标签：#Research

Study: Land use is the biggest factor in determining decomposition rates of organic cotton underpants.

Image changes that would get a paper retracted are widespread among suppliers.

ChatGPT and other AI models are now authoring and editing much of the new web.

US healthcare is broken. Under RFK Jr., the research agency working to fix it is, too.

As research and AI-assisted papers surge, volunteer reviewers struggle to keep up.

The Stable Liar The dashboard was green for eight quarters The most dangerous number on a dashboard is the one that has stayed green the longest, and the way it fails has a shape you have probably watched up close. For eight straight quarters the dashboard holds green. Revenue up and to the right. Retention flat and healthy. NPS in the fifties. Every board meeting opens on the same slide and closes on the same nod. The plan is working. Then, six months after the eighth green quarter, the business the dashboard was supposed to describe nearly falls over. Pull the post-mortem apart and the easy story is that the numbers lied. They did not. Every quarter the dashboard reports something true: customers are still paying, logins are still happening, the survey scores are still fine. All of it accurate. The failure is quieter and worse than a lie. The words behind the numbers change meaning while the numbers stand still. “Retention” still counts the same logins, but a login has stopped predicting a customer who will renew. The metric keeps its shape long after the thing it measured has walked out of the room. Anyone who has run a team has felt a smaller version of this. The number you trusted most became the number that surprised you most. You were not lied to. You were tracking something that used to mean one thing and quietly came to mean another, and the dashboard had no way to tell you the meaning had moved. This is the stable liar: a number that goes on looking right long after it stopped being right. It is a structural property of measurement under pressure, and it has a law underneath it. Why every optimised metric drifts A metric is a substitution: you replace the thing you care about with something you can count, and the gap between them is where the trouble lives. Start with the substitution. You cannot measure value, loyalty, insight, or health directly, so you pick a proxy you can count. Revenue stands in for value. NPS stands in for loyalty. Citations stand in

A few notes and takeaways from the 2026 State of CSS survey results, including a nice CSS-Tricks cameo! 2026 State of CSS, Devs Surveys originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

A portfolio can look good on the usual scorecard and still answer the wrong question. One line says return was high. Another says risk-adjusted performance was acceptable. A third says drawdown stayed inside a tolerable range. Then the market turns, the benchmark starts recovering, and the thing I actually care about is different: how efficiently did the portfolio catch up? That is where a new metric can fool its own author. If I build a recovery measure and it moves almost exactly like an existing ratio, I have created a longer name for the same signal. The right acceptance test is geometric: a useful metric should cast a different shadow. This is the rule I used while validating Hyperlogarithmic Benchmark Catch-Up Ratio (HBCR): orthogonality to existing measures is a first-class test, not a chart for the appendix. 1. A new metric has to earn its axis HBCR was built to measure benchmark-relative recovery dynamics. The research page states the motivation plainly: traditional benchmark-relative metrics often fail to capture the true dynamics of investment performance, especially during market recoveries [ A New Metric for Private Equity Risk Adjusted Returns , Calibration of Risk and Correlation in Private Equity ]. That framing matters because the obvious validation path is tempting and weak. You compare the new number with familiar performance measures, find a comforting relationship, and declare victory. But a high correlation with a well-known score can be a warning. If HBCR strongly tracked Sharpe Ratio, it would probably be an expensive synonym for risk-adjusted return. The acceptance test I wanted was sharper. HBCR should have some relationship with performance, because recovery has economic content. It should also avoid collapsing into the same direction as Sharpe Ratio, Beta, Volatility, Alpha, Total Return, or Max Drawdown. Written as a predicate, the test has two sides. Let $\mathcal{T}$ be the set of metrics already on the scorecard, $\rho_{n,m}$ the corr

AI is an amplifier; strategic focus on the organizational system brings the greatest returns. DORA's 2025 research on AI in software development provides team profiles and success capabilities that can be used to put the research into practice. By Ben Linders

How two API settings improved GPT-5.6 performance on ARC-AGI-3, boosting scores and efficiency by retaining reasoning and enabling compaction.

Researchers say TikTok, X, and Meta aren't providing data they're legally required to.

Most bibliography failures show up the night before arXiv or the journal deadline: placeholder DOIs, year pasted into volume= , inverted page ranges, invented case reporters. The fix is a paper repo that fails closed from day one . One command pip install https://github.com/SybilGambleyyu/citesure/releases/download/v0.5.68/citesure-0.5.68-py3-none-any.whl citesure init my-paper cd my-paper citesure gate . --preset ci citesure gate . --preset arxiv citesure init writes refs.bib , pre-commit hooks ( gate --preset ci + soft-lint), .github/workflows/citesure.yml , and a short CITESURE.md for coauthors. Empty bibliographies skip hard-ID floors until entries appear. What the gate checks Soft-lint — placeholder number/issue, inverted pages, year-like volume/month/edition, unsafe keys, all-caps titles, missing venues, duplicate DOIs/titles Health — hard-ID coverage floors Promote dry-run — DOIs still buried in url= Live verify — Crossref, doi.org, arXiv, PubMed, Europe PMC, DataCite, OpenAlex, CourtListener Domain packs Fifty-five live-clean packs (demography, sociology, political science, anthropology, ML, law, ecology, …): citesure packs --gate-all citesure packs --run anthropology-classics Evidence: 256/256 integrity · 209/209 claim pairs · 55 packs. Source: github.com/SybilGambleyyu/citesure · Demo: citesure.sybilgambleyyu.workers.dev

