Large language models are increasingly being used as agents that interact with tools and environments to carry out complex tasks. This development has prompted a growing range of benchmarks and leaderboards for comparing agent performance. Yet the evidence behind these comparisons is not always fully disclosed. Even when every disclosed record is authentic and correctly scored, a reporter with access to the complete ledger can choose a favorable subset. We study whether rank claims remain informative under such adaptive release. An authenticated disclosure frame fixes systems, denominators, bundle bounds, and the canonical claim universe before selection. Worst-case completion keeps every reported edge sound, but a budgeted reporter can still choose which comparisons become certifiable. This separates the soundness of reported comparisons from neutrality in what gets reported. Across four public agent-evaluation ledgers, target-directed release produces positive attention gaps for most eligible targets at intermediate budgets; the effect depends on budget and bundle layout and disappears under an equal-per-system quota in one ledger. We derive an exact polynomial algorithm for the unit-cost target problem and prove weak NP-completeness under heterogeneous costs. These experiments are structural stress tests, not evidence that benchmark maintainers select evidence strategically. They show that certificate soundness does not imply disclosure neutrality.

Entity-fact unlearning in large language models (LLMs) aims to remove the binding between a target entity and a forgotten fact, producing a model that behaves like one retrained without the forgotten data while preserving neighboring knowledge and overall model utility. We frame this problem as distribution shaping: such a retrained model would yield a plausible distribution at forget queries, fluent, semantically appropriate, and reflective of residual knowledge, which we take as the appropriate target. Existing objectives provide only narrow token-level or semantically uninformative supervision, falling short of this target. We propose Counterfactual Self-Distillation (CSD), a self-distillation framework that approximates the retrained distribution by constructing a counterfactual teacher through type-consistent entity substitution. The resulting teacher is decoupled from the target identity while preserving type-level relational structure, enabling directed probability reallocation without an external oracle. Experiments on TOFU and RWKU show that CSD achieves a strong forget-utility trade-off, including the best overall forgetting on RWKU and strong performance on TOFU.

Eliminating toxicity from Large Language Models (LLMs) is crucial for ensuring user safety. However, current methods have limitations in the analysis and utilization of toxic samples, failing to fully harness their potential. Through comparative analysis of toxic and safe samples, we discover that toxic samples exhibit diversity and, within this diversity, there lies specificity. These findings suggest that leveraging these characteristics of toxic samples could enhance the performance of algorithms in detoxifying LLMs. To this end, we propose a novel diverse detoxification framework, DivDetox, which comprises two innovative components: a Multi-Category-Induced Personalized Sample Generation (MPSG) strategy and a Scaled Contrastive DPO (SC-DPO) approach. The former is designed to elicit a variety of personalized toxic responses from the LLM, while the latter is constructed to precisely and fully utilize these toxic responses. Experiments on benchmark datasets across different model scales and different detoxification tasks verify the effectiveness of our architecture.