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L. Van

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Selective Off-Policy Reference Tuning with Plan Guidance

SORT adds a repair update for all-wrong prompts without changing rollout generation: it derives a plan from the reference solution, compares token probabilities with and without that plan, and gives higher weight to tokens that become more predictable under plan conditioning.

A. Duc, Tien-Phat Nguyen, T. Nguyen et al. · 1 citation
Preprint Sep 2026

Beyond Repository Boundaries: Cross-Repository Graph Retrieval for Code Generation

CrossCoder is proposed, a cross-repository code generation framework that explicitly incorporates external libraries into the retrieval context through a unified knowledge graph over repository and library entities that consistently improves both functional correctness and robustness to dependency-version changes.

Minh Le-Anh, Nam Le Hai, Quyen Tran et al. · 0 citations
#machine learning Preprint Sep 2026

CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing

This work replaces the Jensen-Shannon Divergence routing with C_struct, a structural proxy that measures mass at Vertical-Slash compatible positions and reproduces JSD's routing decisions while eliminating both the pooled matmul and subsequent KL divergence overhead.

H. Nguyen, Chien Van Nguyen, Franck Dernoncourt et al. · 0 citations
#software testing Preprint Aug 2026

XREPOTEST: Benchmarking Multilingual Repository-Level Unit Test Generation for Large Language Models

XREPOTEST is introduced, a multilingual repository-level benchmark for unit test generation spanning five underexplored languages: Rust, Go, Julia, PHP, and Ruby, and Invocation Rate is proposed to assess whether generated tests meaningfully exercise the intended functionality.

L. Dung, Dong Cao Van, Nam Le Hai et al. · 1 citation
Conference Open access 2026

Octopus: Gated Selective Attention for Memory-Bounded Long-Context Inference in Large Language Models

O CTOPUS is proposed, a framework that confers fixed-memory inference onto pretrained Transform-ers without the information loss of linearization and outperforms state-of-the-art linearized baselines on the GSM8K benchmark, demonstrating that learned sparse retention serves as an effective regular-izer for long-horizon...

C. Nguyen, Ryan A. Rossi, L. Van et al. · 0 citations

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