KDD’26 Workshop on Agentic Software Engineering (AgenticSE), 2026
Agentic Programming inverts the usual agent design: the program governs all control flow and the LLM is an adaptive component invoked only where reasoning is needed, so token explosion and control-flow hallucination become architecturally impossible rather than prompt-tuned away.
Paper: arXiv:2606.15874 Code: Fzkuji/OpenProgram
ACL’26 Findings, Findings of the Association for Computational Linguistics, 2026
Tandem is a collaborative framework where an LLM provides strategic reasoning insights to guide an efficient SLM, reducing computational costs by ~40% while maintaining or improving reasoning performance.
Paper: arXiv:2604.23623 Code: Applied-Machine-Learning-Lab/ACL2026_Tandem
ACL’26 Findings, Findings of the Association for Computational Linguistics, 2026
MultiDx is a two-stage diagnostic reasoning framework that performs differential diagnosis by integrating multi-perspective evidence from web search, SOAP-formatted cases, and a clinical case database.
Paper: arXiv:2604.24186
ACL’26 Findings, Findings of the Association for Computational Linguistics, 2026
AdapTime is an adaptive temporal reasoning method that dynamically executes reformulate, rewrite, and review actions guided by an LLM planner, enhancing temporal reasoning without external tools.
Paper: arXiv:2604.24175
ACL’25 Findings, Findings of the Association for Computational Linguistics, 2025
MESH employs three kinds of expert modules to integrate structural and semantic information for temporal knowledge graph reasoning, capturing differences between historical and non-historical events.
ACL’25 Findings, Findings of the Association for Computational Linguistics, 2025
AnchorCoT predicts key entities as “anchors” to guide multi-hop reasoning and uses a ranking algorithm to ensure logical answer sequences, improving LLM performance on multi-hop QA.
ACL’25 (Main, Long Paper), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025
Hi-Merging: a training-free method that merges specialized LLMs into a unified multi-task model using hierarchical pruning and scaling, preserving individual strengths while minimizing parameter conflicts across languages and tasks.
Paper: 10.18653/v1/2025.acl-long.1588 Code: Applied-Machine-Learning-Lab/Hi-Merging
ACL’25 (Industry Track), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025
A two-stage framework combining robust supervised fine-tuning with model merging for efficient knowledge editing in LLMs that preserves general capabilities while outperforming existing methods.
Paper: 10.18653/v1/2025.acl-industry.30 Code: Applied-Machine-Learning-Lab/MM4KE
CIKM’24 (Full Research Paper track), Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024
LLM4MSR enhances multi-scenario recommendation by leveraging LLM for knowledge extraction and hierarchical meta networks, achieving improved performance and interpretability without LLM fine-tuning while maintaining deployment efficiency.
Paper: 10.1145/3627673.3679743
TOIS, ACM Transactions on Information Systems, 2024
Uni-CTR leverages Large Language Models and pluggable domain networks to address the seesaw phenomenon and scalability challenges in multi-domain CTR prediction, achieving SOTA performance across various scenarios.
Paper: 10.1145/3698878 Code: Applied-Machine-Learning-Lab/Uni-CTR
arXiv preprint arXiv:2608.03270, 2026
A coarse-to-fine grounding framework that lets a general-purpose VLM locate GUI targets through active visual observation — cropping and re-examining instead of committing to a single click prediction.
Paper: arXiv:2608.03270 Code: Fzkuji/GUI-Agent-Harness
arXiv preprint arXiv:2604.23009, 2026
The first Chinese span-level job-skill NER dataset: 20,000+ instances from four recruitment platforms (2014–2025), annotated by an LLM-empowered Macro-Micro pipeline with expert adjudication.
Paper: arXiv:2604.23009
arXiv preprint arXiv:2604.21525, 2026
SRICL combines semantic retrieval, in-context learning, and supervised fine-tuning with a deterministic verifier that enforces span legality, fixing the malformed spans and boundary drift generative LLMs produce on skill extraction.
Paper: arXiv:2604.21525
arXiv preprint arXiv:2601.00919, 2026
A unified perspective tracing representational collapse and attention sink to improper attention allocation, introducing Lazy Attention with positional discrimination and Elastic-Softmax for focused attention.
Paper: arXiv:2601.00919
arXiv preprint arXiv:2502.18845, 2025
SWAT enables efficient long-context handling via Sliding Window Attention Training, replacing softmax with sigmoid and combining balanced ALiBi with Rotary Position Embedding to retain information.
Paper: arXiv:2502.18845 Code: Fzkuji/swat-attention