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cs.CL2026

SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing

Sai Ashish Somayajula, Marianne Menglin Liu, Chuan Lei +9

Natural language interfaces to databases aim to translate user questions into executable SQL, yet remain brittle in real-world settings where questions are underspecified and schem…

cs.CL2026

ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering

Marianne Menglin Liu, Daniel Garcia, Fjona Parllaku +3

Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapping names and descriptions, intr…

cs.CL2026

Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning

Jiashu He, Meizhu Liu, Olaitan P Olaleye +9

Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer f…

cs.CL2026

LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding

Zhivar Sourati, Zheng Wang, Marianne Menglin Liu +8

Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents' structural organization and cross-page dependencie…

cs.CL2025

RAG Makes Guardrails Unsafe? Investigating Robustness of Guardrails under RAG-style Contexts

Yining She, Daniel W. Peterson, Marianne Menglin Liu +4

With the increasing adoption of large language models (LLMs), ensuring the safety of LLM systems has become a pressing concern. External LLM-based guardrail models have emerged as…