EvoPathBench

Properties

axis: agentic
verification: mixed
whatItTests: A process-level evaluation benchmark for self-evolving agents that freezes evolving artifacts (memories, skills) at successive checkpoints while holding the base model and tools fixed, then separately tests three capabilities on held-out episodes of trading data: generalization to unseen tasks, retention after unrelated learning, and rule adaptation to new evidence, rather than scoring only final task-endpoint performance.
saturation: open
patternLabFit: Its checkpoint-based decomposition (generalization vs retention vs adaptation, scored separately) is a template for diagnosing why a Pattern Lab pattern that accumulates persistent artifacts across rounds degrades, rather than only observing that passRate dropped; the finding that self-generated candidate updates have larger held-out potential than what actually gets selected flags a selection-mechanism failure worth checking in any Pattern Lab pipeline that lets an agent choose its own memory updates.
source: Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents (EvoPathBench, arXiv:2609.24663)
notes: Submitted 2026-09-21, within the discovery window. Distinct from every tracked entry: no tracked benchmark isolates generalization, retention, and rule-adaptation as separately-scored capabilities of a self-evolving agent's own accumulated artifacts over checkpointed episodes. Domain is public trading data. CAUTION FOR VALIDATOR: this entry was built from a summarized rather than a full verbatim abstract; the benchmark name (EvoPathBench) and the exact grading method (deterministic financial outcome vs judge) should be re-verified against the full paper text before merge. Confidence held low pending that re-check.

Provenance

Source: https://arxiv.org/abs/2609.24663

Properties

axis

agentic

verification

mixed

whatItTests

A process-level evaluation benchmark for self-evolving agents that freezes evolving artifacts (memories, skills) at successive checkpoints while holding the base model and tools fixed, then separately tests three capabilities on held-out episodes of trading data: generalization to unseen tasks, retention after unrelated learning, and rule adaptation to new evidence, rather than scoring only final task-endpoint performance.

saturation

open

patternLabFit

Its checkpoint-based decomposition (generalization vs retention vs adaptation, scored separately) is a template for diagnosing why a Pattern Lab pattern that accumulates persistent artifacts across rounds degrades, rather than only observing that passRate dropped; the finding that self-generated candidate updates have larger held-out potential than what actually gets selected flags a selection-mechanism failure worth checking in any Pattern Lab pipeline that lets an agent choose its own memory updates.

source

Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents (EvoPathBench, arXiv:2609.24663)

notes

Submitted 2026-09-21, within the discovery window. Distinct from every tracked entry: no tracked benchmark isolates generalization, retention, and rule-adaptation as separately-scored capabilities of a self-evolving agent's own accumulated artifacts over checkpointed episodes. Domain is public trading data. CAUTION FOR VALIDATOR: this entry was built from a summarized rather than a full verbatim abstract; the benchmark name (EvoPathBench) and the exact grading method (deterministic financial outcome vs judge) should be re-verified against the full paper text before merge. Confidence held low pending that re-check.