9394 papers
The bond between carbon and chlorine, known as the C–Cl bond, is a fundamental building block in organic chemistry and materials science. This specific interaction influences everything from the stability of pharmaceuticals to the environmental behavior of industrial solvents. Understanding how these atoms connect and react helps scientists design safer chemicals and develop new methods for breaking down persistent pollutants in our ecosystem.
At Gist.Science, we process every new preprint in this category directly from arXiv to make these complex findings accessible to everyone. Whether you are a seasoned researcher or a curious learner, you can access both plain-language overviews and detailed technical summaries for each study. This dual approach ensures that the nuances of C–Cl chemistry are clear without losing the scientific rigor found in the original manuscripts.
Below are the latest papers on carbon-chlorine interactions, updated daily as they appear on arXiv.
This paper introduces DAPA, a training-free, plug-and-play safety enhancement method that leverages knowledge distillation and model fusion to inject alignment signals from well-aligned models into shadow-aligned ones, significantly improving defense success rates against harmful inputs without compromising performance.
This study provides empirical evidence that the release of ChatGPT causally influenced human spoken communication by increasing the frequency of specific words in spontaneous speech, demonstrating that humans internalize and adopt the lexical patterns of large language models, thereby integrating AI into the ongoing process of cultural evolution.
The paper introduces PERL, a Pinyin-enhanced language model that improves Chinese ASR N-best error correction by fusing semantic and phonetic representations through token-wise gating while enforcing strict length constraints via a mask budgeting mechanism to achieve significant character error rate reductions.
This paper introduces Step-Tagging, a lightweight framework that uses a novel reasoning step taxonomy (ReasonType) to enable real-time monitoring and early stopping of Language Reasoning Models, achieving a 20–50% reduction in token generation while maintaining comparable accuracy across mathematical and non-mathematical benchmarks.
This paper proposes WavePhaseNet, a method that reformulates LLM attention mechanisms through measure theory and frequency analysis to construct a Semantic Conceptual Hierarchy Structure via Discrete Fourier Transform, thereby enabling dimensionality reduction and cohomological regularization to theoretically mitigate hallucinations and enforce logical consistency.
This paper introduces the first application of pregroup grammar-based quantum compositional natural language processing to Arabic, demonstrating through controlled experiments that quantum circuits mirroring the language's morphological and syntactic structure can effectively model word order, tense, and word sense disambiguation compared to classical baselines like AraVec and AraBERT.
The paper proposes LBA, a sampling-based method that iteratively integrates prior and posterior knowledge to construct an approximate distribution of high-quality adversarial examples, thereby significantly outperforming existing greedy approaches in generating semantically preserved hard-label adversarial texts under low query budgets.
This paper introduces Token Time Continuous Diffusion (TTCD), a continuous-space diffusion language model that utilizes per-token time scheduling to deterministically map noise to tokens, achieving superior performance in conditional generation and high-speed inference compared to existing discrete models.
Polestar is a training-free inference framework that leverages token representation drift as a unified signal to enable efficient KV-cache reuse and reliable token commitment, thereby significantly improving both the accuracy and throughput of diffusion large language models.
This paper introduces "tool efficiency" and "marginal tool utility" as new quantitative metrics to directly evaluate and optimize the rate of useful tool calls in LLM agent trajectories, aiming to guide the creation of leaner tool suites that complement traditional accuracy-based evaluations.