- AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
Liang Ding · Mar 22, 2026 · Citations: 0
Demonstrations Human EvalLlm As Judge Long Horizon
LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
- VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play
Zelai Xu, Ruize Zhang, Chao Yu, Huining Yuan, Xiangmin Yi · Feb 4, 2025 · Citations: 0
Demonstrations Automatic MetricsSimulation Env Multi Agent
We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement…
- Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation
Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah, Omkar Patil, Thao Nguyen · Mar 19, 2026 · Citations: 0
Demonstrations Simulation Env Multi Agent
To address this limitation, we propose MAPG (Multi-Agent Probabilistic Grounding), an agentic framework that decomposes language queries into structured subcomponents and queries a VLM to ground each component.
- SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
Philip Schroeder, Thomas Weng, Karl Schmeckpeper, Eric Rosen, Stephen Hart · Mar 30, 2026 · Citations: 0
Demonstrations Simulation Env Long Horizon
To address this limitation, we introduce SOLE-R1 (Self-Observing LEarner), a video-language reasoning model explicitly designed to serve as the sole reward signal for online RL.
- RAPTOR: A Foundation Policy for Quadrotor Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno · Sep 15, 2025 · Citations: 0
Demonstrations Simulation Env Long Horizon
Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car.
- Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning
Ruize Zhang, Sirui Xiang, Zelai Xu, Feng Gao, Shilong Ji · May 7, 2025 · Citations: 0
Demonstrations Automatic Metrics Long Horizon
The task is turn-based, multi-agent, and physically grounded, posing significant challenges due to its long-horizon dependencies, tight inter-agent coupling, and the underactuated dynamics of quadrotors.
- Watch and Learn: Learning to Use Computers from Online Videos
Chan Hee Song, Yiwen Song, Palash Goyal, Yu Su, Oriana Riva · Oct 6, 2025 · Citations: 0
Demonstrations Long Horizon
Computer-using agents (CUAs) must plan task workflows across diverse and evolving applications, yet progress is limited by the lack of large-scale, high-quality training data.
- MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation
Chengshu Li, Mengdi Xu, Arpit Bahety, Hang Yin, Yunfan Jiang · Oct 21, 2025 · Citations: 0
Demonstrations Simulation Env Long Horizon
Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming.
- SPACeR: Self-Play Anchoring with Centralized Reference Models
Wei-Jer Chang, Akshay Rangesh, Kevin Joseph, Matthew Strong, Masayoshi Tomizuka · Oct 20, 2025 · Citations: 0
Demonstrations Simulation Env Multi Agent
Developing autonomous vehicles (AVs) requires not only safety and efficiency, but also realistic, human-like behaviors that are socially aware and predictable.
- Dual-Modality Multi-Stage Adversarial Safety Training: Robustifying Multimodal Web Agents Against Cross-Modal Attacks
Haoyu Liu, Dingcheng Li, Lukas Rutishauser, Zeyu Zheng · Mar 4, 2026 · Citations: 0
Demonstrations Simulation Env
Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet their dual-stream architecture opens an underexplored attack surface: an adversary who injects…
- DSPO: Stable and Efficient Policy Optimization for Agentic Search and Reasoning
Chenyang Gu, Yewen Pu, Bruce Yang, Xiaofan Li, Huan Gao · Oct 10, 2025 · Citations: 0
Demonstrations Simulation Env
Current approaches either rely on prompting to elicit the model's innate agent capabilities, or suffer from performance ceilings and collapse when applying RL to complex interactive tasks, leaving their true agentic potential untapped.
- Structured Agent Distillation for Large Language Model
Jun Liu, Zhenglun Kong, Peiyan Dong, Changdi Yang, Tianqi Li · May 20, 2025 · Citations: 0
Demonstrations Simulation Env
Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks.
- IA2: Alignment with ICL Activations Improves Supervised Fine-Tuning
Aayush Mishra, Daniel Khashabi, Anqi Liu · Sep 26, 2025 · Citations: 0
Demonstrations Automatic Metrics
Performing IA2 as a priming step before SFT significantly improves the accuracy and calibration of model outputs, as shown by our extensive empirical results on 12 popular benchmarks and two model families.
- CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation
Faria Huq, Zora Zhiruo Wang, Frank F. Xu, Tianyue Ou, Shuyan Zhou · Jan 28, 2025 · Citations: 0
Pairwise PreferenceDemonstrations Automatic Metrics Web Browsing
We propose CowPilot, a framework supporting autonomous as well as human-agent collaborative web navigation, and evaluation across task success and task efficiency.
- 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?
- IROSA: Interactive Robot Skill Adaptation using Natural Language
Markus Knauer, Samuel Bustamante, Thomas Eiband, Alin Albu-Schäffer, Freek Stulp · Mar 4, 2026 · Citations: 0
Demonstrations Long Horizon
We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and…
- Continual Robot Skill and Task Learning via Dialogue
Weiwei Gu, Suresh Kondepudi, Anmol Gupta, Lixiao Huang, Nakul Gopalan · Sep 5, 2024 · Citations: 0
Demonstrations Simulation Env
In this work we present a framework for robots to continually learn tasks and visuo-motor skills and query for novel skills via dialog interactions with human users.
- State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation
Navan Preet Singh, Anurag Garikipati, Ahmed Abulkhair, Jyani Akshay Jagdishbhai, Atul Yaduvanshi · Apr 7, 2026 · Citations: 0
Demonstrations Automatic Metrics
Arabic-DeepSeek-R1 achieves the highest average score across the seven-benchmark OALL suite while establishing SOTA or near-SOTA, including dominant results on grammar-focused MadinahQA (surpassing both GPT-5.1 and the OALL leader by…
- 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.
