arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely a...
The paper introduces LOCOMO-CONV, a conversational memory benchmark that expands on the existing LoCoMo dataset with four query styles—dialog, implicit, counterfactual, and composed—designed to evaluate memory systems in realistic conversational settings. Experiments across five memory systems reveal that conversational framing uncovers significant retrieval gaps missed by traditional QA benchmarks, particularly for implicit and composed queries, and that strong retrieval does not necessarily translate into higher response quality. The study also highlights silent grounding in implicit queries, where memory enhances contextual grounding without explicitly presenting the gold fact, suggesting a need for reasoning-based memory elaboration.
arXiv:2607. 29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.
The paper introduces QGMem, a memory framework for conversational agents that organizes dialogue history into event-indexed atomic units and consolidates related units into dynamic memory traces. When a query is posed, QGMem retrieves complementary candidate memories, reranks them to form a compact working memory, and represents this working memory as a local graph to expose relational dependencies. Experiments on six benchmarks demonstrate consistent improvements in retrieval, multi‑hop evidence composition, conflict resolution, and ultra‑long dialogue reasoning while keeping context size and inference cost moderate.
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.
AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously.
The paper introduces T$^2$Mem, a Test-Time Memory construction framework that defers memory building to inference, allowing large language models to adaptively retrieve and organize episode-level metadata and raw interactions during reasoning. By extracting lightweight metadata offline and progressively constructing query-specific memory at test time, T$^2$Mem reduces overhead and improves performance on long-horizon tasks. Experiments on LoCoMo and LongMemEval demonstrate consistent gains over strong baselines across various backbone LLMs.
arXiv:2606. 13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks.
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
arXiv:2607. 05794v1 Announce Type: new Abstract: Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence.
ETAMem is a multi‑view memory framework designed to improve long‑term conversational agents by decoupling heterogeneous information and aligning memory representations with distinct characteristics. It parses conversational data into atomic units, identifies affective content, and organizes these units into three complementary hierarchical views—Event, Affect, and Time—using adaptively calibrated thresholds. Retrieval is performed via entropy‑based allocation of query budgets across views, enabling hierarchical search within each view. Experiments on LoCoMo and LongMemEval‑S show that ETAMem surpasses state‑of‑the‑art baselines with relative accuracy gains of 12.46% and 7.96%, while remaining token‑efficient, and analyses confirm the complementary benefits of the different memory views.
arXiv:2606. 04442v1 Announce Type: cross Abstract: AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents.