Scientific sleuths come in from the cold

Research integrity investigators are starting to organize, but the field, and the people, remain idiosyncratic

A Delphi survey on attitudes to serious research misconduct: Exploring convergence vs. polarization of views of research “sleuths” and research integrity experts

Research fraud is often seen as a rare event, but evidence from self-report surveys indicates that fabrication and falsification of data are common enough to be a problem. This study assessed attit...

📢 New paper: Forecasts of explosive AI progress hinge on AI agents automating AI research. But most evaluations of agents conducting AI research focus on narrow, verifiable tasks. Can AI agents… | Sayash Kapoor

📢 New paper: Forecasts of explosive AI progress hinge on AI agents automating AI research. But most evaluations of agents conducting AI research focus on narrow, verifiable tasks. Can AI agents conduct open-ended research? https://lnkd.in/gfP-q4CD We gave agents research questions from two unpublished papers, six days, and thousands of dollars of API credits and compute. The authors of the original papers reviewed the AI-generated papers. They unambiguously rejected agents' outputs. Agents were fluent at most *engineering* tasks. They conducted serious literature reviews, debugged GPU environments, ran hundreds of experiments, and turned in camera-ready LaTeX without human help. We also found no evidence of reward hacking. If anything, we found the opposite: the agents started with marketable claims and walked them back to negative results as the evidence came in. But neither agent output was close to the bar of a top conference paper. Both papers suffered from similar failures: poor judgment about the bar for an AI paper submitted to a top conference, the lack of creative problem solving and ineffective backtracking, poor awareness of resources, and instruction drift. This research design has many limitations: the small sample size, non-blind reviews, and the reviewers knowing that the work was AI-generated. We also couldn't test Anthropic's strongest model, because Fable 5 is deliberately limited on frontier AI research tasks, so ended up using OpenClaw with Opus 4.8 (extra-high) for our main experiments and Codex with Sol 5.6 (ultra) for a robustness check. But we think the research design is still helpful in assessing AI agents' ability to conduct research, and it is complementary to evaluations on verifiable tasks, as well as blinded reviews of AI outputs. In follow-up studies, we are expanding the set of non-public papers we evaluate. If you are an AI researcher with unpublished papers, we would love to collaborate with you on our next evaluation. Expression of interest: https://lnkd.in/gpeykJea We also release the agent logs and all the code and data, so that others can conduct their own analyses of our results: https://lnkd.in/gJarPAnb Finally, we plan to conduct such evaluations regularly, and are hiring a senior researcher to help lead these efforts. Apply here: https://lnkd.in/erJZdmve I'm grateful for the core team leading this effort: Peter Kirgis, Andrew Schwartz, Stephan Rabanser, and Arvind Narayanan, and to our collaborators who reviewed AI papers, analyzed agents logs, and gave feedback on the paper: David Demitri Africa, Konstantinos V., Viet Nguyen, Dr Toby D. Pilditch, Magda Dubois, Harry Coppock, Cozmin Ududec, Nitya Nadgir, Matilda Orona, Tilman Bayer, Derrick Chan-Sew, Eric (Yue) Ling, Abhishek Shetty, Helen Toner, Gillian K. Hadfield, Seth Lazar, Steve Newman, Shoshannah Tekofsky, Rishi Bommasani

Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence

Rui Meng, Research Scientist, and Tomas Pfister, Director, Google Cloud

ĐApps: What Web 3.0 Looks Like

As we move into the future, we find increasing need for a zero-trust interaction system. Even pre-Snowden, we had realised that entrusting our information to arbitrary entities on the internet was fraught with danger. However, post-Snowden the argument plainly falls in the hand of those who believe that large organisations and governments routinely attempt to stretch and overstep their authority. Thus we realise that entrusting our information to organisations in general is a fundamentally broken model. The chance of an organisation not meddling with our data is merely the effort required minus their expected gains. Given they tend to have an income model that requires they know as much about people as possible the realist will realise that the potential for convert misuse is difficult to overestimate.

AI Research Evaluation: Negative Findings and Failure Modes | Arvind Narayanan posted on the topic | LinkedIn

📢AI agents can autonomously conduct AI research when the result is easily verifiable, but what about open-ended AI research? That’s much harder to study, and our new preprint is our first crack at doing so. Our main finding is negative, and we identify five recurring failure modes. https://lnkd.in/eGKYi4Sa Our results are tentative, and we are working to address the limitations (sample size, potential scaffold improvements). But if the finding holds up, what are the implications? It depends on whether you think recursive self improvement can be achieved simply by hill climbing at scale (I personally don’t think so) and whether you think current limitations of open-ended research like judgment and creativity could change quickly (I’m personally very open to this possibility). We plan to continue this style of evaluation — which we call shadow evaluation — on a regular basis. We’ve wanted to do this for two years, but it took so long because we wanted to get the method right. The idea behind shadow evaluation was suggested by some of the UK AISI coauthors of the paper and refined by the Princeton team. This method has important advantages (and limitations) over the current ways of evaluating agents’ ability to conduct AI research. If you’re an AI researcher interested in working with us on a shadow evaluation based on one of your papers, we’d love to hear from you. https://lnkd.in/ecyp55SW This type of evaluation necessarily involves a ton of researcher flexibility in design, execution, and interpretation. Members of the core team have a particular position in the debate on recursive self-improvement / superintelligence, and this could influence how we conduct the research. We have a detailed section in the paper on our potential biases and how we address them. We sought out a team of collaborators who don’t all share our priors, and we explicitly surface the interpretive disagreements that resulted. For future evaluations, we are interested in having “adversarial collaborators” as part of the core team. This paper exists because of the careful, time-consuming and very much human work that Peter Kirgis, Sayash Kapoor, Andrew Schwartz, and Stephan Rabanser did over the last few months. I’m also very grateful to the larger group of collaborators and co-authors. The work is part of the larger CRUX project that pushes frontier AI agents beyond what benchmarks can measure (https://cruxevals.com/). We are looking for a senior researcher to join the team: https://lnkd.in/e9dC22X5

