Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning. To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating intermediates with nuanced differences in causal reasoning. We propose a suite of novel metrics -- intensity ranking concordance, cross-group position agreement, and intra-group clustering -- to evaluate LLMs on this front. Through extensive empirical studies on 21 high-profile LLMs, including GPT-4, Claude3, and LLaMA3-70B, we have favoring evidence that current models struggle to maintain epistemic consistency in identifying the polarity and intensity of intermediates in causal reasoning. Additionally, we explore the potential of using internal token probabilities as an auxiliary tool to maintain causal epistemic consistency. In summary, our study bridges a critical gap in AI research by investigating the self-consistency over fine-grained intermediates involved in causal reasoning.

Conditional dichotomy, the contrast between two outputs conditioned on the same context, is vital for applications such as debate, defeasible natural language inference, and causal reasoning.Existing methods that rely on semantic similarity often fail to capture the nuanced oppositional dynamics essential for these applications.Motivated by these limitations, we introduce a novel task, Conditional Dichotomy Quantification (ConDQ), which formalizes the direct measurement of conditional dichotomy and provides carefully constructed datasets covering debate, defeasible natural language inference, and causal reasoning scenarios.To address this task, we develop the Dichotomy-oriented Geometric Embedding (DoGE) framework, which leverages complex-valued embeddings and a dichotomous objective to model and quantify these oppositional relationships effectively.Extensive experiments validate the effectiveness and versatility of DoGE, demonstrating its potential in understanding and quantifying conditional dichotomy across diverse NLP applications.Our code and datasets are available at https://github.com/cui-shaobo/ conditional-dichotomy-quantification.

Collaborative robots often exhibit limited absolute accuracy despite high repeatability, necessitating cost-effective calibration solutions. This paper presents a novel sensor-free self-calibration method for collaborative robots using position and distance constraints. A tri-sphere end-effector with precision balls and magnetic holders enables repeatable Tool Center Point (TCP) positioning (<0.01mm) through hand-guiding, where the three-sphere configuration crucially enhances the orientation calibration accuracy compared to a single-sphere approach. The proposed device eliminates expensive external sensors while establishing geometric constraints through workspace-wide TCP engagements. By analyzing relative position/distance errors between multiple configurations, the method identifies kinematic parameters via a Local Product of Exponential (Local POE) based error model. Experiments demonstrated a 91.7% position error reduction (7.98mm to 0.66mm) and 69.6% orientation improvement (0.0069rad to 0.0021rad), achieving comparable accuracy to laser-tracker methods at <1% device cost. This approach offers a low-cost, mechanically robust solution for enhancing collaborative robot accuracy in industrial applications.

This paper proposes an innovative virtual chain-based kinematic calibration for the 4PPa-2PaR parallel manipulators with subchain architectures. Conventional calibration methods for such architectures suffer from inherent limitations due to coupled parameter constraints and restricted solution spaces caused by joint displacement and structural parameter dependencies. The presented methodology introduces three fundamental advancements: (1) a novel parameter assignment strategy enabling independent joint/link parameter definition across different kinematic chains, (2) systematic transformation of constrained optimization into an unconstrained one, and (3) significant expansion of error parameter solution space through virtual chain modeling. Comparative experiment on the physical prototype demonstrate improvements in both orientation and position accuracy compared to existing methods.

Industrial robots and collaborative robots are widely employed in industry and are progressively being utilized to assist individuals in their daily routines. To improve their absolute accuracy, self-calibration methods using portable local measurement devices are cost-effective solutions. However, compared with the conventional external calibration methods, self-calibration methods employing two configurations as a calibration sample introduce more non-kinematic errors to the robot. Therefore, noise reduction is significantly necessary in self-calibration. A novel Piecewise-weighted Random Sample Consensus (RANSAC) method is proposed in this paper. Instead of choosing an optimal model with all inliers, the proposed method employs a general weight considering both the sample and hypothesis model qualities to generate a new model with Weighted Least Square (WLS) method. Besides, the proposed method turns the target of finding an uncontaminated set of inliers into the training of the proper weight coefficient for WLS, which not only improves the accuracy but also greatly enhances the speed. The self-calibration experiment on a 6 degree-of-freedom(DOF) robot CR10 shows that the accuracy of the proposed Piecewise-weighted RANSAC method makes a 27.7% accuracy improvement from that employing Least Square method, a 20.0% accuracy improvement from that employing standard RANSAC method, and a 5.5% accuracy improvement from that employing LO-RANSAC method. Besides, the proposed method is also over 10.9 times faster than the standard RANSAC method and 18.6 times faster than the LO-RANSAC method.

In recent years, it has been shown that fault prediction models could effectively guide test effort allocation in finding faults if they have a high enough fault prediction accuracy (Norm(Popt) > 0.78). However, it is often difficult to achieve such a high fault prediction accuracy in practice. As a result, fault-prediction-model-guided allocation (FPA) methods may be not applicable in real development environments. To attack this problem, in this paper, we propose a new type of test effort allocation strategy: reliability-growth-model-guided allocation (RGA) method. For a given project release V, RGA attempts to predict the optimal test effort allocation for V by learning the fault distribution information from the previous releases. Based on three open-source projects, we empirically investigate the cost-effectiveness of three test effort allocation strategies for finding faults: RGA, FPA, and structural-complexity-guided allocation (SCA) method. The experimental results show that RGA shows a promising performance in finding faults when compared with SCA and FPA.