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Sercan Ö. Arik

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#machine learning Preprint Oct 2026

Dynamic Harness Search: Building Multi-Agent Systems Per-Query via Prediction

Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Because the value of each design choice is observable only through execution, tailoring a harness to each query has required either executing alternatives at inference ti...

Som Sagar, Sha-Sha Li, He-Jie Cui et al. · 0 citations
#artificial intelligence Preprint Sep 2026

AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation

A small trainable advisor can steer a frozen language-model executor using natural-language advice. In addition to learning from task rewards, the advisor can use feedback from completed interactions to improve its advice. However, a plausible correction need not change execution, yet learning from such corrections can...

Rishabh Agrawal, He-Jie Cui, Sha-Sha Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

The Procedural Graph is introduced: just as a knowledge graph organizes factual knowledge into (entity, relation, entity) triplets for what-is questions, a Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets for what-to-do questions.

Yu-Xing Lu, Yi-Cheng Chen, Shan-Chan Wu et al. · 0 citations
Jul 2026

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents that achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines is introduced.

Anmol Kankariya, Sercan Ö. Arik · 0 citations

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