large language models

NL2SHACL-Bench: A Benchmark Suite for Natural Language to SHACL Translation

The results show that current LLMs are highly capable of generating syntactically valid SHACL, but still struggle to produce semantically equivalent constraints for complex logical and structural patterns, which indicates that NL2SHACL-Bench provides a meaningful basis for measuring advances in the NL2SHACL state of th...

Full-Layer Memory Injection via Compile-Time Key-Value Caches: An External-Knowledge Mechanism for Small-Parameter Language Models

Knowledge of large language models (LLMs) is frozen in parameters after pretraining and hard to update at low cost. This paper proposes a full-layer memory injection method based on compile-time key-value (KV) caches. Experiments on Qwen3-0.6B show full-layer token-level KV injection raises private-specification QA sco...

Explainable and domain-adaptive prediction models for refrigerant charging in air conditioning systems within industrial processes

AC-RPX improves predictive performance over conventional rule-based heuristic methods while providing clearer diagnostic explanations on real-world consumer data, establishing AC-RPX as a practical solution for automated refrigerant monitoring and decision support in real-world maintenance services.

A survey on synergizing large language models and knowledge graphs for reasoning

The Epistemic Chain Reaction: Human-AI Multiplication from Questions to Readout-Distinguishable Hypotheses — A Controlled Amplification Architecture for Scientific Discovery

Series. Paper IV of the Bounded Knower sequence. Paper I: From Problem to Hypothesis (DOI 10.5281/zenodo.22307148). Paper II: Knowledge Topology and the First Passage to Usable Hypotheses (DOI 10.5281/zenodo.22307561). Paper III: Before Evidence Can Decide (DOI 10.5281/zenodo.22307564). Status. GLOSA K0 conceptual and...

A Study on Impact of Generative AI on Investment Decision-making at Motilal Oswal Financial Services Ltd.

The study concludes that integrating Generative AI into investment workflows is highly financially feasible and operationally sustainable, providing a significant competitive advantage for modern asset management institutions.

Operation Waffles: AI-Mediated Lexical Drift and Distributed Framing Effects

Operation Waffles proposes a harmless proof-of-concept for studying whether large language models can introduce measurable lexical drift into human language. The paper suggests using an innocuous tracer term—“waffles”—to test whether small model-level preferences for particular words, metaphors, or phrasings survive in...

Performance of large language models and pediatricians in complex pediatric scenarios: a prospective, randomized comparative study of clinical task accuracy and decision support

To compare response accuracy between contemporary large language models (LLMs) and pediatricians across difficulty-stratified pediatric clinical vignettes, characterize LLM response times, and quantify the effect of AI-generated outputs on pediatrician decisions. In a prospective, randomized, assessor-blinded, vignette...

Impersonation Ambiguity as a Security Signal: A Quantitative Framework for Role-Play vs. Malicious Impersonation Detection in Large Language Models

Large language models (LLMs) increasingly support persona adoption and character role-play as a first-class, legitimate usecase; creative writing assistants, companion chatbots, and educational simulations all depend on it. Yet the same mechanism isthe substrate of one of the most persistent jailbreak families in the l...

Full-Layer Memory Injection via Compile-Time Key-Value Caches: An External-Knowledge Mechanism for Small-Parameter Language Models

Knowledge of large language models (LLMs) is frozen in parameters after pretraining and hard to update at low cost. This paper proposes a full-layer memory injection method based on compile-time key-value (KV) caches. Experiments on Qwen3-0.6B show full-layer token-level KV injection raises private-specification QA sco...

A source-linked database of all-solid-state battery electrolyte formulations and performance metrics

This is a source-linked database of all-solid-state battery (ASSB) electrolyte formulations and their reported electrochemical performance, extracted from 1,000 peer-reviewed publications. Every record in the database is traceable to a source DOI and the reporting publication. Content • 2,764 electrolyte-formulation re...

Cell line screening of AI-generated drug repurposing predictions for NF2-related schwannomas

Non-NF2 schwannomatosis (SWN) is a rare syndrome that causes nerve sheath tumors with complex genetic variations and poorly understood modifiers. With no approved pharmacologic interventions, there is an urgent need for treatments that prevent or reduce tumor growth, or that ameliorate schwannoma-associated pain. Many...