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Greg Durrett - OpenReview CLEVER: A Curated Benchmark for Formally Verified Code Generation Amitayush Thakur, Jasper Lee, George Tsoukalas, Meghana Sistla, Matthew Zhao, Stefan Zetzsche, Greg Durrett, Yisong Yue, Swarat Chaudhuri NeurIPS 2025 Datasets and Benchmarks Track poster
EVALUATING THE ROBUSTNESS OF NEURAL NET : A E VALUE THEORY APPROACH 4 THE CLEVER ROBUSTNESS METRIC VIA EXTREME VALUE THEORY tack-agnostic score 2 proof deferred to Appendix B 3 proof deferred to Appendix C t of a classifier and Lj q;x0 is defined as maxx2Bp(x0;R) krg(x)kq Although rg(x) can be calculated easily via back propagation, computing Lj q;x0 is more involved be
Counterfactual Debiasing for Fact Verification 579 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
A Protocol-Driven Platform for Agent-Agnostic Evaluation of LLM Agents Hook it up with TaskConfig—our handy layer for crafting clever input templates and grabbing outputs steadily via JMESPath—and switching agents turns effortless, no extra fiddling needed Our benchmark structure ensures reproducibility by locking in versions
The Pitfalls of Next-Token Prediction - OpenReview This verifies our hypothesis that the Clever Hans cheat absorbs away supervision that is critical to learn the first token At the end of this section, we provide more intuition for how the absence of Clever Hans cheat, allows the teacherless models to solve this task that language has enough redundancy to be conducive for next-token prediction
Jonathan Gratch - OpenReview ACII 2021 CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems Kushal Chawla, Jaysa Ramirez, Rene Clever, Gale M Lucas, Jonathan May, Jonathan Gratch 2021 (modified: 04 Jan 2022) NAACL-HLT 2021 Towards Emotion-Aware Agents For Negotiation Dialogues Kushal Chawla, Rene Clever, Jaysa Ramirez, Gale M Lucas
SelM: Selective Mechanism based Audio-Visual Segmentation CLEVER 521 is structured around three identical Audio-Visual (AV) Transformer layers, where each layer sequentially processes through audio cross- 551 552 attention followed by video cross-attention mechanisms This lay- 553 ered operation marks our primary distinction from previous single- 554 level decoders