--- title: "CMCL 2026 — all accepted papers with summaries and citations" description: "All 26 papers of CMCL 2026, the most cited first; those with our summary link to it. Among them: Aggregated Transformer Attention Measures…" canonical: "https://research.elamur.ai/free/en/venues/cmcl/2026/papers/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/venues/cmcl/2026/papers/) [home](https://md.research.elamur.ai/free/en/) / [venues](https://md.research.elamur.ai/free/en/venues/) / [CMCL](https://md.research.elamur.ai/free/en/venues/cmcl/) / [2026](https://md.research.elamur.ai/free/en/venues/cmcl/2026/) / papers # CMCL 2026: all accepted papers 26 papers · the most cited first · [← CMCL 2026](https://md.research.elamur.ai/free/en/venues/cmcl/2026/) 1. **[Aggregated Transformer Attention Measures Predict Reading Times Beyond Surprisal](https://md.research.elamur.ai/free/en/papers/mielczarek-2026-aggregated-transformer-attention-measures-predict/)**Lukas Mielczarek, Laura Kallmeyer*our summary* 2. **[Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulation ↗](https://aclanthology.org/2026.cmcl-1.25/)**Sercan Karakas, Yusuf Şimşek 3. **[Brain-to-Text Decoding with Brain Atlases and Brain Foundation Models ↗](https://aclanthology.org/2026.cmcl-1.11/)**Haruka Akama, Ryo Yoshida, Max Müller-Eberstein et al. 4. **[Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task](https://md.research.elamur.ai/free/en/papers/qiu-2026-can-llms-simulate-human-behavioral/)**Mengyang Qiu, Zoe Brisebois, Siena Sun*our summary* 5. **[Causal Inferences Are Driven by Noun Concept Specificity Evidence from Self-Paced Reading and Large Language Model Surprisal](https://md.research.elamur.ai/free/en/papers/schlotterbeck-2026-causal-inferences-are-driven-noun/)**Fabian Schlotterbeck, Raphael Barth*our summary* 6. **[Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans](https://md.research.elamur.ai/free/en/papers/ramarao-2026-character-aware-transformers-learn-irregular/)**Akhilesh Kakolu Ramarao, Kevin Tang, Dinah Baer-Henney*our summary* 7. **[ChineseDevBench: A Chinese Developmental Benchmark for Language Development](https://md.research.elamur.ai/free/en/papers/wang-2026-chinesedevbench-chinese-developmental-benchmark-language/)**Shaonan Wang, Yiwen Wu, Na Li et al.*our summary* 8. **[Comparing Transformer Model Interpretability with Human Cognition: A Dual Analysis of Attention and Attribution ↗](https://aclanthology.org/2026.cmcl-1.8/)**Lingchen Kong, Jinnie Shin, Pavlo Antonenko 9. **[Correlating Language Model Surprisal With Cloze and Plausibility: Getting the Best of Both Measures ↗](https://aclanthology.org/2026.cmcl-1.9/)**Kate Rebecca Belcher, Matthew Crocker 10. **[Disentanglement and Compositionality of Letter Identity and Letter Position in Variational Auto-Encoder Vision Models ↗](https://aclanthology.org/2026.cmcl-1.23/)**Bruno Bianchi, Aakash Agrawal, Stanislas Dehaene et al. 11. **[Examining Algebraic Recombination for Compositional Generalisation](https://md.research.elamur.ai/free/en/papers/ruiz-2026-examining-algebraic-recombination-compositional-generalisation/)**Joaquin Cardona Ruiz, Antske Fokkens, Lucia Donatelli*our summary* 12. **[Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb–Object Constructions](https://md.research.elamur.ai/free/en/papers/piccirilli-2026-floating-or-suggesting-ideas-large/)**Prisca Piccirilli, Alexander Fraser, Sabine Schulte im Walde*our summary* 13. **[Headlines You Won't Forget: Can Pronoun Insertion Increase Memorability?](https://md.research.elamur.ai/free/en/papers/meyer-2026-headlines-you-won-t-forget/)**Selina Meyer, Magdalena Abel, Michael Roth*our summary* 14. **[Is Cross-Lingual Transfer in Bilingual Models Human-Like? A Study with Overlapping Word Forms in Dutch and English](https://md.research.elamur.ai/free/en/papers/skrjanec-2026-is-cross-lingual-transfer-bilingual/)**Iza Škrjanec, Irene Elisabeth Winther, Vera Demberg et al.*our summary* 15. **[Modeling semantic association in self-paced reading with language model embeddings](https://md.research.elamur.ai/free/en/papers/ostergaard-2026-modeling-semantic-association-self-paced/)**Sara Møller Østergaard, Kenneth Enevoldsen, Afra Alishahi et al.*our summary* 16. **[Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not ↗](https://aclanthology.org/2026.cmcl-1.24/)**Sercan Karakas 17. **[Predicting Sentence Acceptability Judgments in Multimodal Contexts ↗](https://aclanthology.org/2026.cmcl-1.3/)**Hyewon Jang, Nikolai Ilinykh, Sharid Loaiciga et al. 18. **[Social Meaning in Large Language Models: Structure, Magnitude, and Pragmatic Prompting ↗](https://aclanthology.org/2026.cmcl-1.13/)**Roland Mühlenbernd 19. **[Teaching LLMs to unveil tendentious implicit contents of Italian political communication](https://md.research.elamur.ai/free/en/papers/paci-2026-teaching-llms-unveil-tendentious-implicit/)**Walter Paci, Lorenzo Gregori, Alessandro Panunzi*our summary* 20. **[ToM in LLM is not ToM, but a Pragmatic Effect ↗](https://aclanthology.org/2026.cmcl-1.4/)**Agnese Lombardi, Alessandro Lenci 21. **[Topic-context Dependency on Continuous Semantic Reconstruction of Language from fMRI Signals](https://md.research.elamur.ai/free/en/papers/travi-2026-topic-context-dependency-continuous-semantic/)**Fermin Travi, Agustin Delmagro, Diego Fernandez Slezak et al.*our summary* 22. **[Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/)**Wajdi Zaghouani*our summary* 23. **[Transformer Attention as a Unified Model of Encoding and Retrieval in Human Sentence Processing](https://md.research.elamur.ai/free/en/papers/parker-2026-transformer-attention-as-unified-model/)**Dan Parker*our summary* 24. **[Translation from the Information Bottleneck Perspective: an Efficiency Analysis of Spatial Prepositions in Bitexts](https://md.research.elamur.ai/free/en/papers/taroni-2026-translation-information-bottleneck-perspective-efficiency/)**Antoine Taroni, Ludovic Moncla, Frederique Laforest*our summary* 25. **[What Kind of Language is Easy to Language-Model Under Curriculum Learning?](https://md.research.elamur.ai/free/en/papers/naggar-2026-what-kind-language-is-easy/)**Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe*our summary* 26. **[`Layer su Layer': Identifying and Disambiguating the Italian NPN Construction in BERT's family ↗](https://aclanthology.org/2026.cmcl-1.16/)**Greta Gorzoni, Ludovica Pannitto, Francesca Masini - [← back to](https://md.research.elamur.ai/free/en/venues/cmcl/2026/) — CMCL 2026