Anthropic发布Agent记忆组件研究：梦境计算与显著性判断

Søren Kierkegaard wrote that life can only be understood backward, but it must be lived forward. We’ve encountered a similar bind when designing memory for AI agents.

As humans, we encode memory constantly as we go about our lives. The hippocampus plays a central role in this, capturing experiences in the moment so that memories form quickly and are immediately available. But only some experiences are consolidated into long-term knowledge.

Much of that consolidation appears to happen when our brains are offline. As we sleep, and during quiet rest, the hippocampus replays our recent experiences. This is thought to gradually distill those experiences and integrate them into the neocortex—the part of our brains that stores most of what we think of as knowledge.

Agent memory can use a similar division of labor. Agents capture much of their work in logs or transcripts. Researchers at Letta and UC Berkeley proposed sleep-time compute as a way for models to use idle time between interactions to reorganize their existing context. The same idea can help agents organize their memory by reflecting on logs to fix mistakes or produce new notes and representations. This process is often called dreaming (Figure 1).

We’ve found that dreaming has been a useful tool for helping us give Claude effective memory. In this post, we share how we’ve come to think about agent memory and walk through several evaluations we’ve used to explore agent memory, including the use of dreaming, and what each component contributes to memory organization.

Claude can use dreaming to organize memory. This can correct mistakes, create notes, build new memory representations, or shape what’s preemptively loaded into future sessions.

Memory notes

By “agent memory,” we mean knowledge that Claude gains from experience—including interactions with users and tasks it’s been assigned—and that it retains between sessions, saved as context it can read later. It excludes other ways models can learn or manage context, such as changes to their weights (the internal parameters set during training), in-context learning (knowledge gained within a single conversation), and compaction (summaries written by Claude that replace its older turns so it can continue working in a new context window).

In the human brain, chemical signals released before, during, or after an experience can mark a memory as important. Marked memories tend to be replayed more often than unmarked ones during offline periods. This process, called replay, helps stabilize and integrate them into long-term memory.

Similarly, Claude can tag salient events in a session log as they happen, so they’re easier to find later, or it can write them to a memory store, such as a folder of memory notes (Figure 2). But how can Claude tell what’s salient in the moment, before it knows what’s coming next?

Claude can write notes to memory.

Memory can be a prediction about what the future will need

Memory is, occasionally, a prediction. By writing down a rule, an agent predicts that the pattern it’s describing will be relevant again. For instance, writing down a verified fact is a prediction that an agent in a future session will want to rely on that fact without double-checking it. Over time, Claude has become much better at making these kinds of predictions.

Consider a task from Continual Learning Bench (CL-Bench) 1.0, a benchmark that measures an agent’s ability to use memory in order to tackle long-horizon tasks. One particular task asks Claude to answer a series of questions by looking up information in a database. After Claude gives an answer, it’s told the correct solution and can write a memory to help answer future questions.

Memory can support a learning loop here. If Claude gets a question wrong, it can record the failure, come up with theories about what went wrong, test those theories, and turn what it finds into a rule about how to answer in the future.

We ran the CL-Bench 1.0 task using Claude Managed Agents. Claude started each question in a fresh session, with one exception: it could retrieve files from a memory directory that persisted from one session to the next.

Different models achieved different levels of success at creating their own learning loops. Claude Sonnet 4.6 got as far as generating theories about why an answer was wrong. Its memory directory contained a scattering of files, each listing several untested guesses.

CL-Bench database task, comparing memory with a stateless baseline across models.

Claude Opus 4.7 consolidated its notes into a single reference document and began marking which facts it had checked and which were still unconfirmed. Claude Sonnet 5 marked each answer as confirmed after the grader had checked it, and wrote a correction when it had answered wrong.

Claude Opus 5 was the first of these models to close the loop. Most of its entries included a rule (and also how it knew the rule was true), answers to earlier questions, and clear warnings about traps it had discovered along the way (Figure 3).

Like Opus 5, Claude Opus 5.5 and Claude Sonnet 5.5 both closed the loop, using memory to capture validated rules about how to best answer future questions. With memory, both scored about the same as Opus 5, though their baseline performance without memory was slightly higher.

Claude has learned to judge salience

We also evaluated Claude’s ability to play Pokémon Red, a game that challenges Claude to navigate complex long-horizon tasks, as a test of Claude’s judgment about what to save to memory. At the start of each run, we gave each model a folder of memory files that it could read from and write to while it played (Figure 4).

We compared the memory folders from one run each of Claude Sonnet 3.7 and Claude Fable 5, when each was about 10,000 steps into the game. A key difference we noticed was what each model thought was worth remembering. Sonnet 3.7 assumed everything might matter and wrote to memory indiscriminately. Its memory folder contained an archive of encounters, often with a separate file for each battle it fought, each item it found, and each non-player character it met. Ultimately, Sonnet 3.7 wrote 413 memory files across 225 folders and earned one badge (badges mark a player’s progress through the game).

Fable 5, by comparison, wrote nine files in two folders, plus a single top-level file called global.md, and earned four badges.

Claude’s memory directories for Sonnet 3.7 versus Fable 5 while playing Pokémon.

Sonnet 3.7’s approach was a problem mainly because the game state of Pokémon changes constantly—Sonnet 3.7’s detailed record was continuously going out of date. As a result, as Claude progressed, it had to spend more time and resources combing through its memories rather than playing the game. Around step 10,000, for example, Sonnet 3.7 started its fourth folder of “master” memory files. Over the next 17 steps, it created six files, deleted nine others, and took no actions in the game itself.

Fable 5’s approach used memory for things the game didn’t explicitly tell it. Some of the kinds of information Sonnet 3.7 wrote to memory—such as how much money the player has or where the player is on the map—can be read off the screen every turn, so storing it is redundant. By contrast, memory files about strategy, plans, and tested rules exist only if Claude works them out, and they disappear unless Claude writes them down.