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From the 1 of 5 linked papers with an AI index.

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5 papers

cs.SE2026

SpecRL: Reinforcement Learning with Test-Based Completeness Rewards for Formal Specification Synthesis

Zhechong Huang, Zhao Zhang, Zeyu Sun +2

SpecRL is a reinforcement learning system that improves automatic generation of program specifications by rewarding candidates that reject impossible behaviors identified through g…

cs.AI2026

Thinking as Compression: Your Reasoning Model is Secretly a Context Compressor

Guoxin Ma, Yibing Liu, Chengzhengxu Li +7

Context compression aims to shorten long context inputs with minimal information loss for LLM inference acceleration. While existing methods have shown promise, they typically rely…

cs.CV2026

MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale

Zhicong Tang, Zhao Zhang, Jingye Chen +6

Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, analogous to word-level editin…

cs.CL2026

Confidence Should Be Calibrated More Than One Turn Deep

Zhaohan Zhang, Chengzhengxu Li, Xiaoming Liu +3

Large Language Models (LLMs) are increasingly applied in high-stakes domains such as finance, healthcare, and education, where reliable multi-turn interactions with users are essen…

cs.CL2026

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models

Zhaohan Zhang, Ziquan Liu, Ioannis Patras

Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in high-stakes applications, such as healthcare and finance…