1 Announce Type: new Abstract: Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of individual vehicles, making it difficult to reason about trust evolution across road segments. In this paper, we propose a spatio-temporal trust-field framework that aggregates microscopic vehicle-level trust into a continuous representation over space and time. The trust field is formally defined on road segments. We conducted simulation-based experiments using synthetic trajectories generated under controlled conditions, enabling analysis of trust-field behavior in simple road scenarios. Beyond theoretical modeling, we study an implication of the trust-field concept: reconstructing the full trust field from sparse roadside-unit (RSU) measurements. We compare (i) a coordinate-based deep learning baseline that learns a generic trust field from sparse samples and (ii) a field-informed deep
Entropy-Constrained Adaptive Stochastic Quantization
arXiv:2608.18147v1 Announce Type: new Abstract: Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. It is designed to alleviate the communication and memory bottlenecks of modern data and machine learning workloads, including model, gradient, and KV-cache compression and nearest-neighbor search. Further, practical systems can then compress quantized data with a lossless entropy encoder. However, existing unbiased methods, including ASQ, choose their quantization values without considering this later encoding stage, leaving accuracy on the table. We formulate the Entropy Constrained Adaptive Stochastic Quantization (ECASQ) problem, which jointly selects adaptive quantization values to minimize MSE under an entropy budget and an unbiasedness constraint. We give an optimal dynamic program with $O(sd^2)$ time and $O(d^2)$ space for a length-d vector and at most s q
Position: Profiling Game Worlds by Transition Complexity
arXiv:2608.18079v1 Announce Type: new Abstract: Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the underlying transition prediction problem is at the declared interface (pixels/tokens/latents with finite history). We propose the Transition Complexity Profile (TCP): a small, reproducible set of metrics that characterizes an environment's (or gameplay dataset's) induced transition kernel by (i) intrinsic one-step branching, (ii) interaction-induced uncertainty and opponent influence when observable, and (iii) temporal/spatial dependency span via standardized probe curves. TCP is reported with an explicit reference distribution, protocol stochasticity, and a versioned measurement budget (sampling/resampling and fixed probe compute), enabling comparable numbers across benchmarks. We outline how common game families and modern "neural game engine" domains populate this landscape and call for TCP to become
Ilya’s 30u30 v21: Neural Turing Machines
What if a neural network had a little database it could read from and write to?Continue reading on Ilya Sutskever’s 30 Foundational Papers of AI, Explained »
Google AI Overviews SEO: Why Keyword Chasing Will Ruin Your Traffic
Are you still optimizing for blue links? (Bad news )Continue reading on DataDrivenInvestor »
When xAgent Connects to Databases, AI Can Work Directly with Business Data
Databases were one of the missing pieces in xAgent.Continue reading on Medium »
AI Steals from Other Artists. But What Do Great Artists Do If Not Steal?
Good artists copy, great artists steal . Why is it different when AI does it?Continue reading on Medium »
CIA funding helped keep NeXT afloat in the 80s
Comments
There’s a 20% AI Tax Coming. Nobody’s Told Your CFO Yet.
This week, buried in a story most engineering teams skimmed and most finance teams missed entirely, OpenAI told The Register that its Continue reading on Level Up Coding »
Is It Unethical For Writers To Use AI-Generated Images?
I think a use case exists. But even at its most acceptable, it s still morally hairyContinue reading on Thought Thinkers »
TrueFoundry's open source AI agent harness TrueForge boasts 30%-75% cheaper task completion than Claude Managed Agents
Another day, another new AI agent harness is released.Only this time, it s one that aims to solve a growing enterprise problem as AI agents proliferate: enabling greater developer control of agents and tools, while reducing cost. TrueFoundry, a San Francisco B2B machine learning startup co-founded in 2021 by former Meta and Google engineers, has released its own custom TrueForge harness under the permissive MIT License on Github. Thus, it can be used with any of a developer (or their parent enterprise s) preferred AI models, forked, modified, self-hosted and incorporated into commercial products. The company states in a blog post that when it used TrueForge paired with the open source GLM-5.2 LLM to successfully complete 11 of 14 tasks on DevRev’s Enterprise-Bench — testing multi-step tool use across CRM, issue tracking, and document management systems — it cost 75% less than achieving the same results with Anthropic s Claude Managed Agents harness powered by Claude Opus 4.8 ($2.90 com