#language models

try —

38 papers match

cs.GT2026

Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games

Joshua Caiata, Sreepriya Pulyassary, Xiang Li +1

The paper introduces a lightweight behavioural embedding for normal-form games, using Nash equilibrium entropy and response sensitivity, to predict how fine‑tuning large language m…

#game theory#language models#transfer learning#embeddings
cs.AI2026

InfoOps Bench: A live information operations safety benchmark

Dorian Quelle, Lisa-Maria Neudert, Jonathan Bright +1

The paper introduces InfoOps Bench, a continuously updated benchmark that measures how easily frontier language models can be co-opted for state-backed information operations, usin…

#information operations#model safety#benchmarking#language models
cs.CL2026

Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory

Rubin Wei, Jiaqi Cao, Jiarui Wang +4

The paper presents Memory Decoder at Scale, a pretrained parametric long‑term memory module for decoder‑only language models that is scaled up to 6.9 B parameters and shown to impr…

#language models#long-term memory#scaling#retrieval
cs.CL2026

MORFES: A Benchmark for Productive Inflectional Competence in Modern Greek

Ioakeim Perros, Cleopatra Papadopoulou, Ayoub Kirouane +1

The paper presents MORFES, a benchmark of 500 expert‑verified items for testing Greek language models' ability to recognize and generate inflected word forms, especially for low‑fr…

#morphology#inflection#benchmark#greek language
cs.LG2026

Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing

Huiyuan Tian, Bonan Xu, Shijian Li

The paper investigates how sparse mixture-of-experts language models route tokens to multiple experts, showing that expert subspaces overlap substantially yet routing still selects…

#mixture of experts#sparse routing#language models#expert overlap
cs.LG2026

Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning

Efstratios Zaradoukas, Davide Gabrielli, Bardh Prenkaj +1

The paper investigates how different reward functions affect the speed and effectiveness of reinforcement‑learning based machine unlearning for language models, proposing graded an…

#machine unlearning#reinforcement learning#reward design#language models