arXiv:2610.08364v1 Announce Type: new
Abstract: Frontier AI evaluations increasingly use open-ended, agentic, long-horizon tasks whose transcripts can span hundreds of pages of outputs and actions fr...
By Toby D. Pilditch, Konstantinos Voudouris, Alexandra Abbas, Cozmin Ududec
Gist-based context compression---summarising older conversation history into compact representations---is a common approach in long-horizon language model agents, yet its effect on different types of memory retrieval is poorly understood. We use Salience-Weighted Consolidation (SWC), a biologically-inspired compression framework motivated by sleep-based memory consolidation, as a diagnostic probe to study when gist compression helps and when it hurts.
The paper introduces a live trace model that incrementally folds an append‑only event ledger into typed run state, producing per‑consumer views for both human observers and the agent itself. Evaluations show that for observers, the compiled view reduces input tokens by 14–15× and cost by 5–7× while improving accuracy from 0.48 to 0.85–0.87. For agents, maintaining running statistics in per‑step state enables success on 120‑link sequential tasks where full‑context prompting fails, and a prompt‑level scratchpad matches the fold’s accuracy at lower cost.
By Egor Pakhomov, Erik Nijkamp
arXiv:2606.22953v2 Announce Type: replace
Abstract: Context managers summarize or evict old messages so agents can operate beyond a finite window. This creates a reliability problem: removing history...
By Aman Mehta, Anupam Datta
arXiv:2606. 15405v1 Announce Type: cross Abstract: Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their behaviour to each user.
By Weidong Guo, Dakai Wang, Zixuan Wang, Hui Liu, Yu Xu
arXiv:2608. 11775v1 Announce Type: new Abstract: Gist-based context compression---summarising older conversation history into compact representations---is a common approach in long-horizon language model agents, yet its effect on different types of memory retrieval is poorly understood.
By Nicholas E. Kyrkewood