- TimeWarp: Evaluating Web Agents by Revisiting the Past
Md Farhan Ishmam, Kenneth Marino · Mar 5, 2026 · Citations: 0
Demonstrations Web Browsing
The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?
- Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework
Mengze Hong, Chen Jason Zhang, Zichang Guo, Hanlin Gu, Di Jiang · Feb 17, 2026 · Citations: 0
Demonstrations Automatic Metrics
Existing approaches either rely on modular system designs with extensive agent orchestration or employ over-simplified instruction schemas, providing limited guidance and poor generalizability.
- Optimizing In-Context Demonstrations for LLM-based Automated Grading
Yucheng Chu, Hang Li, Kaiqi Yang, Yasemin Copur-Gencturk, Kevin Haudek · Feb 28, 2026 · Citations: 0
Rubric RatingDemonstrations
GUIDE paves the way for trusted, scalable assessment systems that align closely with human pedagogical standards.
- AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors
Abhay Sheshadri, Aidan Ewart, Kai Fronsdal, Isha Gupta, Samuel R. Bowman · Feb 26, 2026 · Citations: 0
Demonstrations
We introduce AuditBench, an alignment auditing benchmark.
- FewMMBench: A Benchmark for Multimodal Few-Shot Learning
Mustafa Dogan, Ilker Kesen, Iacer Calixto, Aykut Erdem, Erkut Erdem · Feb 25, 2026 · Citations: 0
Demonstrations
In this paper, we introduce FewMMBench, a comprehensive benchmark designed to evaluate MLLMs under few-shot conditions, with a focus on In-Context Learning (ICL) and Chain-of-Thought (CoT) prompting.
- Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving
Jiangxin Sun, Feng Xue, Teng Long, Chang Liu, Jian-Fang Hu · Feb 26, 2026 · Citations: 0
Demonstrations
Practically, RaWMPC leverages a world model to predict the consequences of multiple candidate actions and selects low-risk actions through explicit risk evaluation.
- ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models
Adam Dejl, Deniz Gorur, Francesca Toni · Feb 27, 2026 · Citations: 0
Demonstrations
Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation for decision-making, with the aim of making the resulting decisions faithfully explainable to and contestable by…
- Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
Chungpa Lee, Jy-yong Sohn, Kangwook Lee · Feb 26, 2026 · Citations: 0
Demonstrations
We show that fine-tuning all attention parameters can harm in-context learning, whereas restricting updates to the value matrix improves zero-shot performance while preserving in-context learning.
- Explore-on-Graph: Incentivizing Autonomous Exploration of Large Language Models on Knowledge Graphs with Path-refined Reward Modeling
Shiqi Yan, Yubo Chen, Ruiqi Zhou, Zhengxi Yao, Shuai Chen · Feb 25, 2026 · Citations: 0
Demonstrations
Extensive experiments on five KGQA benchmark datasets demonstrate that, to the best of our knowledge, our method achieves state-of-the-art performance, outperforming not only open-source but also even closed-source LLMs.
- Perspectives - Interactive Document Clustering in the Discourse Analysis Tool Suite
Tim Fischer, Chris Biemann · Feb 17, 2026 · Citations: 0
Demonstrations
This paper introduces Perspectives, an interactive extension of the Discourse Analysis Tool Suite designed to empower Digital Humanities (DH) scholars to explore and organize large, unstructured document collections.
