Tag
CABLE is a plug-in augmentation that extends LLM memory retrieval by constructing complementary antecedent-based links to surface implicit supporting evidence, evaluated on benchmarks with higher mean scores across multiple systems.
Introduces ERSkill, a retrieval-centric framework for self-evolving, skill-guided adaptive memory access in LLM agents. It co-evolves retrieval skills and a routing policy, substantially outperforming strong baselines across agent memory benchmarks.
This paper compares semantic search dynamics between humans and LLMs using verbal fluency data, finding that humans exhibit more variable and exploratory search patterns that current models fail to reproduce.
An article discussing how AI agents often repeat mistakes because their memory retrieval mechanisms prioritize semantic similarity over effectiveness, leading to flawed decision-making.
ConvMemory v3 introduces a validity context layer that detects outdated or superseded conversational memories using a target-conditioned dual-evidence gate, achieving high accuracy on synthetic benchmarks and zero-shot transfer to role binding tasks.
PrecisionMemBench is an open-source benchmark that tests retrieval precision as a strict unit test, revealing that popular memory frameworks like Mem0, Zep, and Hindsight have very low precision (0.05-0.09) and rely on LLMs to compensate. The article argues for zero-tolerance hard fail on precision for production memory infrastructure.
This paper proposes a unified framework for memory access and selection in long-context dialogue systems, using Bayes factors to quantify the utility of historical turns for modeling changing user preferences. Experiments show it outperforms embedding-based retrieval on preference-intensive tasks.
A preprint on SSRN presents PHI // DRIFT, a cognitive middleware architecture for AI companions with persistent internal state and salience-weighted memory retrieval, claiming 14.8% more context per prompt versus cosine-only RAG on consumer hardware.
H-Mem is a novel memory mechanism for LLM-based agents that uses a hybrid structure combining a temporal and semantic tree with a knowledge graph to model memory evolution and improve retrieval, achieving state-of-the-art performance on QA benchmarks.
A novel memory retrieval system inspired by episodic memory theory achieves state-of-the-art 96.4% top-50 accuracy on the LongMemEval benchmark using Gemini Flash, outperforming larger Pro-based baselines by isolating retrieval quality from model capability.
This paper introduces PYTHALAB-MERA, an external controller for frozen local LLMs that uses validation-grounded memory and retrieval to improve coding agent performance. It demonstrates superior success rates in strict validation tasks compared to self-refinement baselines by leveraging execution feedback and temporal difference learning.
HAGE introduces a weighted multi-relational memory framework that enables query-conditioned traversal over unified relational memory graphs, improving long-horizon reasoning accuracy through adaptive memory retrieval and reinforcement learning-based optimization.
The author introduces Tiro, an open-source agentic memory and retrieval framework designed to solve long-term context drift in LLM agents by providing modular, inspectable memory lanes for sessions, documents, and operational state.
UnionPay researchers propose SCG-MEM, a schema-constrained generative memory architecture that eliminates structural hallucinations by forcing LLMs to decode only valid memory keys within a dynamic cognitive schema, outperforming dense-retrieval baselines on the LoCoMo benchmark.