Saved in:
| Main Authors: | Ezra, Elon, Weizman, Ariel, Azaria, Amos |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.12277 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Fool Me, Fool Me: User Attitudes Toward LLM Falsehoods
by: Nirman, Diana Bar-Or, et al.
Published: (2024)
by: Nirman, Diana Bar-Or, et al.
Published: (2024)
TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages
by: Ofer, Moshe, et al.
Published: (2025)
by: Ofer, Moshe, et al.
Published: (2025)
SkillOpt: Executive Strategy for Self-Evolving Agent Skills
by: Yang, Yifan, et al.
Published: (2026)
by: Yang, Yifan, et al.
Published: (2026)
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
by: Gehring, Jonas, et al.
Published: (2024)
by: Gehring, Jonas, et al.
Published: (2024)
Confidence Improves Self-Consistency in LLMs
by: Taubenfeld, Amir, et al.
Published: (2025)
by: Taubenfeld, Amir, et al.
Published: (2025)
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects
by: Zhang, Jipeng, et al.
Published: (2025)
by: Zhang, Jipeng, et al.
Published: (2025)
ToolGate: Contract-Grounded and Verified Tool Execution for LLMs
by: Liu, Yanming, et al.
Published: (2026)
by: Liu, Yanming, et al.
Published: (2026)
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models
by: Dong, Guanting, et al.
Published: (2024)
by: Dong, Guanting, et al.
Published: (2024)
CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation
by: Yan, Weixiang, et al.
Published: (2023)
by: Yan, Weixiang, et al.
Published: (2023)
EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies
by: Hu, Xavier, et al.
Published: (2026)
by: Hu, Xavier, et al.
Published: (2026)
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
by: Wu, Yue, et al.
Published: (2023)
by: Wu, Yue, et al.
Published: (2023)
Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs
by: Tie, Guiyao, et al.
Published: (2025)
by: Tie, Guiyao, et al.
Published: (2025)
The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution
by: Li, Junlong, et al.
Published: (2025)
by: Li, Junlong, et al.
Published: (2025)
$\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
by: He, Muyu, et al.
Published: (2026)
by: He, Muyu, et al.
Published: (2026)
Probing the Lack of Stable Internal Beliefs in LLMs
by: Luo, Yifan, et al.
Published: (2026)
by: Luo, Yifan, et al.
Published: (2026)
Benchmarking Real-Time Question Answering via Executable Code Workflows
by: Zhou, Wenjie, et al.
Published: (2026)
by: Zhou, Wenjie, et al.
Published: (2026)
VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents
by: Chen, Yuhao, et al.
Published: (2026)
by: Chen, Yuhao, et al.
Published: (2026)
Code Execution as Grounded Supervision for LLM Reasoning
by: Jung, Dongwon, et al.
Published: (2025)
by: Jung, Dongwon, et al.
Published: (2025)
Execution-Verified Reinforcement Learning for Optimization Modeling
by: Guan, Runda, et al.
Published: (2026)
by: Guan, Runda, et al.
Published: (2026)
VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
by: Ni, Yuansheng, et al.
Published: (2025)
by: Ni, Yuansheng, et al.
Published: (2025)
Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments
by: Schnabl, Christoph, et al.
Published: (2025)
by: Schnabl, Christoph, et al.
Published: (2025)
When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation
by: Xu, Wenda, et al.
Published: (2025)
by: Xu, Wenda, et al.
Published: (2025)
Gistify! Codebase-Level Understanding via Runtime Execution
by: Lee, Hyunji, et al.
Published: (2025)
by: Lee, Hyunji, et al.
Published: (2025)
GenerationPrograms: Fine-grained Attribution with Executable Programs
by: Wan, David, et al.
Published: (2025)
by: Wan, David, et al.
Published: (2025)
Executable Code Actions Elicit Better LLM Agents
by: Wang, Xingyao, et al.
Published: (2024)
by: Wang, Xingyao, et al.
Published: (2024)
DOCE: Finding the Sweet Spot for Execution-Based Code Generation
by: Li, Haau-Sing, et al.
Published: (2024)
by: Li, Haau-Sing, et al.
Published: (2024)
COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following
by: Bhar, Swarnadeep, et al.
Published: (2025)
by: Bhar, Swarnadeep, et al.
Published: (2025)
Adaptive Multimodal Agents-Based Framework for Automatic Workflow Execution
by: Cifani, Susanna, et al.
Published: (2026)
by: Cifani, Susanna, et al.
Published: (2026)
PatchWorld: Gradient-Free Optimization of Executable World Models
by: Bai, Jiaxin, et al.
Published: (2026)
by: Bai, Jiaxin, et al.
Published: (2026)
The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
by: Si, Chenglei, et al.
Published: (2025)
by: Si, Chenglei, et al.
Published: (2025)
Towards Execution-Grounded Automated AI Research
by: Si, Chenglei, et al.
Published: (2026)
by: Si, Chenglei, et al.
Published: (2026)
Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution
by: Sivakumaran, Nithin, et al.
Published: (2026)
by: Sivakumaran, Nithin, et al.
Published: (2026)
Case-Based Calibration of Adaptive Reasoning and Execution for LLM Tool Use
by: Pang, Renning, et al.
Published: (2026)
by: Pang, Renning, et al.
Published: (2026)
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
by: Zheng, Xin, et al.
Published: (2024)
by: Zheng, Xin, et al.
Published: (2024)
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
by: Phute, Mansi, et al.
Published: (2023)
by: Phute, Mansi, et al.
Published: (2023)
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
by: Zhang, Xiaoying, et al.
Published: (2024)
by: Zhang, Xiaoying, et al.
Published: (2024)
Self-Evolved Reward Learning for LLMs
by: Huang, Chenghua, et al.
Published: (2024)
by: Huang, Chenghua, et al.
Published: (2024)
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification
by: Kumar, Adarsh, et al.
Published: (2025)
by: Kumar, Adarsh, et al.
Published: (2025)
Self-controller: Controlling LLMs with Multi-round Step-by-step Self-awareness
by: Peng, Xiao, et al.
Published: (2024)
by: Peng, Xiao, et al.
Published: (2024)
Evolving and Executing Research Plans via Double-Loop Multi-Agent Collaboration
by: Zhang, Zhi, et al.
Published: (2025)
by: Zhang, Zhi, et al.
Published: (2025)
Similar Items
-
Fool Me, Fool Me: User Attitudes Toward LLM Falsehoods
by: Nirman, Diana Bar-Or, et al.
Published: (2024) -
TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages
by: Ofer, Moshe, et al.
Published: (2025) -
SkillOpt: Executive Strategy for Self-Evolving Agent Skills
by: Yang, Yifan, et al.
Published: (2026) -
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
by: Gehring, Jonas, et al.
Published: (2024) -
Confidence Improves Self-Consistency in LLMs
by: Taubenfeld, Amir, et al.
Published: (2025)