ALEE framework uses AMR-based minimal pairs to evaluate 275+ languages' embedding models, revealing performance gaps related to resources and linguistic phenomena.

Andrianos Michail, Stylianos Psychias, Michelle Wastl et al.

GradeSQL framework enhances Text-to-SQL reliability using ORM, achieving a 4.33% gain on BIRD.

Mattia Tritto, Giuseppe Farano, Dario Di Palma et al.

Introduced STE and SPID frameworks to quantify semantic information flow and multi-source contributions in communication.

Leonardo S. Goodall, Andrea I. Luppi, Pedro A. M. Mediano

VISTA enables self-managed context in LLMs via a training-free, model-agnostic layer that visualizes and archives internal states, boosting long-horizon task performance.

Binyan Xu, Haitao Li, Kehuan Zhang

IHDec uses JSD-based role attribution and contrastive decoding for real-time hierarchy correction, improving multi-turn instruction adherence by 11.98pp.

Nicole Geumheon Liu, Haeun Jang, Yonghyun Jun et al.

EVLA fuses multimodal perception with vehicle physics via UCSE and ESRC, achieving energy-efficient driving decisions with +0.0871 score improvement.

Yuxin Liu, Zihan Chen, Haoyu Wang et al.

MultiHashFormer employs a multi-hash signature mechanism supporting causal language modeling, outperforming standard Transformers across 100M-3B parameters, with zero-parameter multilingual vocabulary expansion.

Huiyin Xue, Atsuki Yamaguchi, Nikolaos Aletras

VASAE introduces vocabulary-aligned anchoring to train SAE features with intrinsic token names, maintaining reconstruction quality and achieving over 90% feature-token alignment.

Kairui Zhang, Ziwen Yu, Zahraa S. Abdallah et al.

Introduces Masked Language Flow Models combining masking and flow models to enhance multi-step reasoning.

Iskander Azangulov, Kianoosh Ashouritaklimi, Leo Zhang et al.

Introduces Ko-WideSearch, a Korean breadth-search benchmark with 228 tables, evaluating web agents' ability for exhaustive set enumeration and attribute filling.

Minbyul Jeong

PEEU framework enables small multimodal models to achieve 30.6% success in web navigation by autonomous exploration and hindsight experience, outperforming larger models.

Tianyi Men, Zhuoran Jin, Pengfei Cao et al.

This study pioneers the application of Large Language Models (LLMs) for securities eligibility screening at the German Central Bank, achieving up to 91% document-level precision through a generative information extraction pipeline.

Serhii Hamotskyi, Akash Kumar Gautam, Christian Hänig

Introduces syntactic belief update model using Rényi divergence, outperforming lexical surprisal in predicting human reading difficulty on garden path sentences.

Alan Zhou, Miloš Stanojević, John T. Hale

MinGram uses BPE seed, Hard EM on minimal path, and single pruning to improve compression and morphology.

Sander Land

CAT-Q enables ultra-efficient ternary quantization of large language models using only 512 calibration samples, outperforming state-of-the-art QAT methods on models up to 235B parameters.

Shigeng Wang, Chao Li, Yangyuxuan Kang et al.

This study introduces a low-resource end-to-end Algerian dialect speech-to-speech system using Whisper, achieving 13.74% WER, integrating ASR, NLU, RAG, and TTS.

Dihia Lanasri, Fairouz Taki, Asma Kemmoum

Hybrid-IR combines graph-based and dense retrieval with iterative reasoning, achieving 10% accuracy improvement on medical QA benchmarks.

Sheng Wan, Jiahui Zhang, Zicheng Zhao et al.

Proposes a training-free method using intermediate-layer entropy trajectories to detect jailbreak prompts, achieving AUROC up to 0.94 across Llama, Qwen, and Gemma.

Sofiia Nikolenko, Michele Papucci, Mina Rezaei et al.

Proposes a four-module analytical framework to evaluate 12 agent memory systems across diverse workloads, revealing workload-architecture matching importance.

Wei Zhou, Xuanhe Zhou, Shaokun Han et al.

NatureBench evaluates AI coding agents on 90 scientific tasks; strongest model surpasses SOTA in only 17.8%.

Yuru Wang, Lejun Cheng, Yuxin Zuo et al.