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Travel Time Prediction in Supply Chain Management Using Machine Learning
arXiv:2609.38190v1 Announce Type: new Abstract: The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. The supply chain ecosystem is very complex and heavily relies on the transportation and logistics of raw materials and finished goods. Accurate travel time estimation is critical because it helps supply chain members to improve logistics consistency and performance. This helps in planning, demand forecasting, lead time management and assembly planning. The logistics on the delivery side of the customer also plays a crucial role in customer satisfaction and voice of customer. With the collection of huge historical data and using novel techniques, the research builds an accurate model to predict travel time of inventory.
EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents
arXiv:2609.38193v1 Announce Type: new Abstract: Patient world models and clinical agents aim to predict changes in patients' health and support clinical work. Developing these systems requires reliable histories of patient conditions, treatments, and the information available at each decision. Electronic health records (EHRs) contain these histories, but differences in how events are recorded make them difficult to use consistently. We present EHR2Trace, a system that converts EHRs from different sources into traceable patient events for model training and evaluation. It links events to source records, separates event time from information availability, and distinguishes medication orders, dispensing, and administration. A shared event representation supports both OMOP and MEDS exports, with automated validation and reproducible builds. Across three clinical datasets, EHR2Trace converted 846.4 million events, with every applicable check passing except one unit-consistency check on MIMI
AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks
arXiv:2609.38288v1 Announce Type: new Abstract: We present AREX-2, an effort to advance the self-improving capability of LLM agents, which we define as the ability to iteratively refine a solution at test time. This ability rests on two complementary capabilities: reflection, which produces a solution better than the current one, and long-horizon execution, which keeps the iteration effective over many rounds. We hypothesize that both capabilities are domain-agnostic, and can therefore be learned in scenarios that are well suited for supervision. Accordingly, we synthesize long-horizon improvement trajectories from machine learning and algorithmic programming tasks, two domains that offer verifiable feedback and reward sustained iteration. Trained on this data, our agent, built on Qwen3.8-27B, achieves strong results on MLE-bench Lite (81.8) and Frontier-CS (70.7), transfers to deep research with 84.0 on BrowseComp, 52.6 on HLE, 92.2 on GAIA, and 93.8 on DeepSearchQA, and keeps improvi
Fiatlux: A Long-Horizon Benchmark for Humanoid Ladder Climbing and Light-Bulb Replacement
arXiv:2609.38216v1 Announce Type: new Abstract: Existing benchmarks evaluate tabletop manipulation, flat-floor household activity, or humanoid locomotion and manipulation as separate task groups; none scores vertical mobility and dexterous work on a fragile payload in one long-horizon episode. We present Fiatlux, a light-bulb replacement benchmark built on NVIDIA Isaac Lab. In one episode, a Unitree G1 humanoid positions a step ladder under a ceiling or wall fixture, climbs it, exchanges a spent bulb in a socket for a fresh one, and leaves the spent one in a disposal crate. We decompose the episode into twelve subtask environments scored on difficulty-weighted gates. The goal is a successful replacement, with the fresh bulb seated, the spent one disposed of, neither dropped, and a fragility bound not crossed. Runs that fall short can earn partial credit. Observations are split into a standard mode (signals a physical robot could sense or estimate) and a privileged mode (exact simulator
Build a RAG API with FastAPI
OverviewContinue reading on Medium »
12 Best AI Books 2026 Worth Reading If You Actually Build Things
A developer s AI bookshelf looks very different in 2026.Continue reading on Towards AI »
Skill-Based Agentic QA: Automated, Governed Evaluation for AI Skill Outputs
TLDRContinue reading on Medium »
How to get OpenAI Dots on Claude
OpenAI s new always-on agents have their own computer, run on schedules, and react to events. Claude has most of those pieces under a Continue reading on Generative AI »
Elon Musk’s Grokipedia has a ‘newly refreshed’ design
Grokipedia, SpaceXAI's AI-powered competitor to Wikipedia, recently started incorporating edits again, and today, it got some design tweaks as part of a v0.3 update, including a new logo and refreshes to its homepage and live edits page. SpaceXAI head of design Benji Taylor calls it a "newly refreshed Grokipedia." Grokipedia's old homepage was pretty much just a logo and a search bar, but the updated homepage includes a list of featured articles, a list of the most-read articles, and a tracker showing the "latest edits." The new featured list showcases digital representations of books that you can spin around, and the most read section also … Read the full story at The Verge.
Gemini Weighs in on the OpenAI “escape” from AI AND THE FLIPSIDE: Artificial Intelligence Pictures…
As I was writing this book, one LLM suggested I note in the introduction, In six months this chapter will be outdated. Continue reading on Medium »
The Hidden Cost of Keeping Up with AI: What Families Need to Understand About Today’s IT Leaders…
For decades, technology moved at a pace that allowed professionals to adapt gradually. New programming languages emerged, new frameworks Continue reading on Medium »
Google releases Gemini 4 Argon, called its most powerful model yet
Google has released its latest Gemini model, marketing it as a workhorse for coding and cybersecurity work.