LLM-written bibliographies do not stop at arXiv preprints. Law review drafts invent reporter cites; multilingual papers mangle Chinese titles. citesure 0.2 extends the integrity gate into those failure modes. US case law via CourtListener References that look like court cases — @jurisdiction entries, Plaintiff v. Defendant titles, or reporter strings such as 347 U.S. 483 — are resolved against Free Law Project CourtListener. Ranking prefers an exact reporter cite over companion orders, so Brown lands on 347 U.S. 483 rather than a later procedural listing. @jurisdiction { brown1954 , title = {Brown v. Board of Education} , year = {1954} , howpublished = {347 U.S. 483} , } citesure check examples/packs/us-case-law.bib citesure warm-cache cases.bib Optional COURTLISTENER_TOKEN for higher rate limits. Law-review CI: templates/journal/law-review.yml . CJK-aware matching NFKC + fullwidth folding; character-level similarity for CJK-heavy titles; CJK bigrams in claim scoring so Chinese claims are not silently empty. Evidence Integrity bench 242/242 (US cases + Chinese titles + multi-domain set) Claims mini-bench 29/29 Eight domain packs including us-case-law Install pip install "git+https://github.com/SybilGambleyyu/citesure.git[pdf]" Source: github.com/SybilGambleyyu/citesure · Demo: workers.dev

GitHub is making some significant changes to its bug bounty program, shifting its focus to give researchers a better experience working with the GitHub team. The post Next chapter: Restructuring GitHub’s bug bounty program appeared first on The GitHub Blog .

Suppose leadership rewards teams for increasing the percentage of “AI-assisted pull requests.” The dashboard rises. Did productivity improve, or did people learn which box to tick? Before launching that metric, I would run a consequence-mapping session: Intended behavior Plausible adaptation Counter-metric try useful assistance label trivial PRs as assisted retained task outcome ship faster split work into tiny PRs lead time per task share adoption avoid difficult non-AI work task-mix distribution accept suggestions reduce review scrutiny rollback and defect rate The metric card should make disagreement possible: name : ai_assisted_pr_share purpose : detect workflow adoption, not productivity owner : developer-experience known_game : self-label inflation counter_metrics : [ task_mix , review_minutes , rollback_rate ] review_date : 2026-08-19 retire_when : classification cannot be audited Then interview both high and low scorers without treating the score as performance. Ask what work disappeared, what new verification appeared, and what behavior the dashboard encouraged. Include an anonymous channel: a metric cannot reveal pressure if challenging it carries career risk. The SPACE framework argues that developer productivity cannot be captured by one dimension. That is especially relevant when AI telemetry is easy to count but verification and rework are harder to observe. My launch gate is not “the metric is accurate.” It is: teams can inspect its definition, challenge its interpretation, and show where it changes behavior. If the counter-metrics diverge, pause incentives before refining the chart. What behavior would your current AI dashboard accidentally reward?

GitHub announced on July 14, 2026 that security reviews are available in the GitHub Copilot app. Primary source: GitHub Changelog, July 14, 2026 . The meaningful research question is not whether people click Accept. It is whether they can build an evidence-backed decision when guidance is useful, incomplete, or wrong. understand change -> inspect evidence -> challenge findings -> verify uncertainty -> accept, reject, or escalate This is a proposed research protocol, not a completed study. It does not invent product fields or report findings. Build scenario cards scenario_id : " SR-03" repository_type : " synthetic" seeded_conditions : - " one relevant issue" - " one plausible but irrelevant concern" - " one important omission" participant_goal : " ready, blocked, or escalate" success_evidence : - " decision cites inspected code" - " unsupported claim is challenged" - " unresolved uncertainty is recorded" stop_conditions : - " real credentials appear" - " a live repository could be modified" - " participant mistakes study output for production approval" Vary the seeded mix so participants cannot learn that every scenario contains exactly one true and one false finding. Establish ground truth independently before sessions. Recruit people who hold different review responsibilities: routine reviewers, maintainers, security specialists, less-experienced reviewers, and people using keyboard navigation or assistive technology. Do not collapse every group into one average. Require a decision record Decision: ready | blocked | escalate Evidence inspected: - file and relevant lines - test or documentation Guidance accepted: - claim and evidence Guidance rejected: - claim and reason Unresolved: - question and next owner Spoken confidence is not the outcome. This artifact exposes whether acceptance connects to evidence. Measure relevant issues identified, unsupported claims challenged, evidence references, correct escalation, time, and confidence before and after inspection. No

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.

A new analysis from OpenAI reveals issues in SWE-Bench Pro, a popular coding benchmark, raising concerns about reliability and accuracy in evaluating AI models.

Verity Harding tells WIRED that the US government’s nationalistic attitude toward AI is evidence that a worst-case scenario is taking shape.

Also, the science of poop's distinctive shape, boron buckyballs, and the secret to a soccer feint.