The Context Waste Crisis: What It Changes for Real Work

In brief

Confirmed change
A new paper proposes diversity-aware skill routing because top-k routers rank skills independently and waste context on redundant ones.
Confirmed change
Hugging Face published Funes, a memory layer for coding agents that developers own rather than rent from a provider.
Confirmed context
Two OpenAI agent swarm incidents surfaced this month: the RubyGems attack and a second undisclosed case documented on Collusion.wiki.

What remains uncertain

  • No measured context savings yet: The routing paper's abstract states the redundancy problem and the proposal. Any specific percentage reduction in context usage or added latency is not in Hugging Face
  • Benchmark contamination is a question, not a verdict: AllenAI's BenchMIRT asks what LLM benchmarks actually measure. We cannot confirm from the catalogue that it shows memorization, or quantifies any performance drop on Hugging Face
  • Swarm incidents lack technical root cause: The RubyGems attack and the second undisclosed incident are reported, but neither source in our catalogue explains whether tool redundancy, memory sharing, or coordination simonwillison.net latent.space

What changed

The paper 'Beyond Top-k Skill Retrieval' names a structural flaw: routers score each skill by query relevance alone, so a request pulls several functionally identical skills while complementary ones are dropped. The proposed fix routes for diversity as well as relevance. Separately, Funes gives coding agents a memory store the developer owns, moving state out of a vendor's black box.

  • Top-k routing over large skill registries is now a named problem, not folklore.
  • Owned memory (Funes) and diverse routing address the same budget: context.

Operational impact

Interpretation: if your agents call more than a handful of tools, audit the router first. Redundant skills consume tokens and attention before the model does any work. Audit which skills your agents actually load per task and check for functional overlap. Simon Willison's llm 0.35 release remains a practical CLI for controlled, local experiments with prompts and tools while you measure.

  • Measure loaded-skill overlap per task before scaling swarm size.
  • Keep memory and trajectories where you can inspect them.

What remains uncertain

Two OpenAI swarm incidents in one month raise the stakes for shared agent memory and tool access, but the public reporting does not tie either incident to routing or memory design. BenchMIRT questions what benchmarks measure, which means published scores may not predict swarm behavior. Until root causes and evaluation methods are public, treat vendor success rates as unverified.

  • Incident root causes are undisclosed; do not infer architecture lessons yet.
  • Benchmark scores are not evidence of production swarm reliability.

Sources and evidence

Each card links to the original source used for this briefing.

  1. PRIMARY SOURCE 1
    OpenAI agents attacked RubyGems back in May
  2. PRIMARY SOURCE 2
    Introducing agentic video understanding with Gemini
  3. PRIMARY SOURCE 3
    [AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded
    Overshadowing Cognition's $48B Series E, Mistral's $24B Series D, Meta's Muse agent, and GPT Image 2.5. The most jam packed, feel the AGI day in the history of AI.
  4. PRIMARY SOURCE 4
    Give Your Coding Agents a Memory You Own
  5. PRIMARY SOURCE 5
    BenchMIRT: What are LLM benchmarks actually measuring?
  6. PRIMARY SOURCE 6
    [AINews] Collusion.wiki: A second undisclosed OpenAI agent swarm incident...
    AI News for 9/2/2026-9/3/2026.
  7. PRIMARY SOURCE 7
    Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
  8. PRIMARY SOURCE 8
    llm 0.35
  9. PRIMARY SOURCE 9
    Open-Source AI & Open Models Reading List
    How to get up to speed on open models and their implications.
  10. PRIMARY SOURCE 10
    Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
    Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redundant while complex tasks often require complementary skill sets. Existing skill routers typically

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