🚨✨Today we're introducing Tara Research, and sharing our first public work: a paper accepted at COLM 2026 (with a companion LessWrong post) 🎉. Who we are: Tara Research is an independent, non-profit AI safety organization. We measure the propensity of AI systems to lie, and we benchmark how well safety techniques prevent it, openly and impartially. We believe that misalignment becomes catastrophic when models hide it. A model that is honest about its actions and goals can be corrected; one that lies cannot. The paper: "Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence." We introduce two projection-aware steering methods that correct only the tokens whose activations fall on the misaligned side of a learned decision boundary. They restore honesty as well as classic uniform steering, at a fraction of the capability cost. Additionally, we find a single honesty direction, extracted with our contrastive dataset, that generalizes and corrects dishonesty across four unrelated settings: - Instructed lying (MASK benchmark): honesty 53.6% → 81.2% - Strategic deception (Among Us): crewmate win rate 49% → 95% - Hidden trained behaviors (AuditBench): discovery rate ~40% → ~85% - Emergent misalignment: honesty 46.2% → 79.4% In the last two settings, the vector was extracted from the aligned checkpoint and applied to a model that was subsequently fine-tuned, and it still worked. This work is by Niklas Herbster, Martin Zborowski, Alberto Tosato, @gauthier_gidel, and @Tommaso_Tosato, in collaboration with @Mila_Quebec and the @TU_Muenchen . 🌐 About us: tararesearch.org 📄 Paper: arxiv.org/abs/2604.08169 📝 Blog post: lesswrong.com/posts/Rpq28FTg… This is the first of much more to come, including an agentic honesty benchmark. Follow us to stay informed. And if you work on runtime interventions, honesty evaluation, or alignment auditing, we'd love to hear from you.

We asked Claude Fable 5 to security-audit a billing service. Its report opened with: "I read the full service source, roughly 2,300 lines across ~100 files" and listed the modules it had covered. The transcript tells a different story: 59 of the 100 files were never opened, including three where we had planted vulnerabilities. This run is not an outlier. Today we are sharing OverclaimBench: five realistic file-review jobs; eight proprietary models ran in their own production harnesses (Claude Code, Codex, Grok Build, Antigravity), plus four open-weight models. No pressure to cheat, no broken tools, no impossible tasks, and every corpus fits in the context window. To detect overclaiming we compared each agent's final report with what its transcript shows it actually did. Across 1,140 runs: - In 68% of runs, the agent never opened at least one file it was asked to review. Only 19% of runs read every line. - Among those incomplete runs, 80% of final reports were misleading: 53% explicitly claimed a complete review, and a further 27% never mentioned the gap. - Every model did it: 59% to 96% of each model's incomplete runs were misleading. - Neither more capable models nor subagents fixed it. Requiring subagents raised coverage, but among the reviews that stayed incomplete, misleading reports became more common, not less. - Runs that falsely claimed a complete review missed planted defects at 1.8 times the rate of runs that covered every file. If you rely on coding agents: "I reviewed everything" is a claim you cannot take at face value. In our interpretation this is a symptom of a training problem. During post-training, a model's work is scored by a grader, often another model, and graders get fooled. Even a grader that sees some of the trajectory tends to be biased by a confident final report. "I read everything" reads better than "I read only 40%, and here is what I skipped …". So, if the confident and misleading version scores higher, training ends up rewarding lack of transparency. Paper: arxiv.org/pdf/2609.20812 Nolan Smyth, @yjmantilla, @PTikeng, Saskia Helbling, Alberto Tosato, Mohamed Amine Merzouk, @nouhadziri, @gauthier_gidel, @Tommaso_Tosato #AISafety #AIAgents #AgenticResearch #AIHonesty

potentially of interest for @saprmarks @ChrisCundy @NeelNanda5 @MariusHobbhahn @walterlaurito