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Notes on memory
17 notes
Use and Disuse: Intent-Structured Experience Consolidation for Memory and Learning in LLM Agents
The preprint proposes Hippocam, a hierarchical memory and continual-learning architecture that organizes ongoing work as nested intents. Active context stays centered on the current intent, while completed intents are…
Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval
The preprint identifies a retrieval-level positivity problem: memories that are never retrieved cannot have their utility identified by store-level interventions. It proposes Causal Memory Policy, which reserves…
VISTA: A Visual Harness for Reasoning in an Interactive World
The technical report introduces a visual harness that gives a multimodal model long-horizon visual context through direct perception, lossless visual memory, active retrieval, and input reorganization. The abstract…
Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
The preprint introduces an inference-time harness in which a controller explicitly decides what partial work to reuse, whether to restart, when to stop, and which next work to dispatch. Workers perform task-level…
Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents
The preprint describes artifact-mediated propagation, in which adversarial content inserted into a shared artifact is stored in one assistant's persistent memory, reproduced in a later artifact, and acquired by another…
Mutable Transcripts: Mitigating Context Pollution through Editable Conversation State
The preprint proposes allowing users to revise prior conversation turns through natural-language edit requests, treating the transcript as an editable representation of conversational state rather than an immutable…
C3M: Cross-Session Multimodal Memory Maintenance for Long-Horizon Tasks
The preprint proposes bounded query-blind memory for long-horizon tasks. It describes relation-aware updates for complementary and incompatible records, budgeted routing that selects index pages and expands source…
AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
AutoViewMem discovers low-overlap semantic views from interaction traces, uses a compact complementary view set for structured write-time extraction, and grounds memories in provenance. It moves semantic…
Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design
Designer-RSI uses a frozen frontier model operating professional design software through more than 230 tools and maintains an external procedural-memory bank of natural-language skills. The memory widens by adding…
MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Researchers have introduced a new benchmark called MemTrapBench to test the performance of large language models (LLMs) in terms of cognitive traps caused by their memory components. The study identifies two types of…
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
Researchers have developed an adaptive memory and reflection multi-agent system for medical question answering. This system, which uses specialized agents with dedicated memory and feedback, aims to improve the…
ContextWeave: A Real-World Workflow Benchmark
Researchers introduce ContextWeave, a new benchmark for evaluating the performance of language agents in long-horizon, stateful workflows. The benchmark measures how well an agent can recall and apply past experiences…
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
The authors propose HiGram, a hierarchical graph memory framework for long-term reasoning agents. This framework aims to efficiently update memories over time by organizing them into a coarse-to-fine architecture…
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
The authors propose HiGram, an evolving hierarchical graph memory framework that aims to improve the efficiency and effectiveness of long-term reasoning agents by reducing irrelevant information during retrieval and…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks in which an action agent must make decisions based on state information scattered across an expanding trajectory. The proposed memory agent updates a…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks where decision-relevant information can get buried as trajectories grow. The agent updates a structured memory bank from the recent trajectory and…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Researchers have proposed a new benchmark called MemSyco-Bench to evaluate how memory in large language models (LLMs) can lead to sycophancy, where agents over-align with the user at the cost of factual accuracy or…
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