arXiv:2410.10857v2 Announce Type: replace-cross Abstract: Self-Consistency, a widely-used decoding strategy, significantly boosts the reasoning capabilities of Large Language Models (LLMs). However, it depends on the plurality voting rule, which focuses on the most frequent answer while overlooking all other minority…
arXiv:2509.13761v1 Announce Type: new Abstract: Large Language Models (LLMs) have made remarkable progress in mathematical reasoning, but still continue to struggle with high-precision tasks like numerical computation and formal symbolic manipulation. Integrating external tools has emerged as a promising approach…
arXiv:2502.07445v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language understanding. We introduce the Chameleon Benchmark Overfit Detector…
arXiv:2509.13773v1 Announce Type: new Abstract: The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices. To simplify access to predefined AI services, this paper introduces MIRA,…
arXiv:2504.01153v4 Announce Type: replace-cross Abstract: While we increasingly rely on large language models (LLMs) for various tasks, these models are known to produce inaccurate content or ‘hallucinations’ with potentially disastrous consequences. The recent integration of web search results into LLMs…
arXiv:2509.13880v1 Announce Type: new Abstract: Linear constraints are one of the most fundamental constraints in fields such as computer science, operations research and optimization. Many applications reduce to the task of model counting over integer linear constraints (MCILC). In this…
arXiv:2505.12381v2 Announce Type: replace-cross Abstract: Current research on bias in language models (LMs) predominantly focuses on data quality, with significantly less attention paid to model architecture and temporal influences of data. Even more critically, few studies systematically investigate the origins…
arXiv:2509.13968v1 Announce Type: new Abstract: Transitional accounts of evolution emphasise a few changes that shape what is evolvable, with dramatic consequences for derived lineages. More recently it has been proposed that cognition might also have evolved via a series of…
arXiv:2507.20923v2 Announce Type: replace-cross Abstract: Multi-objective combinatorial optimization problems (MOCOP) frequently arise in practical applications that require the simultaneous optimization of conflicting objectives. Although traditional evolutionary algorithms can be effective, they typically depend on domain knowledge and repeated parameter tuning,…
arXiv:2509.14030v1 Announce Type: new Abstract: High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language Models (LLMs), Small Language Models (SLMs), and human experts-they often focus narrowly…
