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

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

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

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.

LangMAP extends UnigramLM with language-specific MAP estimation, enabling multi-language tokenization from a shared vocabulary, improving morphological boundary alignment.

Clara Meister, Suchir Salhan, Andrzej Szablewski et al.

Proposes the nine-dimensional Meaning Intelligence Framework (MIF) to distinguish surface sentiment from true intent in Nigerian discourse; zero-shot accuracy 33.3%, schema-guided 73.3%.

Celestine Achi

Proposes Turing-RL, a reinforcement learning approach using discriminative Turing rewards to train human user simulators, outperforming traditional response matching methods.

Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu et al.

RubricsTree constructs a hierarchical Boolean rubric system guided by expert-curated clinical criteria, enabling scalable, expert-aligned evaluation with over 100 atomic metrics, surpassing industry baselines.

Weizhi Zhang, Zechen Li, Hamid Palangi et al.

This study benchmarks 12 LLM pipeline configurations on MetaSyn, revealing a screening bottleneck with a maximum recall of 52.7% despite 90.9% retrieval recall at K=200.

Anzhe Xie, Weihang Su, Yujia Zhou et al.

Proposes a method combining lexical token analysis and multi-word similarity to quantify cultural localization in AI-generated stories, revealing only 9-17% of vocabulary accounts for cultural differences.

Shaily Bhatt, Supriti Vijay, Jeremiah Milbauer et al.

Introduces operads as a formal framework for question decomposition, with operadic consistency correlating strongly with model accuracy across multiple datasets.

Nathaniel Bottman, Kyle Richardson

VISTA introduces a hybrid user simulator combining UI and API actions, with six metrics for realism and coverage, outperforming existing methods in diverse scenarios.

Yunan Lu, Ryan Shea, Yusen Zhang et al.

This paper reveals that RLHF achieves shallow alignment by compressing partisan signals without removing the underlying partisan structure, as shown through internal representation analysis of Llama 3.1 8B.

Wendy K. Tam

AdvGRPO framework combines dense multi-channel rewards and advantage decoupling for joint attacker-defender training, achieving over 90% attack success rate and superior defense robustness.

Blake Bullwinkel, Eugenia Kim, Amanda Minnich et al.

Agentopia introduces a long-term multi-agent society simulation over 10 years, leveraging life reward-based reinforcement learning to enhance social behaviors and anthropomorphic capabilities of LLMs.

Xintao Wang, Sirui Zheng, Hongqiu Wu et al.

Proposes EmbedFilter, a linear transformation that filters the latent subspace encoding high-frequency, uninformative tokens, improving zero-shot text embedding performance by up to 14%.

Songhao Wu, Zhongxin Chen, Yuxuan Liu et al.

This paper introduces LLM-guided MAP-Elites evolution for optimizing medical decision pipelines, improving accuracy and safety metrics significantly across tasks.

Ivan Sviridov, Artem Oskin, Ivan Panin et al.

Proposes emergent language in multi-agent RL to study consciousness-related structures without prior biases, revealing self-referential communication and echo-mismatch circuits.

Zengqing Wu, Chuan Xiao

Introduced a benchmark for data snapshot detection, evaluated open-source models, revealing significant gaps in real-world institutional document understanding.

AJ Carl P. Dy, Aivin V. Solatorio