Practical research tool

Progressive Cramming

Progressive cramming is a staged approach to token compression that repeatedly reduces context while checking whether critical instructions and facts survive. Instead of making one aggressive summary, the process creates smaller representations over time and treats retention loss as a signal that compression has gone too far.

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Compress context without losing the constraints

A transparent extractive compressor that prioritizes requirements, numbers, goals, and deadlines.

Live result

47% smaller

239 characters remain from 451.

It must answer order-status questions, but it cannot issue refunds. Customer payment details must never be included in logs. Success means resolving 70% of order-status questions without escalation while keeping incorrect answers below 2%.

Compare progressive levels
75% retained

The team is launching a customer support agent in September. It must answer order-status questions, but it cannot issue refunds. The agent may use order history and shipping events. Customer payment details must never be included in logs. The initial pilot covers US English and runs for four weeks. Success means resolving 70% of order-status questions without escalation while keeping incorrect answers below 2%.

50% retained

It must answer order-status questions, but it cannot issue refunds. Customer payment details must never be included in logs. The initial pilot covers US English and runs for four weeks. Success means resolving 70% of order-status questions without escalation while keeping incorrect answers below 2%.

25% retained

It must answer order-status questions, but it cannot issue refunds. Success means resolving 70% of order-status questions without escalation while keeping incorrect answers below 2%.

Plain-language guide

What is progressive cramming for AI context?

Built and reviewed by Imran
Reviewed 26 July 2026

How does it work?

  1. Identify constraints, goals, numbers, deadlines, and other facts the final context must retain.
  2. Compress in stages and measure how much text or token volume is removed at each level.
  3. Stop or revise the compression when a critical fact disappears or changes meaning.

When is it useful?

  • Shrinking long agent histories before the next reasoning step.
  • Testing whether memory summaries preserve non-negotiable constraints.
  • Comparing context strategies under a fixed token budget.

Example: compressing a 24k-token support history

A useful compressed context should retain the launch date, permissions, refund restriction, privacy rule, and success criteria. A shorter summary that loses one of those items has a better compression ratio but a worse operational result.

What are the limitations?

  • The browser tool uses extractive heuristics and does not reproduce the paper’s learned compression method.
  • Retention checks are only as good as the list of facts and constraints selected for evaluation.

Common questions

Questions about Progressive Cramming

How is progressive cramming different from summarization?

It treats compression as a sequence of measured stages and explicitly checks retention, rather than producing one summary and assuming it is adequate.

What information should never be compressed away?

Non-negotiable rules, current goals, deadlines, identifiers, settled decisions, and facts that determine whether an action is safe or correct.

What is a good compression ratio?

There is no universal ratio. The right stopping point is the smallest representation that still preserves the information required for the next task.

One useful idea when the research moves. No noise.