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版本更新 · v4arXiv:2610.02236

Fixed-Capacity Causal Models with Parallel Recovery for GPU Lossless Compression

Zeyu Jia

原文摘要

GPU lossless compression requires both compact complete frames and fast checked recovery. We evaluate fixed-capacity causal byte-bank representations under a shared 512-row probability budget, select a single representation via a pre-payload complete-cost proxy charging both empirical cross-entropy and compressed model tables, and align two rANS states per 8\,KiB chunk with the causal byte banks. Independent byte banks decode concurrently, whereas intra-word conditional banks decode in two ordered phases. On ten structured imaging and electrophysiology inputs, split-bank execution lowers aggregate median encode-plus-recovery time by 13.10\% for 0.18\% additional frame bytes, and reduces complete encoding cost by 44.16\% relative to a matched native-CPU model control while supporting a standalone CUDA-free CPU receiver. In a twelve-configuration GPU comparison, Split GPX produces 5.75\% and 7.11\% smaller complete frames than Zigshuffle + nvCOMP Zstd at 35.14\% and 19.03\% lower paired cost for 64\,KiB and 8\,KiB requests. For multi-channel sample-interleaved waveforms, extending causal differencing with zero-oracle GPU stride screening (strided-residual) reduces holdout 12-lead PTB Diagnostic ECG frames by 49.89\% (from 71.47\% to 35.81\% of raw bytes, 37.20\% smaller than Zigshuffle + Zstd-1 and 46.67\% smaller than OpenZL-9) while preserving byte parity across all single-stream benchmarks.

cs.AR

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2509.24102

Pragmatic Inference for Improving Social Reasoning Generalization in Large Language Models

Guangliang Liu, Xi Chen, Bocheng Chen, Han Zi, Xitong Zhang, Yuda Chen, Kristen Johnson

原文摘要

Achieving social reasoning, including moral reasoning, cultural awareness, and politeness, is critical for the socially reliable deployment of large language models (LLMs). A key bottleneck is limited generalization: social situations that convey similar social implicatures can be expressed through highly diverse linguistic forms, creating an enormous space of possible situations and posing a substantial computational challenge even for LLMs. From a linguistic perspective, this challenge arises because social reasoning operates at the pragmatic level, requiring a pragmatic inference process to infer social implicatures from a given situation and its underlying social context. Although moral values such as moral foundations are generally considered as the representations of the social context, how to construct the corresponding pragmatic inference steps remains underexplored. Taking moral reasoning as an instance of social reasoning, this paper develops a pragmatic inference approach that enables LLMs to infer moral implicatures by (1) specifying the inferential steps through which LLMs derive moral implicatures from language, and (2) explicitly modeling the metapragmatic links among these inferential steps to bridge the pragmatic gaps between them and make such inferences learnable for LLMs. Experimental results demonstrate that our approach substantially improves LLMs' generalization in moral reasoning, highlighting the potential of pragmatic inference for future social reasoning research.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2601.03079

Learning to Diagnose and Correct Moral Errors in Large Language Models

Bocheng Chen, Xi Chen, Han Zi, Haitao Mao, Zimo Qi, Xitong Zhang, Yuda Chen, Kristen Johnson 等 9 位作者

原文摘要

Enabling large language models (LLMs) to diagnose and correct moral errors is crucial not only for ensuring their reliable deployment but also for charting a pathway toward continuous improvement. Existing research has primarily followed two directions: fine-tuning LLMs on corpora of morally appropriate texts and developing carefully designed prompting strategies to promote moral behavior, including moral self-correction and self-improvement. However, both lines of work often remain brittle, relying heavily on shallow heuristics rather than enabling LLMs to reason about why and how a given moral discourse is morally (in)appropriate. In this paper, we address this challenge by developing pragmatic inference-driven approach that help LLMs learn to diagnose and correct moral errors. Pragmatic inference is the reasoning process through which implied meanings are derived, a central concept in linguistics. To improve the reasoning efficiency, our approach adapt the inference procedure according to the inferential load required by different moral discourses. Empirical results demonstrate that our method not only improves LLMs' ability to diagnose and correct moral errors, but also generalizes effectively across tasks without relying on shallow heuristics.

cs.CL

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arXiv ↗PDF ↗