RAH improves coding performance on Oolong-Synthetic from 71.75% to 81.36% with fixed GPT-5 backbone by spawning subagent harnesses via executable scripts.
The Y-Combinator for LLMs: Solving Long-Context Rot with λ-Calculus,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
Design-time Hindley-Milner unification over finitely generated abelian groups is claimed to verify AI model reliability properties and to compute a MAP hypothesis under a restricted Solomonoff prior.
Proposes MedRLM, a recursive agent-based multimodal framework for long-context clinical reasoning, sensor-guided screening, and referral optimization using a Clinical Evidence Graph Memory.
citing papers explorer
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Recursive Agent Harnesses
RAH improves coding performance on Oolong-Synthetic from 71.75% to 81.36% with fixed GPT-5 backbone by spawning subagent harnesses via executable scripts.
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Decidable By Construction: Design-Time Verification for Trustworthy AI
Design-time Hindley-Milner unification over finitely generated abelian groups is claimed to verify AI model reliability properties and to compute a MAP hypothesis under a restricted Solomonoff prior.
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MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
Proposes MedRLM, a recursive agent-based multimodal framework for long-context clinical reasoning, sensor-guided screening, and referral optimization using a Clinical Evidence Graph Memory.