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Hao-Han Wang

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#computer vision Preprint Oct 2026

Large-scale Repository Engineering via Agent-Native Reusable Code Primitives

Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete repositories remains difficult because interacting modules, interfaces, configurations, tests, and dependencies must work together. We introduce Code Primitives, agent-nati...

Hai-Bo Jin, Peng Kuang, Xu-Chen Yu et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Let the Library Speak: Self-Advertised Method Selection for Formal Proving

This work organizes 82 reusable methods from Putnam 2000-2014 as Method Contracts, which pair applicability descriptions with Mathlib anchors, a checked example or scaffold, and expected proof obligations, and analyzes when similarity-based representations cannot distinguish methods with different applicability, how er...

Xiao-Peng Yuan, Sui-Jin Wang, Yan-Lin Wang et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Harness Engineering for Software Engineering via Modular Executable Dev-Primitives

Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, existing agents remain brittle on long-horizon workflows, where they must repeatedly reconstruct program state scattered across source files, configurations, tests, depende...

Hai-Bo Jin, Xin-Jie Li, Peng Kuang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Composing Task-specific Agent Harnesses at Test Time with Reusable Primitives

Agent harnesses govern how large language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting agent performance. However, the value of each harness mechanism can differ across heterogeneous tasks: a mechanism that improves one task may impose overhead or context...

Peng Kuang, Hai-Bo Jin, De-Hao Wu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

ANTMAN: Adaptive Need Tracking for Multi-Agent Navigation in Large Information Spaces

ANTMAN is introduced, an adaptive coordination framework that treats evolving unresolved information needs as the unit of runtime coordination and maintains a revisable Need Graph that tracks unresolved requirements, accumulated evidence, prior attempts, and search progress.

Jerry Wang, Hai-Bo Jin, Xiao-Peng Yuan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

STEVE: Stabilizing Textual Gradient-Based Prompt Optimization via Error-Driven Refinement and Regularized Verification

STEVE, a stabilization framework with two coupled mechanisms that reduces degradation and produces more robust prompts, provides a practical way to improve the stability and effectiveness of textual-gradient prompt optimization.

Yi-Fan Xu, Yi-Xuan Li, Xin-Zhuo Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives

This work introduces Tool Primitives, a design that replaces rigid API schema-based invocation with natural language as the interface for tool calling, where each tool is wrapped with an LLM interface that handles schema resolution and execution internally, enabling natural inter-tool communication for nested and multi...

Hai-Bo Jin, Sui-Jin Wang, Xu-Chen Yu et al. · 2 citations
Preprint Aug 2026

Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior

A synthetic player population is constructed whose traits are ground truth by construction, and an opportunity-aware decision-moment representation is introduced that disentangles preference from the chance to express it; ablating it selectively degrades opportunity-dependent traits.

Yifan Lu, Xiaopeng Yuan, Hao-Han Wang · 0 citations

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