User facts are internalized as surgical local edits to a hash-keyed Engram memory table with reasoning skill held in a shared adapter, claimed to match LoRA recall, improve indirect reasoning 5.6x on average, and compose across users with 33,000x smaller footprint than per-user adapters.
A-mem: Agentic memory for llm agents.Advances in Neural Information Processing Systems, 38:17577–17604, 2026a
5 Pith papers cite this work. Polarity classification is still indexing.
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PERMA is a new benchmark using temporally ordered events, text variability, and linguistic alignment to evaluate LLM memory agents on persona consistency beyond simple retrieval.
Training a 9B LLM agent with reinforcement learning to actively navigate a structured multi-granularity memory pyramid yields competitive performance on memory-intensive benchmarks while preserving non-memory capabilities.
Human data reveals LLMs struggle at extracting attributes from real conversations, selecting relevant ones, and generating responses humans rate no better than generic ones, with reward models showing only modest human correlation.
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
citing papers explorer
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User as Engram: Internalizing Per-User Memory as Local Parametric Edits
User facts are internalized as surgical local edits to a hash-keyed Engram memory table with reasoning skill held in a shared adapter, claimed to match LoRA recall, improve indirect reasoning 5.6x on average, and compose across users with 33,000x smaller footprint than per-user adapters.
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PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments
PERMA is a new benchmark using temporally ordered events, text variability, and linguistic alignment to evaluate LLM memory agents on persona consistency beyond simple retrieval.
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From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
Training a 9B LLM agent with reinforcement learning to actively navigate a structured multi-granularity memory pyramid yields competitive performance on memory-intensive benchmarks while preserving non-memory capabilities.
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Re-Centering Humans in LLM Personalization
Human data reveals LLMs struggle at extracting attributes from real conversations, selecting relevant ones, and generating responses humans rate no better than generic ones, with reward models showing only modest human correlation.
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Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.