HFEPX · Eval paper review

ADIAS: Automated Design of Interactive Agentic Systems

Lekang Jiang, Bohan Tang, Stephan Goetz, Yiwen Guo

Published

Aug 3, 2026

Citations

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 17, 2026

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Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

The available metadata is too thin to trust this as a primary source.
The abstract does not clearly describe the evaluation setup.
The abstract does not clearly name benchmarks or metrics.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal

Detected

From extracted signals

Evaluation signal

Weak or implicit

Validate from full paper

Usefulness for eval research

40/100

Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

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Abstract

Self-evolving agents can improve without updating model weights by iteratively optimizing the harness based on evaluation feedback. Existing methods are largely candidate-centric: they record evolution outcomes (which agent variants have been produced and how they perform), rather than evolution progress (which issues, i.e., recurring failure patterns, have been fixed and which remain). Such methods pose two difficulties for cumulative harness evolution. First, repair context is scattered across candidate records and must be reconstructed every round, which leads to inefficient, redundant, or misdirected revisions. Second, because candidates are evaluated as a whole, partial fixes across different agents are difficult to combine, while ineffective changes persist alongside beneficial ones. To address these problems, we formulate issue-centric agent optimization, which explicitly carries forward evolutionary progress through a persistent issue state. We instantiate this paradigm in ADIAS, a harness-evolution framework with two key components. An Issue Manager maintains the issue state with stable issue identities, lifecycle status, supporting evidence, and intervention-outcome histories. An Issue-Guided Optimizer proposes repair targets from the issue state and performs focused harness revision. On five interactive benchmarks, ADIAS outperforms the strongest baseline by 25.2% on average and achieves consistent gains across four backbone models. Controlled ablations show that removing the Issue Manager or the Issue-Guided Optimizer reduces performance by up to 40.7%.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.