1d ago

Workshop Seeks Submissions On Long-Context LLM Techniques At COLM

0
Original post

🚨Excited to announce our workshop Context Beyond the Window hosted at COLM in SF! 🚨 LLMs have finite context windows, yet real-world tasks demand absorbing, retaining, and acting on information that far exceeds any single prompt. 1/3 We're looking for submissions across: https://context-beyond-window.github.io/ • Context compression 🧃 — token compaction, recursive subagent calls, and external memory for storing and retrieving information • Efficient architectures 🚀 — sub-quadratic attention variants that make extremely long context computationally feasible • Continual training 🌱 — test-time training on streaming data, context distillation, and knowledge accumulation through continued pre-training • Agentic memory systems 🐘 — scaffolds and test-time scaling techniques that improve knowledge retention and acquisition in LLMs • Evaluation 🎯 — benchmarking models on increasingly long-horizon tasks

Modern language models operate within finite context windows, yet many real-world tasks require models to absorb, retain, and act on information that far exceeds any single prompt.

This workshop addresses the full spectrum of context management: fitting more into the window, maintaining state across interactions, and transferring knowledge into parameters. We frame this around the trade-off between context-time memory (information supplied at inference) and weight-time memory (information absorbed into parameters).

Our goal is to build a shared vocabulary across subcommunities that rarely meet in one venue: long-context modeling, retrieval-augmented systems, continual learning, knowledge distillation, and LLM agents.
9:26 AM · May 28, 2026 View on X