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20242026
most citedGet More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference

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cs.LG2025

ThetaEvolve: Test-time Learning on Open Problems

Yiping Wang, Shao-Rong Su, Zhiyuan Zeng +13

Recent advances in large language models (LLMs) have enabled breakthroughs in mathematical discovery, exemplified by AlphaEvolve, a closed-source system that evolves programs to im…

cs.DB2025

L2T-Tune:LLM-Guided Hybrid Database Tuning with LHS and TD3

Xinyue Yang, Chen Zheng, Yaoyang Hou +4

Configuration tuning is critical for database performance. Although recent advancements in database tuning have shown promising results in throughput and latency improvement, chall…

cs.LG2025

Efficient Parallel Samplers for Recurrent-Depth Models and Their Connection to Diffusion Language Models

Jonas Geiping, Xinyu Yang, Guinan Su

Language models with recurrent depth, also referred to as universal or looped when considering transformers, are defined by the capacity to increase their computation through the r…

cs.CR2025

When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?

Yibo Peng, James Song, Lei Li +6

Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In th…

cs.CL2025

Web-CogReasoner: Towards Multimodal Knowledge-Induced Cognitive Reasoning for Web Agents

Yuhan Guo, Cong Guo, Aiwen Sun +12

Multimodal large-scale models have significantly advanced the development of web agents, enabling perception and interaction with digital environments akin to human cognition. In t…

cs.LG2025

Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation

Xinyu Yang, Yuwei An, Hongyi Liu +2

Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit parallelism in sequential generation. Inspired by this, we introduce Multiverse, a new generative model t…

cs.LG2025

APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding

Xinyu Yang, Tianqi Chen, Beidi Chen

Context-augmented generation (CAG) techniques, including RAG and ICL, require the efficient combination of multiple contexts to generate responses to user queries. Directly inputti…