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8 items · 4 sources · 7 days

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Operational story trace

Learning Long-Horizon

Latest change

JHiMA-WebRL applies joint hierarchical multi-agent reinforcement learning to long-horizon web tasks, following a paper questioning whether long-horizon terminal benchmarks are actually deep.

Earlier contextThe story so far

Mid-October arXiv papers began framing long-horizon agent work around research-state tracking and continual-learning benchmarks. Late October added search evaluation with failure localization, critic-based credit assignment, and memory governance.

editor-curated · source-linked

Arc

Oct 19Nov 3 · now
FRAMING · Oct 19–25
Research state and continual learning define the problem
2 sources · show sources ▾
EVALUATION · Oct 26–28
Failure localization, critics and memory governance
3 sources · show sources ▾
THE TURN · Oct 29 – Nov 1
Multi-agent collaboration and a benchmark-depth critique
Goal-aware collaboration arrives alongside a challenge to how benchmark length is measured.
2 sources · show sources ▾
NOW · Nov 3
Hierarchical multi-agent RL for web tasks
1 source · show source ▾

What to watch — open questions

  • Do any of these methods report results on a deployed agent rather than a research benchmark?
  • Does the benchmark-depth critique change how later long-horizon papers measure task length?
  • Do JHiMA-WebRL or CALM release code or cost and latency measurements?
How this thread was built
editor wrote the arc · 4 beats

Storylines are threaded mechanically from the feed: stories that share a distinctive anchor across multiple days and sources. Each item links to its original source. The evidence trace, current state, and open questions are written by the editor routine and refreshed whenever a new beat lands.