- Maximizing Asynchronicity in Event-based Neural Networks
Haiqing Hao, Nikola Zubić, Weihua He, Zhipeng Sui, Davide Scaramuzza · May 16, 2025 · Citations: 0
Demonstrations Automatic Metrics
Event cameras deliver visual data with high temporal resolution, low latency, and minimal redundancy, yet their asynchronous, sparse sequential nature challenges standard tensor-based machine learning (ML).
- 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.
- Learning to Answer from Correct Demonstrations
Nirmit Joshi, Gene Li, Siddharth Bhandari, Shiva Prasad Kasiviswanathan, Cong Ma · Oct 17, 2025 · Citations: 0
Demonstrations Automatic Metrics
We study the problem of learning to generate an answer (or completion) to a question (or prompt), where there could be multiple correct answers, any one of which is acceptable at test time.
- 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.
- Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion
Minghan Li, Ercong Nie, Siqi Zhao, Tongna Chen, Huiping Huang · Feb 9, 2026 · Citations: 0
Demonstrations
We present an automated, domain-adaptive QE framework that builds in-domain exemplar pools by harvesting pseudo-relevant passages using a BM25-MonoT5 pipeline.
- Schema for In-Context Learning
Pan Chen, Shaohong Chen, Mark Wang, Shi Xuan Leong, Priscilla Fung · Oct 14, 2025 · Citations: 0
Demonstrations
Inspired by cognitive science, specifically schema theory, which holds that humans interpret new information by activating pre-existing mental frameworks (schemas) to structure understanding, we introduce Schema-Activated In-Context…
- A Framework for Closed-Loop Robotic Assembly, Alignment and Self-Recovery of Precision Optical Systems
Seou Choi, Sachin Vaidya, Caio Silva, Shiekh Zia Uddin, Sajib Biswas Shuvo · Mar 23, 2026 · Citations: 0
Demonstrations
In this work, we present a robotics framework for the autonomous construction, alignment, and maintenance of precision optical systems.
- 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.
- RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin, Pin-Yu Chen, Hong-Yan Huang · Jan 7, 2026 · Citations: 0
DemonstrationsCritique Edit
On a fake news detection benchmark, RADAR consistently outperforms strong retrieval-augmented trainable baselines, as well as general-purpose LLMs with retrieval.
- AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Zhongjun Ding, Yin Lin, Tianjing Zeng, Rong Zhu, Bolin Ding · Aug 21, 2025 · Citations: 0
Demonstrations
We provide 40 ambiguous queries collected from two real-world benchmarks that SIGMOD'26 attendees can use to explore how disambiguation improves SQL generation quality.
- Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment
Ruoxi Cheng, Haoxuan Ma, Weixin Wang, Ranjie Duan, Jiexi Liu · Mar 23, 2025 · Citations: 0
Pairwise PreferenceDemonstrations
Existing techniques are either reward-based (training a reward model on preference pairs and optimizing with reinforcement learning) or reward-free (directly fine-tuning on ranked outputs).
- In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads
Charlotte Pouw, Hosein Mohebbi, Afra Alishahi, Willem Zuidema · Apr 7, 2026 · Citations: 0
Demonstrations
In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain.
- Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
Yuning Wu, Ke Wang, Devin Chen, Kai Wei · Mar 11, 2026 · Citations: 0
Demonstrations
To resolve this dilemma, we introduce Hindsight-Anchored Policy Optimization (HAPO).
- COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics
Kartik Sharma, Rakshit S. Trivedi · Mar 6, 2026 · Citations: 0
Pairwise PreferenceDemonstrations
Experiments across a variety of steering tasks and benchmarks demonstrate that COLD-Steer achieves upto 95% steering effectiveness while using 50 times fewer samples compared to the best baseline.
- 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.
- AITutor-EvalKit: Exploring the Capabilities of AI Tutors
Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova, Ekaterina Kochmar · Dec 3, 2025 · Citations: 0
Demonstrations
We present AITutor-EvalKit, an application that uses language technology to evaluate the pedagogical quality of AI tutors, provides software for demonstration and evaluation, as well as model inspection and data visualization.
- EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
Haolei Xu, Xinyu Mei, Yuchen Yan, Rui Zhou, Wenqi Zhang · Sep 29, 2025 · Citations: 0
Demonstrations
We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM.
- Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers
Peter Shaw, James Cohan, Jacob Eisenstein, Kristina Toutanova · Sep 26, 2025 · Citations: 0
Demonstrations
The Minimum Description Length (MDL) principle offers a formal framework for applying Occam's razor in machine learning.
- NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng · Jul 11, 2025 · Citations: 0
Demonstrations
The model is trained on a dataset of Ubuntu XFCE recordings, which include both randomly generated interactions and realistic interactions produced by AI agents.
- MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning
Yihong Guo, Yu Yang, Pan Xu, Anqi Liu · Jun 10, 2025 · Citations: 0
Demonstrations
We evaluate MOBODY on a wide range of MuJoCo and Adroit benchmarks, demonstrating that it outperforms state-of-the-art off-dynamics RL baselines as well as policy learning methods based on different dynamics learning baselines, with…
- REFLEX: Metacognitive Reasoning for Reflective Zero-Shot Robotic Planning with Large Language Models
Wenjie Lin, Jin Wei-Kocsis, Jiansong Zhang, Byung-Cheol Min, Dongming Gan · May 20, 2025 · Citations: 0
Demonstrations
Inspired by human metacognitive learning and creative problem-solving, we address this limitation by exploring a fundamental question: Can LLMs be empowered with metacognitive capabilities to reason, reflect, and create, thereby enhancing…