Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting

PrivGemo has been introduced as a privacy-preserving framework designed for knowledge graph (KG)-grounded reasoning, addressing the risks associated with using private KGs in large language models (LLMs). This dual-tower architecture maintains local knowledge while allowing remote reasoning through an anonymized interface, effectively mitigating semantic and structural exposure.

A recent study published on arXiv investigates the generalization capabilities of AI-generated text detectors, revealing that while these detectors perform well on in-domain benchmarks, they often fail to generalize across various generation conditions, such as unseen prompts and different model families. The research employs a comprehensive benchmark involving multiple prompting strategies and large language models to analyze performance variance through linguistic features.

A recent study titled 'Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations' highlights the limitations of current confidence estimation methods for large language models (LLMs), emphasizing the need for evaluations that account for language variations and semantic differences. The research proposes a new framework that assesses confidence quality based on robustness, stability, and sensitivity to variations in prompts and answers.

Recent advancements in prompting methods for Large Language Models (LLMs) have led to the introduction of the Adaptive Causal Prompting with Sketch-of-Thought (ACPS) framework, which aims to enhance reasoning capabilities while reducing token usage and inference costs. This framework utilizes structural causal models to adaptively select interventions for improved generalizability across diverse reasoning tasks.

A recent study titled 'BenchOverflow' investigates a failure mode in large language models (LLMs) where plain-text prompts lead to excessive outputs, termed Overflow. This phenomenon can increase operational costs, latency, and degrade performance across users, particularly in high-demand environments.

A new technical paradigm called ExpSeek has been introduced, enhancing web agents' interaction capabilities by enabling proactive experience seeking rather than passive experience injection. This approach utilizes step-level entropy thresholds to optimize intervention timing and tailor-designed experience content, demonstrating significant performance improvements in Qwen3-8B and Qwen3-32B models across various benchmarks.

A recent study published on arXiv compares the effectiveness of neural models and large language models (LLMs) in predicting nationality and region from personal names. The research evaluates six neural models and six LLM prompting strategies across three levels of granularity, revealing that LLMs consistently outperform traditional models in accuracy.

A recent study published on arXiv investigates the phenomenon of negative constraints in large language models, revealing that such instructions often lead to unexpected failures. The research introduces the concept of semantic pressure, which quantitatively measures the likelihood of generating forbidden tokens, and establishes a logistic relationship between violation probability and semantic pressure.