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Tag: Demonstrations

Demonstrations papers in the current HFEPX explorer (95 papers).

Papers in tag: 95

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Research Utility Snapshot

Evaluation Modes

  • Automatic Metrics (7)
  • Simulation Env (1)

Human Feedback Types

  • Demonstrations (20)
  • Pairwise Preference (1)

Required Expertise

  • General (12)
  • Coding (5)
  • Law (4)
OpenVisTool: An Open Recipe for Synthesizing Instructive Visual Tool-Use Trajectories

Changhao Xiang, Shilin Zhang, Zheng Ma, Kanzhi Cheng, Ruize Ma, Yi Feng · Aug 9, 2026 · Citations: 0

Demonstrations Law
  • Visual tool use has emerged as a fundamental capability for multimodal agents to actively acquire evidence beyond a fixed image encoding.
  • Using this framework, we construct OpenVisTool-42K, a dataset spanning five visual reasoning domains, together with OpenVisTool-Bench, a benchmark covering the same domains.
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations

Mehul Damani, Isha Puri, Idan Shenfeld, Jacob Andreas · Jul 1, 2026 · Citations: 0

Demonstrations Automatic Metrics MathCoding
  • We propose an adversarial generator-discriminator framework that augments verifiable rewards with a learned signal from human demonstrations.
  • In story generation, our method significantly improves win rate while producing stories that are diverse and more human-like.
KbSD: Knowledge Boundary aware Self-Distillation for Behavioral Calibration in Agentic Search

Tao Feng, Xinke Jiang, Chao Wu · Jun 29, 2026 · Citations: 0

Demonstrations Automatic Metrics General
  • Agentic search equips large language models with dynamic retrieval abilities, but existing reinforcement learning methods remain limited by reward sparsity in knowledge boundary calibration -- deciding when to trust parametric memory, when…
  • Experiments on multiple benchmarks show that KbSD consistently improves both task accuracy and hallucination mitigation over strong baselines, with the largest gains appearing in the challenging quadrants where sparse rewards are least…
Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts

Alex Kwon · Jun 28, 2026 · Citations: 0

Demonstrations General
  • LLM agents carry conclusions across steps and sessions in compressed memory, and memory products (e.g., mem0, LangMem) rewrite conversation into stored "facts" that later steps trust.
  • We show this rewriting manufactures confidence: across our constructed agent settings, a casual, hedged remark becomes a confident, dated assertion the agent then obeys like a verified fact, granting every above-clearance request it faces.
PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models

Chang Wu, Junfeng Fang, Houcheng Jiang, Kai Tang, Pengyu Cheng, Xiaoxi Jiang · Jun 24, 2026 · Citations: 0

Pairwise PreferenceDemonstrations LawMedicine
  • Safety alignment of large language models (LLMs) typically depends on high-quality supervision data, such as safe demonstrations or preference pairs.
  • To address this, we propose PolicyAlign, a simple yet effective framework for directly aligning LLMs with safety policies.
Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Qiuyue Wang, Mingsheng Li, Jian Guan, Jinhui Ye, Sicheng Xie, Yitao Liu · May 28, 2026 · Citations: 0

Demonstrations Simulation Env General
  • Qwen-VLA is trained with a large-scale joint pretraining recipe over diverse data sources, including robotics manipulation trajectories, human egocentric demonstrations, synthetic simulation data, vision-and-language navigation data,…
  • Experiments on manipulation, navigation, and trajectory-centric benchmarks show consistent multi-task performance and out-of-distribution generalization under variations in scene layout, background, lighting, object configuration, and robot…
ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair

Simiao Liu, Fang Liu, Peiding Wang, Taichuan Li, Yinghao Zhu, Xiaoli Lian · May 17, 2026 · Citations: 0

Demonstrations General
  • We present ContraFix, an agentic AVR framework that constructs such evidence through contrastive runtime analysis.
  • A semantic audit of benchmark-validated SEC-Bench patches shows that 58.2% of ContraFix's patches are semantically correct, compared with 31.3% for the strongest baseline, indicating that the proposed framework improves semantic correctness…
Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Study

Volodymyr Ovcharov · May 14, 2026 · Citations: 0

Demonstrations Automatic Metrics Law
  • We benchmark seven models from five providers on 273 validated court decisions from Ukraine's state registry (EDRSR), measuring tokenizer fertility and zero-shot performance on three tasks.
  • To support reproducibility and address the absence of Ukrainian from legal NLP benchmarks, we release a public dataset of 14,452 court decisions spanning 2008-2026, annotated with seven outcome labels across three temporal epochs that…
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL

Sudong Wang, Weiquan Huang, Xiaomin Yu, Zuhao Yang, Hehai Lin, Keming Wu · Apr 30, 2026 · Citations: 0

Demonstrations Automatic Metrics Coding
  • Experiments on Qwen3-VL show that PRISM consistently improves downstream RLVR performance across multiple RL algorithms (GRPO, DAPO, GSPO) and diverse multimodal benchmarks, improving average accuracy by +4.4 and +6.0 points over the…
Learning-augmented robotic automation for real-world manufacturing

Yunho Kim, Quan Nguyen, Taewhan Kim, Youngjin Heo, Joonho Lee · Apr 24, 2026 · Citations: 0

Demonstrations General
  • Here we present Learning-Augmented Robotic Automation, a hybrid system that integrates learned task controllers and a neural 3D safety monitor into conventional industrial workflows.
  • We deployed the system on an electric-motor production line to automate deformable cable insertion and soldering under real manufacturing constraints, a step previously performed manually by human workers.
Removing Sandbagging in LLMs by Training with Weak Supervision

Emil Ryd, Henning Bartsch, Julian Stastny, Joe Benton, Vivek Hebbar · Apr 23, 2026 · Citations: 0

Demonstrations MathCoding
  • As AI systems begin to automate complex tasks, supervision increasingly relies on weaker models or limited human oversight that cannot fully verify output quality.