repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 27d ago
mirror29/inalpha
🦊 Open-source professional quant agent framework. Agents pick the factors working now to time entries, write full strategies, and evolve them in a sandbox — every order through machine approval, the LLM never on the order path. Multi-market, audit-grade.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 56%Alibaba's model never trained as an agent — and improved agent performance across seven benchmarks →
- PossiblePossibly related (embedding) · 56%Best Local Agents - Jun 2026 →
- PossiblePossibly related (embedding) · 55%Open-source agent framework crosses 50k stars →
- PossiblePossibly related (embedding) · 54%PACE: A Proxy for Agentic Capability Evaluation →
- PossiblePossibly related (embedding) · 53%Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use →
- PossiblePossibly related (embedding) · 46%Collection policies | From Theory to Application: Advances in Multi‑Agent Systems/Frameworks - Nature →
- PossiblePossibly related (embedding) · 57%Building Open Source Agentic Models — Will Brown, Prime Intellect｜AI Engineer - finance.biggo.com →
- PossiblePossibly related (embedding) · 52%Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs →
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Related across the graph
newsOpen-source agent framework crosses 50k starsnewsCollection policies | From Theory to Application: Advances in Multi‑Agent Systems/Frameworks - NaturepaperPACE: A Proxy for Agentic Capability EvaluationpaperEvidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel ProofsnewsAlibaba's model never trained as an agent — and improved agent performance across seven benchmarksnewsBuilding Open Source Agentic Models — Will Brown, Prime Intellect｜AI Engineer - finance.biggo.compaperCan Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool UsenewsBest Local Agents - Jun 2026