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MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design

-cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and…

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Why am I seeing this Ranked on source trust — arXiv

It ranks mainly on source trust: arXiv is the most reliable outlet we track on this subject, and is the only one on the story so far.

It clears the bar without leading strongly on any one factor. A middling score is not a claim that the story is important — only that nothing about it is weak.

Link-outLink-out, because it scores 0.40, below the 0.50 bar for a write-up. Link-out means we point at the publisher and say nothing of our own.

Blended score 0.398 — every figure below is computed, none of it is editorial.
FactorWeightScore ContributionWhere it came from
Corroboration 0.35 0.39 +0.135 34% 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true.
Source trustleads 0.25 0.85 +0.212 53% arXiv is the highest-trust source on this story and is first-party — the organisation announcing its own news. Trust is taken from the best source, not averaged.
Pickup rate 0.20 0.00 +0.000 0% One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate.
Freshness 0.20 0.25 +0.050 13% Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent.

Corroboration counts distinct organisations, once each, and only from tiers 1 and 2. Freshness halves every 10 hours, so this ranking is a snapshot and will differ at the next build.

SalehRokon · CC BY-SA 4.0 · Wikimedia Commons · illustrative
Read the full article at Arxiv →

What happened

-cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthesis. In LLM-based AHD, the LLM reasons about algorithm design and generates executable heuristic code. Existing architectures adopt two main paradigms: Natural Evolution applies crossover and mutation to this code to explore diverse strategies, but discards the reasoning traces behind the design decisions, weakening knowledge retention; Metacognitive Evolution retains these reasoning traces and refines them through reflection, but lacks population-level recombination, limiting exploration. These limitations reduce search efficiency, stability, and solution quality on complex problems.

1independent orgs
40story score
0velocity
85source trust
4passes seen

How this story arrived

Ordered by when each source was first observed, which is what the velocity figure is computed from. Publishers backdate; observed order does not.

  1. 01 Arxivfirst-party first seen MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Desig

Overclock clusters coverage from independent sources and grades it automatically. The figures above are computed, not editorial. This page summarises and links to reporting by the outlets named — follow the links for the original work.