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CLEVER: A Curated Benchmark for Formally Verified Code Generation TL;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
Submissions | OpenReview Promoting openness in scientific communication and the peer-review process
Clever: A Curated Benchmark for Formally Verified Code Generation We 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
Do Histopathological Foundation Models Eliminate Batch Effects? A . . . Deep learning has led to remarkable advancements in computational histopathology, e g , in diagnostics, biomarker prediction, and outcome prognosis Yet, the lack of annotated data and the impact of batch effects, e g , systematic technical data differences across hospitals, hamper model robustness and generalization Recent histopathological foundation models --- pretrained on millions to
STAIR: Improving Safety Alignment with Introspective Reasoning One 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
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
EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic . . . A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like “clever but clueless interns” in novel environments This severely limits their practical utility To systematically measure and drive progress on this challenge, we first introduce the Jericho Test-Time Learning (J-TTL) benchmark J-TTL is a new evaluation
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