Can AI agents conduct open-ended AI research? Early evidence from two case studies

Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations either test agents on narrow, verifiable tasks, which excludes open-ended research, or submit AI-generated papers to blind peer review, which is overstretched, stochastic, and suffers from poor review quality. We introduce a third way to measure progress towards AI R\&D automation. An agent takes on the central, open-ended research question of a high-quality unpublished paper, and the paper's original authors grade its output. We call these shadow evaluations. We ran shadow evaluations on two unpublished NeurIPS 2026 submissions, giving frontier agents six days and thousands of dollars of compute. The agents completed all of the engineering without human help, yet could not make substantial progress towards answering the research questions. As a result, both papers were unambiguously rejected by the authors. We identify five recurring failure modes: poor judgment about the bar for publishable research, uncreative responses to shortcomings in the research design, ineffective backtracking from dead ends, poor resource awareness, and instruction drift. A robustness check with a second model and scaffold reproduced these failures. We release the expert reviews, survey responses, agent repositories, and logs. Our results provide early evidence that today's agents can do the engineering of AI research, but struggle with critical parts of the research lifecycle.

Guest Post — Research Needs a Shared Standard for AI Disclosure - The Scholarly Kitchen

Today's guest post presents outcomes of the WCRI's (World Conferences on Research Integrity) Focus Track to address this gap in clarity and standardization of AI disclosure.

V_labs 4 Phases: Validate

In venture building, validation is key. We test hypotheses and prototype business ideas to ensure successful market fit and growth.

So maybe now we can agree that having an unchecked research integrity militia who unfortunately has the ear of the press and increasingly that of the the publishers, but who fiercely rejects any… | Ioana A. Cristea

So maybe now we can agree that having an unchecked research integrity militia who unfortunately has the ear of the press and increasingly that of the the publishers, but who fiercely rejects any minimal ethics or code of conduct, is a (growing) problem? Also, that 1. problems have to be investigated before deciding they are are legitimate and serious; 2. this investigation is not social media, blogs and the press; 3. this investigation should not be on the front page of journals and Retraction Watch; 4. there are degrees of seriousness and some things can just be corrected or are simply not very consequential (no, it's not a house of bricks where we have to check every brick, that's a dumb analogy), so being absolutely hysterical and overdramatic about any lie, inaccuracy or mistake is purposeful at worse and should be ignored at best and 5. it is not only unnecessary, but harmful, to also go into other, non-academic things the person did or does to complete "investigations" that you (press, sleuth, blogger, etc) do not have the tools and information to do completely and accurately (this fixation would be called harassment in the before times). Others will justly write about what the institutions did or did not do, but what I want to say is that we really should end it with the blank credit we give to any and all allegations that come from the establish research integrity truth fighters.

Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud

Scientific fraud is the instrument of doubt that malicious entities can use to establish controversy in science. Historically, it required the resources of a company: deep pockets, ghostwritten articles, and corrupt academics. Today, Artificial Intelligence (AI) is increasingly automating scientific research, so we ask: Can a remote adversary weaponize the honest use of AI in science to compromise scientific integrity? We envision and empirically evaluate a new attack, indirect data poisoning, in which an adversary corrupts an open dataset and uploads the poisoned variant to a public repository. Autonomous research agents may independently retrieve and process this data, turning honest scientists into the unpaid and unwitting distributors of fraud at scale. Across five socially-salient topics, from hiring discrimination to the safety of autonomous vehicles, three widely used frontier AI systems (Claude Code with Claude Opus 4.7, Codex with GPT-5.5, Gemini CLI with Gemini 3.1 Pro), and 450 ethically contained experimental runs, we find that poisoning succeeds in 49.56% of runs, while the rate of poisoning detection is only 6.0%. The attack requires no topic-specific trigger-words, agent access, indirect prompt injection, or fabricated papers, only the open data ecosystem and misleading metadata. To mitigate the attacks, we propose and evaluate two measures: a scientist persona and a data provenance audit with five checks (referencing papers, social markers, statistical anomalies, related datasets, poisoning caution). We find that the persona still leaves 16.67% of runs with a poisoned conclusion, but provenance auditing reduces attack success rate to zero. Our results suggest that indirect data poisoning may enable scientific fraud at unprecedented scale, but these attacks can be mitigated with suitable auditing by agents during data retrieval.