Large Language Models enhanced with Retrieval Augmented Generation show strong potential in knowledge intensive tasks.However, they often encounter knowledge conflicts, where retrieved information contradicts the model's internal knowledge or exhibits internal inconsistencies.Existing methods force models into a binary choice between context and memory, leading to unreliable predictions.We argue that a more principled approach is to embrace contradictions as opportunities for deeper reasoning.To this end, we introduce Debate-of-Thoughts (DoT), a framework that transforms conflict resolution into an active deliberation process.DoT guides a single model through three phases: 1) hypothesis generation, which forms competing perspectives; 2) internal debate, where the model acts as both a proponent and a critic to stress test each view; and 3) adjudication, where the model acts as a judge to evaluate arguments based on evidence and logical consistency.We implement DoT via two complementary strategies: inference time prompt chaining and supervised fine tuning.Experiments across multiple conflict benchmarks show that DoT consistently outperforms state-of-the-art methods, while generating transparent debate transcripts that explain its decisions.By improving both accuracy and interpretability under knowledge conflicts, DoT establishes a more reliable paradigm for retrieval augmented generation systems. 1

In this paper, we develop a novel algorithm for constructing time-uniform, asymptotic confidence sequences for quantiles under local differential privacy (LDP). The procedure combines dynamically chained parallel stochastic gradient descent (P-SGD) with a randomized response mechanism, thereby guaranteeing privacy protection while simultaneously estimating the target quantile and its variance. A strong Gaussian approximation for the proposed estimator yields asymptotically anytime-valid confidence sequences whose widths obey the law of the iterated logarithm (LIL). Moreover, the method is fully online, offering high computational efficiency and requiring only $\mathcal{O}(\kappa)$ memory, where $\kappa$ denotes the number of chains and is much smaller than the sample size. Rigorous mathematical proofs and extensive numerical experiments demonstrate the theoretical soundness and practical effectiveness of the algorithm.

We introduce a novel algorithm for estimating Cumulative Distribution Function (CDF) values under Local Differential Privacy (LDP) by exploiting an unexpected connection between LDP and the current status problem, a classical survival data problem in statistics. This connection leads to the development of tools for constrained isotonic estimation based on binary queries. Through mathematical proofs and extensive numerical testing, we demonstrate that our method achieves uniform and $L_2$ error bounds when estimating the entire CDF curve. By employing increasingly dense grids, the error bound can be improved, exhibiting an asymptotic normal distribution of the proposed estimator. Theoretically, we show that the error bound smoothly changes as the number of grids increases relative to the sample size $n$. Computationally, we demonstrate that our constrained isotonic estimator can be efficiently computed deterministically, eliminating the need for hyperparameters or random optimization.

As generative large language models (LLMs) such as ChatGPT gain widespread adoption in various domains, their potential to propagate and amplify social biases, particularly in high-stakes areas such as the labor market, has become a pressing concern. AI algorithms are not only widely used in the selection of job applicants, individual job seekers may also make use of generative LLMs to help develop their job application materials. Against this backdrop, this research builds on a novel experimental design to examine social biases within ChatGPT-generated job applications in response to real job advertisements. By simulating the process of job application creation, we examine the language patterns and biases that emerge when the model is prompted with diverse job postings. Notably, we present a novel bias evaluation framework based on Masked Language Models to quantitatively assess social bias based on validated inventories of social cues/words, enabling a systematic analysis of the language used. Our findings show that the increasing adoption of generative AI, not only by employers but also increasingly by individual job seekers, can reinforce and exacerbate gender and social inequalities in the labor market through the use of biased and gendered language.

In recent years, the paradigm of neural implicit representations has gained substantial attention in the field of Simultaneous Localization and Mapping (SLAM). However, a notable gap exists in the existing approaches when it comes to scene understanding. In this paper, we introduce NIS-SLAM, an efficient neural implicit semantic RGB-D SLAM system, that leverages a pre-trained 2D segmentation network to learn consistent semantic representations. Specifically, for high-fidelity surface reconstruction and spatial consistent scene understanding, we combine high-frequency multi-resolution tetrahedron-based features and low-frequency positional encoding as the implicit scene representations. Besides, to address the inconsistency of 2D segmentation results from multiple views, we propose a fusion strategy that integrates the semantic probabilities from previous non-keyframes into keyframes to achieve consistent semantic learning. Furthermore, we implement a confidence-based pixel sampling and progressive optimization weight function for robust camera tracking. Extensive experimental results on various datasets show the better or more competitive performance of our system when compared to other existing neural dense implicit RGB-D SLAM approaches. Finally, we also show that our approach can be used in augmented reality applications. Project page: https://zju3dv.github.io/nis_slam.

Based on binary inquiries, we developed an algorithm to estimate population quantiles under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides asymptotically normal estimation with valid inference, resulting in tight confidence intervals without the need for nuisance parameters to be estimated. Our proposed method can be conducted fully online, leading to high computational efficiency and minimal storage requirements with $\mathcal{O}(1)$ space. We also proved an optimality result by an elegant application of one central limit theorem of Gaussian Differential Privacy (GDP) when targeting the frequently encountered median estimation problem. With mathematical proof and extensive numerical testing, we demonstrate the validity of our algorithm both theoretically and experimentally.

The spectral response function (SRF) is a crucial parameter in multispectral radiometers, and it influences the radiometric calibration accuracy and quantitative application capabilities. The in-flight SRF often has errors due to prelaunch contamination or postlaunch degradation. This study proposes an innovative new method to retrieve SRFs of multispectral radiometers based on intercomparisons with hyperspectral sounders via the functional data analysis (FDA) technique. Under the FDA framework, all variables, including the hyperspectral radiance and SRF, are regarded as functions rather than discrete data by expanding in the Fourier functional basis. The forward convolution equation is processed directly into a functional integration model rather than a normally pointwise summation; this ensures that the unknown quantities are transformed from numerous SRF samples to several function parameters, thus avoiding the ill-posed problem. The proposed algorithm is verified with both simulated and real data from multiple thermal infrared bands of the FY-3 IRAS and FY-4 AGRI using collocations with METOP-B IASI. All these results demonstrate our algorithm’s qualitative and quantitative effectiveness for infrared SRF retrieval. Although the demonstrations are particularly relevant to infrared spectra, the algorithm is universal and also applicable to other spectral bands.