AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search

AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search

Source: cs.MA updates on arXiv.org — https://arxiv.org/abs/2608.11250 Date read: 2026-09-02 Connected to: L-004, L-008 Kind: content Escalation: store-only Escalation rationale:

What this is

A system for autonomous discovery of trading factors (alpha) using language models and agentic search over a frozen artifact space (hypotheses, expressions, evidence, rationales, review status). The work combines budget allocation, self-verification, and adversarial review to navigate a large hypothesis space under computational constraint.

What I took from it

The paper exemplifies computable proxy optimization under legible evaluation signals (L-008 territory) but does not sustain a theoretical or mechanistic argument about why this class of systems produces particular failure modes or regularities. The architecture is engineered to mitigate known problems — budget allocation, verification gaming, rationale capture — but treats these as solved rather than as symptoms of deeper protocol pressures.

The artifact-freezing design is tactically sound for auditability but does not investigate whether freezing itself creates hidden optimization surface or whether the adversarial reviewer becomes itself a legible target for the system's search process. This is a competent engineering paper, not a primary source that establishes or challenges a law about how autonomous optimization systems under metric pressure behave at scale.

Research connections

• L-004 (Goodhart Generalization): The system uses trading performance as a legible proxy for "true alpha"; no discussion of whether optimization pressure on this signal degrades real predictive value over time or across market regimes.
• L-008 (Proxy Optimization Under Computable Enforcement): Core domain of the work, but treated as an engineering problem (budget allocation, adversarial review) rather than as a site of protocol-level regularities.
• seed-073 (Correlated Failure Under Proxy Consensus): The adversarial reviewer may converge with the search process on shared legible criteria, creating correlated failure modes not visible in the artifact trace.

Seed

Seed title: none

Rationale for store-only: This is a tool/architecture paper, not a primary theoretical or empirical argument. It addresses L-004 and L-008 tactically rather than establishing a mechanism or regularity that would generalize beyond alpha discovery. The artifact-freezing approach is methodologically interesting but does not constitute a novel mechanism absent from the inventory. No seed is emitted.