PreCoMem introduces a memory consolidation framework for long‑horizon dialogue agents, inspired by predictive coding. It represents user memory as confidence‑weighted beliefs and uses an Effective Surprisal signal to decide whether to maintain, profile, or correct each belief. Experiments on LoCoMo, LongMemEval‑S, PersonaMem‑v2, and the new DiMoBench demonstrate state‑of‑the‑art accuracy and robust adaptation to non‑stationary belief drift without over‑reacting to noise.
arXiv:2610.08586v1 Announce Type: new Abstract: Long conversational agents have become essential in our daily lives. They must remember what was said long back in order to help us efficiently complet...
Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States introduces PoS, an inference-time framework that builds and maintains explicit belief states to guide large language model agents. Each belief state combines an estimate of the current world with unresolved task requirements, making clear what the agent still needs to learn and accomplish. PoS validates consistency, monitors task progress to detect Belief Trapping, and tailors recovery to the trapping pattern and unresolved requirements, achieving top performance across four benchmarks with all three LLM backbones.
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
The paper introduces a hybrid memory system for long-horizon dialogue agents that combines episodic memories with structured relational memories, supported by a consolidation mechanism to maintain efficiency. Experiments on the LoCoMo and LongMemEval-S benchmarks show that the two memory types complement each other, outperforming existing memory frameworks with 90.2% accuracy on LoCoMo and 85.5% on LongMemEval-S.
arXiv:2512. 20111v2 Announce Type: replace-cross Abstract: As the time horizons of sequential decision-making tasks grow, keeping full interaction histories in model context becomes increasingly costly.
arXiv:2609.17010v1 Announce Type: new Abstract: Lifelong conversational agents rely on memory systems to maintain deep, context-aware interactions with users. However, existing explicit textual memor...
arXiv:2605. 13438v3 Announce Type: replace Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure.
arXiv:2608. 02515v1 Announce Type: cross Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context.
arXiv:2607. 23927v1 Announce Type: new Abstract: A conversational AI that cannot tell its own output from what a user said will treat its own mistakes as user-provided facts.
UTILMEM is a new diagnostic benchmark that tests how conversational agents use long‑term memory, focusing on reasoning over dense histories, spotting implicitly relevant memories, synthesizing distributed evidence, and resisting interference from similar distractors. It contains 1,717 instances across five domains and evaluates a range of retrieval‑based and memory‑augmented systems. The study shows that strong performance on traditional factual recall does not guarantee effective memory utilization, highlighting a gap between retrieving information and integrating it into coherent, task‑oriented outputs.
The paper introduces HiPS, a hierarchical strategy co‑evolution framework for memory‑augmented agents that separates memory management into a globally shared foundation and a user‑specific adaptive tier. HiPS uses a Universal Strategy to capture shared principles from cross‑persona trajectories, Persona Delta Distillation to create tailored rules for users deviating from general patterns, and Cross‑Level Rule Flow to dynamically adjust the boundary between global and personal rules. Experiments show that this approach consistently outperforms existing memory‑augmented baselines.
DiaLong is a new long‑context dialogue benchmark that tests transformer‑based Large Language Models’ ability to retain and recall memories. The dataset contains 100 human‑created conversations, expanded with LLMs to exceed lengths of existing datasets, and includes a fact‑recall task where models must classify 40 candidate facts as true or false. Experiments show current LLMs and memory‑enhancement methods still struggle, indicating a need for improved memory performance in extended conversations.