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Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Qizheng Zhang et al. · 2025 · arXiv 2510.04618 · Code · L2 · in the list

Treats the context as a playbook that the model grows and edits from its own results without collapsing it: +10.6% on agent benchmarks.

  1. 1Listed
  2. 2Collected
  3. 3Analyzed
  4. 4Replicated
  5. 5Audited

1

Listed

30 October 2026

Added to the list. How entries were checked when they were listed is in the journal.

2

Collected

7 October 2026 · by rsi-design study

FileSourceSHA-256
paper PDF, 2510.04618v32510.04618.pdf · 2.3 MBarxiv.org/pdf/2510.04618v351050ced82df75c143b151262d5af8763916968ca50374bd8ff778f40552b0ad

To check a file, download it from its source and compare its SHA-256 with the one above. Collected copies are kept in the editor's RSI system; manifest: design-life/literature/rsi-design/sources.json.

3

Analyzed

7 October 2026 · by rsi-design study

Quoted verbatim from design-life/literature/rsi-workflow-improvement/comparison.md (2026-10-07); design-life/research/rsi-design/evidence-review-2026-10-28.md (2026-10-28). Parts about applying the paper to the editor's private work are left out.

From “Evidence for improving reusable agent workflows” (2026-10-07)

Inspected edition: Agentic Context Engineering, v3, 29 March 2026; PDF pp. 4–7, 10

Mechanism and reported evidence: Generator, reflector and curator make focused playbook updates. AppWorld Table 1: with DeepSeek-V3.1, offline ACE without ground-truth labels averages 57.2 across the reported task/scenario completion metrics, versus ReAct 42.4; offline GEPA with labels averages 46.4.

From “Evidence for effective RSI implementation” (2026-10-28)

Design choice: Evolve the representation

Supporting evidence: AFlow searches explicit agent structures; ACE maintains useful, incremental knowledge records. AFlow, ACE

Challenge or limit: AFlow also changes prompts and control code. ACE largely changes contents within a fixed structure. Neither isolates semantic task-schema evolution as the dominant source of improvement.

The evidence also leaves other productive changes available. DGM changes agent tools and software under fixed task criteria. AIDE² changes search and context policies. ACE changes reusable knowledge contents. AFlow changes prompts and connections. These interventions overlap, but they should not all be relabeled as improvements to the semantic task schema. DGM

4

Replicated

Not done yet. Not attempted yet.

5

Audited

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The editor's view

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