We try to solve a similar problem to put long documents in context. We built an MCP for Claude to allow you to put long PDFs in your context window that go beyond the context limits: https://pageindex.ai/mcp.

Plus, the model got trained and RLed with a continuous context, except if they now tune it with messing with the context as well.

https://manus.im/blog/Context-Engineering-for-AI-Agents-Less...

1. Multi-agent orchestration 2. Summarising and chunking large tool and agent responses 3. Passing large context objects by reference between agents and tools

Two things to note that might be interesting to the community:

Firstly, when managing context, I recommend adding some evals to our context management flow, so you can measure effectiveness as you add improvements and changes.

For example, our evals will measure the impact of using Anthropics memory over time. Thus allowing our team to make a better informed decisions on that tools to use with our agents.

Secondly, there's a tradeoff not mentioned in this article: speed vs. accuracy. Faster summarisation (or 'compaction') comes at a cost of accuracy. If you want good compaction, it can be slow. Depending on the use case, you should adjust your compaction strategy accordingly. For example, (forgive my major generalisation), for consumer facing products speed is usually preferred over a bump in accuracy. However, in business accuracy is generally preferred over speed.

For context: I have background in CV and ML in general. Currently reviewing and revising RL.

Any idea how I can get into RL?

I have 3 years of industry/research experience.

Whenever I see post like this, it triggers a massive fomo creating a scene of urgency on I should work in these problems.

Not being able to work here is making be anxious.

what does it take for someone in Non-US/Non-EU region to get into big labs such as these?

Do I really have to pursue PhD? I am already old that pursuing PhD is a huge burden that I can't afford.

Context editing: Replace tool call results in message history (i.e replace a file output with an indicator that it’s no longer available).

Memory: Give LLM access to read and write .md files like a virtual file system

I feel like these formalizations of tools are on the path towards managing message history on the server, which means better vendor lock in, but not necessarily a big boon to the user of the API (well, bandwidth and latency will improve). I see the ChatGPT Responses API going a similar path, and together these changes will make it harder to swap transparently between providers, something I enjoy having the ability to do.

Let's say I submit or let it create a piece of code, and we're working on improving it. At some point I want to consider the piece of code to be significantly better that what I had initially, so all those initial interactions containing old code could be removed from the context.

I like how Google AI Studio allows one to delete sections and they are then no longer part of the context. Not possible in Claude, ChatGPT or Gemini, I think there one can only delete the last response.

Maybe even AI could suggest which parts to disable.

Funny, I was just talking about my personal use of these techniques recently (tool output summarization/abliteration with memory backend). This isn't something that needs to be Claude Code specific though, you can 100% implement this with tool wrappers.

I've been doing this for a bit, dropping summarized old tool output from context is a big win, but it's still level ~0 context engineering. It'll be interesting to see which of my tricks they figure out next.

I'll wait for the day they will release a js library to programmatically do all this llm context juggling, instead of using a UI, and then I will adopt it by doing what I do now, writting code.

I will write code that orchestrates llms for writting code.

Edit: This is obviously a joke... but is it really a joke?

Outside of context manipulation, it'd also be nice to standardize a format to label sections of the context and later append keywords to the context to ignore / unignore those sections (or refer back to them, emphasize them, whatever). With that, I imagine we'd be able to have a pretty good set of standard LoRA adapters that enable all LLMs to be controlled in the same fashion. That way agents will be able to manipulate LLMs in a standard way, without having to rewrite context itself.