LLM-based forecasting systems have improved on real-world tasks like financial markets and sports outcomes, largely by strengthening search, but the final prediction step remains crude: systems typically ask an LLM to read all collected evidence and emit one answer. We call this approach Monolithic Prediction, which fits forecasting poorly: probabilities over competing outcomes carry information valuable for downstream decisions, but a holistic answer collapses the supporting evidence, discarding this information. To address the above issue, we propose LEAP Likelihood Elicitation and Aggregation for Probabilistic forecasting) to keep the LLM out of the final prediction. LEAP treats evidence items as local signals, uses likelihood parameters to estimate how each supports candidate outcomes, and aggregates them through a deterministic probabilistic model into a posterior over candidate outcomes, thereby preserving per-item contributions, yielding comprehensive forecasts, and simplifying audits. We build a benchmark covering real-world prediction, information-seeking, and browsing tasks, evaluating LEAP on our custom agent loop and practical agent CLI frameworks. Given the same evidence, LEAP outperforms Monolithic Prediction in most settings and metrics across models.

LLM-based multi-agent systems (MAS) are increasingly considered for high-stakes decision-making, yet outcome-based fairness audits can miss where risks arise within the decision trajectory. We present SCOPED-Hiring, a process-aware fairness diagnosis pipeline for LLM-based hiring MAS. SCOPED-Hiring constructs controlled resume variants, runs role-based hiring committees, logs over 311K structured decision trajectories, and converts trajectory fields into quantitative fairness signals organized by six diagnostic lenses: final outcome, counterfactual, process, pathway, dynamic, and design effects. SCOPED-Hiring reveals that balanced final hire rates can mask hidden trajectory unfairness in multi-agent decision trajectories: career gaps trigger suspicion, proxy cues shape qualification judgments, and identity cues lead to unequal investigation. Targeted repair guided by these diagnoses reduces total layered burden by 72.3% while shifting the hire rate by only 1.86 pp, showing that process diagnosis can guide effective repair.

Text-to-image (T2I) models excel at generating high-quality images from text via powerful text encoders but training these encoders demands substantial computational resources. Consequently, many users seek pre-trained text encoders from model plugin-sharing platforms like Civitai and Hugging Face, which introduces an underexplored threat: the potential for adversaries to embed Trojans within these plugins. Existing Trojan attacks often require extensive training data and suffer from poor generalization across different triggers, limiting their effectiveness and scalability. To the best of our knowledge, this paper introduces the first Text-encoder Weight-editing method for Inserting Secret Trojans (TWIST). By identifying the bottleneck MLP layer—the critical point where minimal edits can dominantly control cross-modal alignment—TWIST achieves training-free and data-free Trojan insertion, which makes it highly efficient and practical. The experimental results across various triggers demonstrate that TWIST attains an average attack success rate of 91%, a 78% improvement over the state-of-the-art (SOTA) method proposed in 2024 and highlights the excellent generalization capability. Moreover, TWIST reduces modified parameters by 8-fold and cuts injection time to 25 seconds. Our findings underscore the security risks associated with text encoders in real-world applications and emphasize the need for more robust defense mechanisms.