47 citations · 61 across the 10 of their papers we have counts for
10 papers
Extending LLM Context via Associative Recurrent Memory
Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov +8
Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and lin…
Semantic Chunking and the Entropy of Natural Language
Weishun Zhong, Doron Sivan, Tankut Can +2
Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong redundancy of language viewed as a stoch…
Random Tree Model of Meaningful Memory
Weishun Zhong, Tankut Can, Antonis Georgiou +3
Traditional studies of memory for meaningful narratives focus on specific stories and their semantic structures but do not address common quantitative features of recall across dif…
Information Theory of Meaningful Communication
Doron Sivan, Misha Tsodyks
In Shannon's seminal paper, entropy of printed English, treated as a stationary stochastic process, was estimated to be roughly 1 bit per character. However, considered as a means…
Synaptic Theory of Chunking in Working Memory
Weishun Zhong, Mikhail Katkov, Misha Tsodyks
Working memory often appears to exceed its basic span by organizing items into compact representations called chunks. Chunking can be learned over time for familiar inputs; however…
Large-scale study of human memory for meaningful narratives
Antonios Georgiou, Tankut Can, Mikhail Katkov +1
The statistical study of human memory requires large-scale experiments, involving many stimuli conditions and test subjects. While this approach has proven to be quite fruitful for…
Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes
Christopher J. Cueva, Adel Ardalan, Misha Tsodyks +1
Many daily activities and psychophysical experiments involve keeping multiple items in working memory. When items take continuous values (e.g., orientation, contrast, length, loudn…
Biases and Variability from Costly Bayesian Inference
Arthur Prat-Carrabin, Florent Meyniel, Misha Tsodyks +1
When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations…
Retroactive Interference Model of Power-Law Forgetting
Antonios Georgiou, Mikhail Katkov, Misha Tsodyks
Memory and forgetting constitute two sides of the same coin, and although the first has been rigorously investigated, the latter is often overlooked. A number of experiments under…
Fundamental Law of Memory Recall
Michelangelo Naim, Mikhail Katkov, Sandro Romani +1
Free recall of random lists of words is a standard paradigm used to probe human memory. We proposed an associative search process that can be reduced to a deterministic walk on ran…