REVIEW 5 cited by
From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic, tree-structured memory representation to optimize the organization, retrieval, and integration of information, akin to human cognitive schemas. MemTree organizes memory hierarchically, with each node encapsulating aggregated textual content, corresponding semantic embeddings, and varying abstraction levels across the tree's depths. Our algorithm dynamically adapts this memory structure by computing and comparing semantic embeddings of new and existing information to enrich the model's context-awareness. This approach allows MemTree to handle complex reasoning and extended interactions more effectively than traditional memory augmentation methods, which often rely on flat lookup tables. Evaluations on benchmarks for multi-turn dialogue understanding and document question answering show that MemTree significantly enhances performance in scenarios that demand structured memory management.
Forward citations
Cited by 5 Pith papers
-
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control
A two-tier private/shared memory system with provenance-based access control reduces redundant queries in multi-user LLM agent teams by up to 61 percent without losing accuracy.
-
TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents
TrajWiki stores long-dialogue facts as evolving, source-linked claim histories organized into wiki pages, improving long-horizon QA on LoCoMo and MedMT-Bench in reported experiments.
-
Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory
An LLM agent that builds a tool-transition graph with state summaries from past experience improves tool selection and RL exploration by large margins on multi-turn benchmarks.
-
Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
A learnable memory cycle with adaptive retrieval, merging, and storage, trained online, improves LLM agent accuracy on HotpotQA and MemDaily for most backbones.
-
RGMem: Renormalization Group-inspired Memory Evolution for Language Agents
RGMem, a hierarchical memory framework with thresholded updates inspired by renormalization group ideas, reports state-of-the-art scores on the LOCOMO long-term conversational memory benchmark.
Discussion (0). Continue with ORCID to comment.