activity
20192026
most citedNatural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study

37 citations · 69 across the 30 of their papers we have counts for

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31 papers · 1 filter

cs.CL2026

ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement

Siwei Wu, Jincheng Ren, Yizhi Li +11

Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution mechanisms from experience.…

cs.CL2026

MDB-Link: Hierarchical Schema Linking for Multi-Database Text-to-SQL

Beiyu Xu, Zhenyu Wu, Jiaoyan Chen +1

Traditional Text-to-SQL research and benchmarks assume a known target database, overlooking settings in which a query must be routed within a large, heterogeneous database collecti…

cs.CL2026

The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage

Afnan Aloraini, Viktor Schlegel, Goran Nenadic +1

Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change. Despite recent progress in compu…

cs.CL2026

A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression

Jincheng Ren, Siwei Wu, Yizhi Li +8

As terminal agents scale to long-horizon, multi-turn workflows, a key bottleneck is not merely limited context length, but the accumulation of noisy terminal observations in the in…

cs.CL2026

Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments

Siwei Wu, Yizhi Li, Yuyang Song +8

Training agentic models for terminal-based tasks critically depends on high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. H…

cs.CL2025

Natural Context Drift Undermines the Natural Language Understanding of Large Language Models

Yulong Wu, Viktor Schlegel, Riza Batista-Navarro

How does the natural evolution of context paragraphs affect question answering in generative Large Language Models (LLMs)? To investigate this, we propose a framework for curating…