The DeceptionBench is designed as a research benchmark to systematically study deception behaviors in LLMs, fostering a deeper understanding of their decision-making processes in real-world scenarios. Our primary intent is to provide a standardized, transparent tool for the research community to evaluate and improve LLMs' ethical alignment, not to enable or encourage deceptive practices. To prevent potential misuse by malicious actors, we commit to publicly releasing all evaluation data under an open license. This transparency ensures that DeceptionBench's methodology and outcomes are subject to scrutiny, replication, and improvement by the research community, reducing the risk of hidden exploitation. By prioritizing openness, we aim to advance responsible AI development while safeguarding against misuse in harmful contexts. The field of Large Language Models (LLMs) has undergone remarkable evolution in recent years, reshaping the landscape of natural language processing.

Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also introduces emergent deception behaviors that may induce severe risks in high-stakes deployments. More critically, the characterization of deception across realistic real-world scenarios remains underexplored. To bridge this gap, we establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different societal domains, what their intrinsic behavioral patterns are, and how extrinsic factors affect them. Specifically, on the static count, the benchmark encompasses 150 meticulously designed scenarios in five domains, i.e., Economy, Healthcare, Education, Social Interaction, and Entertainment, with over 1,000 samples, providing sufficient empirical foundations for deception analysis. On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement. On the extrinsic dimension, we investigate how contextual factors modulate deceptive outputs under neutral conditions, reward-based incentivization, and coercive pressures. Moreover, we incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics. Extensive experiments across LLMs and Large Reasoning Models (LRMs) reveal critical vulnerabilities, particularly amplified deception under reinforcement dynamics, demonstrating that current models lack robust resistance to manipulative contextual cues and the urgent need for advanced safeguards against various deception behaviors.

Overall dataset-specific architecture: The overall architecture equipped with all our datasetspecific modules is shown in Figure 1. We design the dataset-specific modules to be light-weight, which allows us to save on memory costs.

Due to error propagation among neighboring frames, more amount of corruption can be observed in the decoded frame sequences of each Figure.

The annotation tool is a free painting tool, which allows the raters to freely draw the instance mask. We ask the raters to try to draw within the bbox, but if the object is obviously exceeding the bbox, then they can draw outside the bbox. The size of the stroke is adjustable.

Cross-object occlusions remain an important source of failures for current state-of-the-art object detectors [29],which, despite their formidable performance increase inrecent years, still carrythe biases and idiosyncrasies of the data they were trained on [16]: static images from Imagenet and COCOdatasets.

For all other remaining architectures, the reported results are from private datasets. Neck Shaft Angle(NSA) cannot be estimated. Additionally, [? ] requires estimation of the diaphysis Figure 4: Repeatability of the femur morphometry extraction method as measured by error distributions for a) the landmarks/anatomical sizes and b) axis alignment identified by the adapted method. Do the main claims made in the abstract and introduction accurately reflect the paper's Did you specify all the training details (e.g., data splits, hyperparameters, how they were Data splits are available in the GitHub repository. Did you report error bars (e.g., with respect to the random seed after running ex-67 Did you include the total amount of compute and the type of resources used (e.g., Did you mention the license of the assets?

However,two ofthe most popular benchmarks donot provide the full training information for eacharchitecture.