memory

This paper introduces MERIT, a training-free agent that uses causal episodic memory of past repair outcomes to improve subsequent Text-to-SQL generations, boosting execution accuracy on Spider and BIRD benchmarks.

ContextWeave is a new longitudinal benchmark that evaluates whether recalled memory improves downstream agent performance in realistic office-work streams, using privacy-preserved multi-month workflows of 14 participants to create 1,005 executable tasks.

Mem Agent now lets users see its internal tracking of projects, tasks, reminders, and routines, offering transparency to help align the AI assistant's mental model with the user's world. Available in the Mem Proactive plan.

Hansel is a product that helps you remember everything you've worked on, likely a memory or productivity tool.

PI-Mem is a parallel-iterative memory mechanism that pushes long-context reasoning to 3.6M tokens, outperforming recurrent-memory baselines while achieving significant inference speedups.

MemArena is a new ego-centric benchmark for evaluating on-device personal memory assistants, using a MASim agent simulator to generate multi-session conversational worlds and ground truth across recall, reasoning, and trustworthiness dimensions. Initial results show memory-backend choice often matters more than reader scale, and permission-aware access remains a universal challenge.

SK Hynix introduces CMM-Ax, an ASIC-based CXL-PNM solution developed with Marvell Technology, designed to overcome memory bottlenecks in long-context LLM inference, achieving up to 5.5× higher throughput than GPU-only systems.

The author argues that AI agent recall is largely solved but permission/authorization over retrieved memories is the missing layer, introducing Provem, an open-source governance layer between agents and memory stores with benchmarks showing it eliminates compliance violations.

This paper introduces MemoryForge, a framework for synthesizing lifelong autobiographical memory from brief target personas to enable frozen LLMs to exhibit more human-like behaviors in role-play and user-simulation, outperforming descriptive conditioning baselines.

Introduces PAST-Bench, a benchmark for evaluating whether personal AI agents improve from retained experience across sessions, and Hermes+, an extension with targeted interventions. Finds improvement is real but uneven across capabilities and models.

The article describes a simple AI agent memory system using four markdown files, an index, and freshness-tracked caches, avoiding vector databases and retrieval pipelines.

A developer building a production chatbot for a yoga studio (therapy, health coaching, booking) seeks advice on choosing between Llama 70B, GPT-OSS 120B, and Claude, and discusses memory architecture, latency, and cost tradeoffs for ~200 users on Groq/OpenRouter.

MemHarness proposes changing Agent memory from simple replay to reconstruction based on the current state, trained end-to-end with GRPO, significantly improving success rates on ALFWorld and WebShop.

The author shares how their AI coding agent ignored an instruction to keep a project on SQLite and tried to sneak in Postgres. They built two local agents sharing one memory—one logs decisions, the other reviews new code against past decisions—and it caught the violation instantly, fully on-device.

Memtensor Research Group released Metis, a family of LLMs (4B/9B/27B) that internalize memory into the backbone, eliminating external RAG. The model performs memory read/write in a single forward pass and deploys with frozen weights like a standard LLM.

This paper introduces CoMem, a method that exploits the depth-wise division of labor in LLMs to cache intermediate residual tensors and recompute only upper layers for retrieval, enabling bounded read compute and memory independent of stored-context length. Evaluated on Qwen3-8B, CoMem achieves strong long-context performance with significant memory savings and prefill speedups.

ChronoMem introduces a semantic version-control layer for LLM agent memory, enabling whole-memory snapshots, natural-language rollback via hybrid retrieval, and counterfactual evaluation. It is the first open-source system and benchmark for global memory rollback in LLM agents.

A speculative essay on the future of AI assistants, envisioning a central assistant that integrates with third-party apps, creates personalized apps, and proactively manages workflows via continuous conversation and memory.

Microsoft Research introduces EvoLib, a framework that enables LLMs to continually learn from their own experience during inference by extracting reusable skills and insights, without model updates or external labels.

This paper presents the first systematic exploration of filesystem-based memory for LLM agents, formalizing roles of management, search, and execution agents around a shared memory store. It finds that organization primarily reduces retrieval cost but does not yet improve answer quality, and that tooling choices affect store shape as much as model selection.