http://www.bing.com:80/search?q=Calculator+GUI+JavaSearch resultshttp://www.bing.com:80/s/a/rsslogo.gifhttp://www.bing.com:80/search?q=Calculator+GUI+JavaCopyright © 2026 Microsoft. All rights reserved. These XML results may not be used, reproduced or transmitted in any manner or for any purpose other than rendering Bing results within an RSS aggregator for your personal, non-commercial use. Any other use of these results requires express written permission from Microsoft Corporation. By accessing this web page or using these results in any manner whatsoever, you agree to be bound by the foregoing restrictions.https://openreview.net/OpenReview promotes transparency and openness in scientific communication and peer-review processes, fostering collaboration and innovation in research communities.Mon, 17 Aug 2026 06:16:00 GMThttps://openreview.net/attachment?id=pqNFDA2TFm&name=pdfWe introduce CLEVER, the first curated benchmark for evaluating the generation of specifications and formally verified code in Lean. The benchmark comprises of 161 programming problems; it evaluates both formal speci-fication generation and implementation synthesis from natural language, requiring formal correctness proofs for both.Sat, 15 Aug 2026 16:27:00 GMThttps://openreview.net/profile?id=~Jonathan_Gratch1Towards Emotion-Aware Agents For Negotiation Dialogues Kushal Chawla, Rene Clever, Jaysa Ramirez, Gale M. Lucas, Jonathan Gratch 2021 (modified: 04 Jan 2022) CoRR 2021Mon, 17 Aug 2026 12:50:00 GMThttps://openreview.net/pdf?id=BddNTCq65yq579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models. Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- ence stage. In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information.Sat, 08 Aug 2026 13:51:00 GMThttps://openreview.net/forum?id=kIuqPmS1b1The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning Wenqian Ye, Luyang Jiang, Eric Xie, Guangtao Zheng, Yunsheng Ma, Xu Cao, Dongliang Guo, Daiqing Qi, Zeyu He, Yijun Tian, Christopher W. Porter, Megan Coffee, Zhe Zeng, Sheng Li, Ziran Wang, Ting-Hao Kenneth Huang, James Matthew Rehg, Henry Kautz, Aidong ...Sun, 16 Aug 2026 09:02:00 GMThttps://openreview.net/pdf?id=X6dEqXIsEWWhile, as we mentioned earlier, there can be thorny “clever hans” issues about humans prompting LLMs, an automated verifier mechanically backprompting the LLM doesn’t suffer from these. We tested this setup on a subset of the failed instances in the one-shot natural language prompt configuration using GPT-4, given its larger context window.Sun, 16 Aug 2026 14:53:00 GMThttps://openreview.net/forum?id=aHzPGyUhZaOne common approach is training models to refuse unsafe queries, but this strategy can be vulnerable to clever prompts, often referred to as jailbreak attacks, which can trick the AI into providing harmful responses. Our method, STAIR (SafeTy Alignment with Introspective Reasoning), guides models to think more carefully before responding.Sun, 16 Aug 2026 16:48:00 GMThttps://openreview.net/forum?id=pqNFDA2TFmTL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean. It requires full formal specs and proofs. No few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning.Mon, 17 Aug 2026 00:47:00 GMThttps://openreview.net/forum?id=BkUHlMZ0bOur analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks.Sun, 16 Aug 2026 21:27:00 GMThttps://openreview.net/forum?id=IbOacMF5qdThis paper introduces CLEVER, a benchmark dataset designed to evaluate LLMs on formally verified code generation. It consists of 161 carefully crafted Lean specifications derived from programming problems in the existing HumanEval dataset.Sun, 16 Aug 2026 03:26:00 GMT