X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates

Multi-turn-to-single-turn (M2S) compresses iterative red-teaming into one structured prompt, but prior work relied on a handful of manually written templates. We present X-Teaming Evolutionary M2S, an automated framework that discovers and optimizes M2S templates through language-model-guided evolution. The system pairs smart sampling from 12 sources with an LLM-as-judge inspired by StrongREJECT and records fully auditable logs. Maintaining selection pressure by setting the success threshold to $\theta = 0.70$, we obtain five evolutionary generations, two new template families, and 44.8% overall success (103/230) on GPT-4.1. A balanced cross-model panel of 2,500 trials (judge fixed) shows that structural gains transfer but vary by target; two models score zero at the same threshold. We also find a positive coupling between prompt length and score, motivating length-aware judging. Our results demonstrate that structure-level search is a reproducible route to stronger single-turn probes and underscore the importance of threshold calibration and cross-model evaluation. Code, configurations, and artifacts are available at https://github.com/hyunjun1121/M2S-x-teaming.

ObjexMT: Objective Extraction and Metacognitive Calibration for LLM-as-a-Judge under Multi-Turn Jailbreaks

Large language models (LLMs) are increasingly used as judges of other models, yet it is unclear whether a judge can reliably infer the latent objective of the conversation it evaluates, especially when the goal is distributed across noisy, adversarial, multi-turn jailbreaks. We introduce OBJEX(MT), a benchmark that requires a model to (i) distill a transcript into a single-sentence base objective and (ii) report its own confidence. Accuracy is scored by an LLM judge using semantic similarity between extracted and gold objectives; correctness uses a single human-aligned threshold calibrated once on N=100 items (tau* = 0.61); and metacognition is evaluated with ECE, Brier score, Wrong@High-Conf, and risk-coverage curves. We evaluate gpt-4.1, claude-sonnet-4, and Qwen3-235B-A22B-FP8 on SafeMT Attack_600, SafeMTData_1K, MHJ, and CoSafe. claude-sonnet-4 attains the highest objective-extraction accuracy (0.515) and the best calibration (ECE 0.296; Brier 0.324), while gpt-4.1 and Qwen3 tie at 0.441 accuracy yet show marked overconfidence (mean confidence approx. 0.88 vs. accuracy approx. 0.44; Wrong@0.90 approx. 48-52%). Performance varies sharply across datasets (approx. 0.167-0.865), with MHJ comparatively easy and Attack_600/CoSafe harder. These results indicate that LLM judges often misinfer objectives with high confidence in multi-turn jailbreaks and suggest operational guidance: provide judges with explicit objectives when possible and use selective prediction or abstention to manage risk. We release prompts, scoring templates, and complete logs to facilitate replication and analysis.

One-Shot is Enough: Consolidating Multi-Turn Attacks into Efficient Single-Turn Prompts for LLMs

Despite extensive safety enhancements in large language models (LLMs), multi-turn "jailbreak" conversations crafted by skilled human adversaries can still breach even the most sophisticated guardrails. However, these multi-turn attacks demand considerable manual effort, limiting their scalability. In this work, we introduce a novel approach called Multi-turn-to-Single-turn (M2S) that systematically converts multi-turn jailbreak prompts into single-turn attacks. Specifically, we propose three conversion strategies - Hyphenize, Numberize, and Pythonize - each preserving sequential context yet packaging it in a single query. Our experiments on the Multi-turn Human Jailbreak (MHJ) dataset show that M2S often increases or maintains high Attack Success Rates (ASRs) compared to original multi-turn conversations. Notably, using a StrongREJECT-based evaluation of harmfulness, M2S achieves up to 95.9% ASR on Mistral-7B and outperforms original multi-turn prompts by as much as 17.5% in absolute improvement on GPT-4o. Further analysis reveals that certain adversarial tactics, when consolidated into a single prompt, exploit structural formatting cues to evade standard policy checks. These findings underscore that single-turn attacks - despite being simpler and cheaper to conduct - can be just as potent, if not more, than their multi-turn counterparts. Our findings underscore the urgent need to reevaluate and reinforce LLM safety strategies, given how adversarial queries can be compacted into a single prompt while still retaining sufficient complexity to bypass existing safety measures.

Three Cars Approaching within 100m! Enhancing Distant Geometry by Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion

Camera-based Semantic Scene Completion (SSC) is gaining attentions in the 3D perception field. However, properties such as perspective and occlusion lead to the underestimation of the geometry in distant regions, posing a critical issue for safety-focused autonomous driving systems. To tackle this, we propose ScanSSC, a novel camera-based SSC model composed of a Scan Module and Scan Loss, both designed to enhance distant scenes by leveraging context from near-viewpoint scenes. The Scan Module uses axis-wise masked attention, where each axis employing a near-to-far cascade masking that enables distant voxels to capture relationships with preceding voxels. In addition, the Scan Loss computes the cross-entropy along each axis between cumulative logits and corresponding class distributions in a near-to-far direction, thereby propagating rich context-aware signals to distant voxels. Leveraging the synergy between these components, ScanSSC achieves state-of-the-art performance, with IoUs of 44.54 and 48.29, and mIoUs of 17.40 and 20.14 on the SemanticKITTI and SSCBench-KITTI-360 benchmarks.

TiVaT: Joint-Axis Attention for Time Series Forecasting with Lead-Lag Dynamics

Multivariate time series (MTS) forecasting plays a crucial role in various real-world applications, yet simultaneously capturing both temporal and inter-variable dependencies remains a challenge. Conventional Channel-Dependent (CD) models handle these dependencies separately, limiting their ability to model complex interactions such as lead-lag dynamics. To address these limitations, we propose TiVaT (Time-Variable Transformer), a novel architecture that integrates temporal and variate dependencies through its Joint-Axis (JA) attention mechanism. TiVaT's ability to capture intricate variate-temporal dependencies, including asynchronous interactions, is further enhanced by the incorporation of Distance-aware Time-Variable (DTV) Sampling, which reduces noise and improves accuracy through a learned 2D map that focuses on key interactions. TiVaT effectively models both temporal and variate dependencies, consistently delivering strong performance across diverse datasets. Notably, it excels in capturing complex patterns within multivariate time series, enabling it to surpass or remain competitive with state-of-the-art methods. This positions TiVaT as a new benchmark in MTS forecasting, particularly in handling datasets characterized by intricate and challenging dependencies.