Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues
arXiv:2603.04885v1 Announce Type: new Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold. To explore this challenge, we introduce \textbf{STEM-Bench}, the first benchmark for \textbf{ST}reaming \textbf{E}valuation of \textbf{M}emory. It comprises over 14K QA pairs in dialogue streams that assess perception fidelity, temporal reasoning, and global awareness under infinite-horizon constraints. The preliminary analysis on STEM-Bench indicates a critical \textit{fidelity-efficiency dilemma}: retrieval-based methods use fragment context, while full-context models incur unbounded latency. To resolve this, we propose \textbf{ProStream}, a proactive hierarchical memory framework for streaming dialogues. It enables ad-hoc memory recall on de
arXiv:2603.04885v1 Announce Type: new Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold. To explore this challenge, we introduce \textbf{STEM-Bench}, the first benchmark for \textbf{ST}reaming \textbf{E}valuation of \textbf{M}emory. It comprises over 14K QA pairs in dialogue streams that assess perception fidelity, temporal reasoning, and global awareness under infinite-horizon constraints. The preliminary analysis on STEM-Bench indicates a critical \textit{fidelity-efficiency dilemma}: retrieval-based methods use fragment context, while full-context models incur unbounded latency. To resolve this, we propose \textbf{ProStream}, a proactive hierarchical memory framework for streaming dialogues. It enables ad-hoc memory recall on demand by reasoning over continuous streams with multi-granular distillation. Moreover, it employs Adaptive Spatiotemporal Optimization to dynamically optimize retention based on expected utility. It enables a bounded knowledge state for lower inference latency without sacrificing reasoning fidelity. Experiments show that ProStream outperforms baselines in both accuracy and efficiency.
Executive Summary
This article introduces STEM-Bench, a benchmark for evaluating memory in streaming dialogues, and proposes ProStream, a proactive hierarchical memory framework to resolve the fidelity-efficiency dilemma in infinite-horizon dialogue processing. The framework employs multi-granular distillation and Adaptive Spatiotemporal Optimization to achieve ad-hoc memory recall, bounded knowledge state, and lower inference latency without sacrificing reasoning fidelity. ProStream outperforms baselines in both accuracy and efficiency, indicating its potential for real-world dialogue applications. However, the article's focus on the technical aspects of the framework and the benchmark raises questions about its broader implications for human-computer interaction and the role of memory in dialogue systems.
Key Points
- ▸ Introduction of STEM-Bench, a benchmark for evaluating memory in streaming dialogues
- ▸ Proposing ProStream, a proactive hierarchical memory framework for infinite-horizon dialogue processing
- ▸ Resolving the fidelity-efficiency dilemma through multi-granular distillation and Adaptive Spatiotemporal Optimization
Merits
Technical Innovation
The article introduces a novel benchmark and framework for addressing the challenges of infinite-horizon dialogue processing.
Empirical Evaluation
The authors provide thorough empirical evaluation of ProStream, demonstrating its superiority over baselines in accuracy and efficiency.
Potential for Real-World Applications
The framework's ability to achieve ad-hoc memory recall and bounded knowledge state makes it potentially useful for real-world dialogue applications.
Demerits
Limited Broader Implications
The article's focus on the technical aspects of the framework and benchmark raises questions about its broader implications for human-computer interaction and the role of memory in dialogue systems.
Dependence on Specific Dataset
The evaluation of ProStream relies on the STEM-Bench dataset, which may limit its generalizability to other dialogue scenarios.
Expert Commentary
The article's technical innovations and empirical evaluation are significant contributions to the field of natural language processing and human-computer interaction. However, the article's focus on the technical aspects of the framework and benchmark raises questions about its broader implications for human-computer interaction and the role of memory in dialogue systems. The development of ProStream and STEM-Bench may lead to improved dialogue systems with enhanced memory capabilities, potentially benefiting applications such as customer service chatbots and virtual assistants. Nevertheless, the article's findings highlight the need for policy considerations regarding the design and deployment of dialogue systems with advanced memory capabilities.
Recommendations
- ✓ Future research should focus on exploring the broader implications of ProStream and STEM-Bench for human-computer interaction and the role of memory in dialogue systems.
- ✓ The development of more diverse and comprehensive benchmarks for evaluating dialogue systems with advanced memory capabilities is necessary to ensure the generalizability of ProStream and other similar frameworks.