Research
On the shoulders of giants.
From Donald Hebb to current SIGIR and ACL research: Hebby unites the best of memory psychology, neuroscience and computer science.
43 sources
Classic foundational works and cutting-edge research.
Psychology meets computer science
How human memory works, translated into a memory for AI.
Gold standard, not gut feeling
From Psychological Review and Nature Reviews Neuroscience to SIGIR, NeurIPS and ACL.
The human model: memory psychology
The foundation of Hebby: classic and key papers on how the human brain learns, links, consolidates and remembers.
The Organization of Behavior: A Neuropsychological Theory
Hebb, D. O. (1949). Wiley; reprint Psychology Press.
A spreading-activation theory of semantic processing
Collins, A. M., & Loftus, E. F. (1975). Psychological Review, 82(6).
Reflections of the Environment in Memory
Anderson, J. R., & Schooler, L. J. (1991). Psychological Science, 2(6).
Why there are complementary learning systems in the hippocampus and neocortex
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Psychological Review, 102(3).
The memory function of sleep
Diekelmann, S., & Born, J. (2010). Nature Reviews Neuroscience, 11(2).
Language models and agent memory
Current research on how language models handle context and how AI agents get a memory.
Large Language Models Can Be Easily Distracted by Irrelevant Context
Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E., Schärli, N., & Zhou, D. (2023). ICML 2023.
Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory
Yuan, B., Su, Y., & Yao, K. (2026). arXiv.
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). UIST '23.
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
Zhu, J., Li, J., Zhang, C., Liu, J., & Yang, M. (2026). ACL 2026.
Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring
Zhang, J., Elluri, S., Cherukuvada, B., Joffe, Y., Sena, J., et al. (2026). arXiv.
Multilingual E5 Text Embeddings: A Technical Report
Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., & Wei, F. (2024). arXiv.
Learning from feedback
Learning methods that let a memory keep improving itself and relearn along with its environment.
An Empirical Evaluation of Thompson Sampling
Chapelle, O., & Li, L. (2011). NeurIPS 2011.
A Tutorial on Thompson Sampling
Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). Foundations and Trends in Machine Learning, 11(1).
Taming Non-stationary Bandits: A Bayesian Approach
Raj, V., & Kalyani, S. (2017). arXiv.
On Upper-Confidence Bound Policies for Switching Bandit Problems
Garivier, A., & Moulines, E. (2011). ALT 2011.
A Change-Detection Based Framework for Piecewise-Stationary Multi-Armed Bandit Problem
Liu, F., Lee, J., & Shroff, N. (2018). AAAI 2018.
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
Besson, L., Kaufmann, E., Maillard, O.-A., & Seznec, J. (2022). Journal of Machine Learning Research, 23.
Search and ranking
State-of-the-art information retrieval: how many memories become exactly the right ones.
Towards a Relevance Posterior in Neural Information Access
Parry, A., Lionis, A., Ganguly, D., & MacAvaney, S. (2026). SIGIR '26.
Relevance weighting for query independent evidence
Craswell, N., Robertson, S., Zaragoza, H., & Taylor, M. (2005). SIGIR '05.
Voting for candidates: adapting data fusion techniques for an expert search task
Macdonald, C., & Ounis, I. (2006). CIKM '06.
Formal models for expert finding in enterprise corpora
Balog, K., Azzopardi, L., & de Rijke, M. (2006). SIGIR '06.
A re-examination of text categorization methods
Yang, Y., & Liu, X. (1999). SIGIR '99.
Regularizing ad hoc retrieval scores
Diaz, F. (2005). CIKM '05.
Corpus structure, language models, and ad hoc information retrieval
Kurland, O., & Lee, L. (2004). SIGIR '04.
Cluster-based retrieval using language models
Liu, X., & Croft, W. B. (2004). SIGIR '04.
Adaptive Re-Ranking with a Corpus Graph
MacAvaney, S., Tonellotto, N., & Macdonald, C. (2022). CIKM '22.
Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors
Zerveas, G., Rekabsaz, N., & Eickhoff, C. (2023). EMNLP 2023.
Query quality and vector space
How a memory recognizes how reliably a memory fits the query.
Predicting query performance
Cronen-Townsend, S., Zhou, Y., & Croft, W. B. (2002). SIGIR '02.
Predicting Query Performance by Query-Drift Estimation
Shtok, A., Kurland, O., Carmel, D., Raiber, F., & Markovits, G. (2012). ACM Transactions on Information Systems, 30(2).
Modeling score distributions for combining the outputs of search engines
Manmatha, R., Rath, T., & Feng, F. (2001). SIGIR '01.
Performance prediction using spatial autocorrelation
Diaz, F. (2007). SIGIR '07.
Query Performance Prediction for Neural IR: Are We There Yet?
Faggioli, G., Formal, T., Marchesin, S., Clinchant, S., Ferro, N., & Piwowarski, B. (2023). ECIR 2023.
On Coherence-based Predictors for Dense Query Performance Prediction
Vlachou, M., & Macdonald, C. (2023). arXiv.
Coherence-based Query Performance Measures for Dense Retrieval
Vlachou, M., & Macdonald, C. (2024). ICTIR '24.
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Radovanović, M., Nanopoulos, A., & Ivanović, M. (2010). Journal of Machine Learning Research, 11.
How Contextual are Contextualized Word Representations?
Ethayarajh, K. (2019). EMNLP-IJCNLP 2019.
Feedback and query expansion
How a memory thinks connections further and expands a query in a targeted way.
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
Li, H., Mourad, A., Zhuang, S., Koopman, B., & Zuccon, G. (2023). ACM Transactions on Information Systems.
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
Yu, H., Xiong, C., & Callan, J. (2021). CIKM '21.
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Li, H., Zhuang, S., Mourad, A., Ma, X., Lin, J., & Zuccon, G. (2022). ECIR 2022.
Entity query feature expansion using knowledge base links
Dalton, J., Dietz, L., & Allan, J. (2014). SIGIR '14.
Improving automatic query expansion
Mitra, M., Singhal, A., & Buckley, C. (1998). SIGIR '98.
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Subramanian, A., Tamayo, P., Mootha, V. K., et al. (2005). PNAS, 102(43).
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
Althammer, S., Hofstätter, S., Sertkan, M., Verberne, S., & Hanbury, A. (2022). ECIR 2022.