10 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Learning When to Attend: Conditional Memory Access for Long-Context LLMs
Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato +4
Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval. Continued pretraining on long-context data can help but is…
Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression
Liangzu Peng, Aditya Chattopadhyay, Luca Zancato +3
Linear State-Space Models (SSMs) offer an efficient alternative to softmax Attention with constant memory and linear compute, but their lossy, fading summary of the past hurts reca…
Priming: Hybrid State Space Models From Pre-trained Transformers
Aditya Chattopadhyay, Elvis Nunez, Prannay Kaul +6
Hybrid State-Space models combine Attention with recurrent State-Space Model (SSM) layers, balancing eidetic memory from Attention with compressed fading memory from SSMs. This yie…
Maximally-Informative Retrieval for State Space Model Generation
Evan Becker, Benjamin Bowman, Matthew Trager +4
Given a query and dataset, the optimal way of answering the query is to make use all the information available. Modern LLMs exhibit impressive ability to memorize training data, bu…
Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models
Elvis Nunez, Luca Zancato, Benjamin Bowman +3
The "state" of State Space Models (SSMs) represents their memory, which fades exponentially over an unbounded span. By contrast, Attention-based models have "eidetic" (i.e., verbat…