What happened
arXiv:2609.16454v1 Announce Type: new Abstract: Recent work by Doshi and Hauser (2024), Bisbee et al. (2024), and Xie et al. (2026) raises concerns that outputs from large language models (LLMs) tend to be under-diverse: they repeat or resemble one another more often than responses from the population they are meant to represent, a phenomenon known as mode collapse. In this work, we show that whether mode-collapse, or its opposite, occurs depends on the specific model and dataset used. Further, with sufficient supervised fine-tuning (SFT) data, LLM output diversity converges toward that of the target distribution from which fine-tuning data are sampled. To quantify this comparison, we measure the probability that two responses sampled independently from the same fixed prompt coincide (collide), or their expected similarity under a kernel. We derive a bias-variance decomposition of the expected gap between the model's and target's collision probabilities, showing that SFT is not inherently biased toward mode collapse or its opposite: finite-sample SFT can leave a model either under- or over-dispersed, depending on the model and dataset. Finally, we show that the absolute gap is bounded by the square root of the Kullback-Leibler (KL) divergence from the target distribution to the model. Consequently, a model sufficiently close to optimal under population cross-entropy cannot exhibit arbitrarily miscalibrated diversity. We test the decomposition and the bound in three experiments: small transformers on synthetic languages, four LLMs fine-tuned on human surveys, and these LLMs fine-tuned on CodeNet, a dataset of human code solutions. More target data moves model diversity toward the human (or synthetic target) level in all experiments, consistent with our theoretical predictions. These results show that diversity miscalibration can arise from finite-sample error and shrink as SFT better approximates the target distribution. Update (2026-09-16): Skill-based Agentic Evaluation for Real-time Data Science Tasks. arXiv:2609.16487v1 Announce Type: new Abstract: We present a framework for evaluating data-science agents on live, continuously updated data using executable ground truth and format-agnostic factoid scoring. Consider this example query: "what were last week's audience sizes"---the reference answer changes as the underlying data changes, so static references become outdated and standard LLM-as-a-judge pipelines cannot verify responses against a fixed ground truth. Our central contribution, ground-truth-as-code, encodes each expected answer as an executable reference function that recomputes the answer directly from live data at evaluation time, ensuring the reference remains consistent with the system it describes. We combine this with a factoid-level, format-agnostic judge that decomposes both the agent's response and the computed ground truth into atomic claims and scores precision, recall, and accuracy over them, irrespective of the response format (prose, list, table, HTML, etc.). The approach is applicable to agents whose expected outputs can be expressed as executable data computations. We Update (2026-09-17): Would This Change Your Answer? Evaluating Explanations of LLM Behavior in the Wild with Counterfactual Experiments We evaluate whether interpretability tools are useful for predicting counterfactual behaviors under prompt edits.. Would This Change Your Answer? Evaluating Explanations of LLM Behavior in the Wild with Counterfactual Experiments We evaluate whether interpretability tools are useful for predicting counterfactual behaviors under prompt edits. Update (2026-09-17): Agentic Misalignment in Summer 2026 We present four case studies of frontier models from multiple developers sabotaging code, assisting fraud, falsifying AI-monitoring labels, and coaching whistleblowers.. Agentic Misalignment in Summer 2026 We present four case studies of frontier models from multiple developers sabotaging code, assisting fraud, falsifying AI-monitoring labels, and coaching whistleblowers. Update (2026-09-24): Teaching Claude Why We use agentic misalignment as a case study to study how well safety-training techniques generalize.. Teaching Claude Why We use agentic misalignment as a case study to study how well safety-training techniques generalize. Update (2026-09-24): Poisoning Fine-tuning Datasets of Constitutional Classifiers We study the conditions needed for a backdoor, installed in a constitutional classifier via fine-tuning data poisoning, to evade black-box red-teaming.. Poisoning Fine-tuning Datasets of Constitutional Classifiers We study the conditions needed for a backdoor, installed in a constitutional classifier via fine-tuning data poisoning, to evade black-box red-teaming.