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20212026
most citedFast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

230 citations · 345 across the 29 of their papers we have counts for

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8 papers · 1 filter

cs.AI2026

SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness

Haozhe Liu, Tian Ye, Sensen Gao +11

As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning,…

cs.AI2026

VERDI:Verified Contradictions for Continual World-Model Optimization

Junyu Wu, Shiqin Nie, Youyi Kou +10

Foundation world models have made remarkable progress in planning, simulation, and embodied intelligence. However, optimizing a pretrained world model toward a user-specified objec…

cs.AI2026

Self-Improvements in Modern Agentic Systems: A Survey

Zhe Ren, Yimeng Chen, Dandan Guo +9

Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or…

cs.LG2026

Neural Computers

Mingchen Zhuge, Changsheng Zhao, Haozhe Liu +16

We propose a new frontier: Neural Computers (NCs) that unify computation, memory, and I/O of traditional computers in a learned runtime state. Our long-term goal is the Completely…

cs.AI2026

RPRA: Predicting an LLM-Judge for Efficient but Performant Inference

Dylan R. Ashley, Gaël Le Lan, Changsheng Zhao +7

Large language models (LLMs) face a fundamental trade-off between computational efficiency (e.g., number of parameters) and output quality, especially when deployed on computationa…

cs.AI2026

dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models

Wenxuan Zhang, Lemeng Wu, Changsheng Zhao +11

Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them with human preferences. In this wo…

cs.CV2026

Small Vision-Language Models are Smart Compressors for Long Video Understanding

Junjie Fei, Jun Chen, Zechun Liu +13

Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost-in-the…

cs.CV2026

VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice

Shuming Liu, Mingchen Zhuge, Changsheng Zhao +20

Chain-of-thought (CoT) reasoning has emerged as a powerful tool for multimodal large language models on video understanding tasks. However, its necessity and advantages over direct…