aiqbench analysis
This arXiv paper is about memory allocation for LLM agents: deciding what past interaction history to keep in limited context, at what level of detail, using an adaptive "price" on context usage. It is clearly within AI, and its core contribution is a theoretical regret bound plus benchmark results on LongMemEval, LoCoMo, AppWorld and WebShop, so readers should weigh the empirical gains against the stated assumptions (e.g., "priced-frontier regularity") rather than the framing alone.
MemPrice: Online Memory Allocation via Adaptive Pricing for Long-Horizon Agents
arXiv:2610.24955v1 Announce Type: new Abstract: Long-horizon LLM agents face a fundamental allocation problem as interaction histories grow: which information to retain, how much context to devote to it, and at what granularity. Existing memory mechanisms largely focus on relevance, retrieval, or compression, leaving this allocation trade-off implicit. We introduce MemPrice, which treats active context as a shared resource and dynamically allocates it across memories and representation levels. MemPrice turns accumulated memory pressure into an adaptive price on context, encouraging detail only when its estimated retention value justifies the additional footprint. This pressure persists across memory refreshes, allowing the contents of memory to evolve while preserving long-run control over context usage. We show that irreversible compression can force linear worst-case regret against hindsight following its own memory trajectory, and introduce a same-state benchmark to evaluate allocation on MemPrice's realized memory states. MemPrice enforces the hard context budget at every round and regulates long-run occupancy through adaptive pricing, without prior knowledge of the interaction horizon. Under priced-frontier regularity, its average allocation-regret bound against this benchmark vanishes when accumulated estimation error is sublinear. We evaluate the framework under matched memory budgets on LongMemEval, LoCoMo, AppWorld, and WebShop across three foundation models, spanning both query-blind memory construction and goal-directed agent context management.
Abstract:Long-horizon LLM agents face a fundamental allocation problem as interaction histories grow: which information to retain, how much context to devote to it, and at what granularity. Existing memory mechanisms largely focus on relevance, retrieval, or compression, leaving this allocation trade-off implicit. We introduce MemPrice, which treats active context as a shared resource and dynamically allocates it across memories and representation levels. MemPrice turns accumulated memory pressure into an adaptive price on context, encouraging detail only when its estimated retention value justifies the additional footprint. This pressure persists across memory refreshes, allowing the contents of memory to evolve while preserving long-run control over context usage. We show that irreversible compression can force linear worst-case regret against hindsight following its own memory trajectory, and introduce a same-state benchmark to evaluate allocation on MemPrice's realized memory states. MemPrice enforces the hard context budget at every round and regulates long-run occupancy through adaptive pricing, without prior knowledge of the interaction horizon. Under priced-frontier regularity, its average allocation-regret bound against this benchmark vanishes when accumulated estimation error is sublinear. We evaluate the framework under matched memory budgets on LongMemEval, LoCoMo, AppWorld, and WebShop across three foundation models, spanning both query-blind memory construction and goal-directed agent context management.
Artificial Intelligence (cs.AI)
arXiv:2610.24955 [cs.AI]
(or arXiv:2610.24955v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.24955 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 295 words