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20242026
most citedA Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

3 citations · 8 across the 25 of their papers we have counts for

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10 papers · 1 filter

cs.AI2026

Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory

Minghao Guo, Qingyue Jiao, Xiujin Liu +77

Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…

cs.CV2026

VideoGen-Agent: Reinforcing Video Generation Agents

Binxu Li, Haoyi Duan, Yuhui Zhang +9

Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring speci…

cs.AI2026

Demystifying Agent Skills: Why They Work-Until They Don't

Zhiyuan Jiang, Fangrui Huang, Hanwen Xing +6

Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely…

cs.AI2026

Quantifying Consistency in LLM Logical Reasoning via Structural Uncertainty

Baishali Chaudhury, Mengdie Flora Wang, Hyunji Hayley Park +3

Large language models can arrive at the same answer through reasoning paths that are unstable, contradictory, or difficult to rank consistently -- a failure mode especially prevale…

cs.AI2026

Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents

Aijing Gao, Yiming Kang, Mengdie Flora Wang +1

In hierarchical reasoning, failures often originate at intermediate decision points where the agent commits to a wrong branch without recognizing that it lacks critical information…

cs.CL2026

Learning Agent Routing From Early Experience

Yimin Wang, Jiahao Qiu, Xuan Qi +6

LLM agents achieve strong performance on complex reasoning tasks but incur high latency and compute cost. In practice, many queries fall within the capability boundary of cutting-e…

cs.AI2026

Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts

Aiden Yiliu Li, Xinyue Hao, Shilong Liu +1

Despite advances in multimodal large language models, autonomous web agents still struggle to reliably execute long-horizon tasks on complex and dynamic web interfaces. Existing ag…

physics.chem-ph2026

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning

Xinwu Ye, Yicheng Mao, Yuxuan Liao +16

Current chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) to solve complex reasoning problems. However, forcing nonverbal tacit chemical l…

cs.AI2026

FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains

Jiashuo Liu, Siyuan Chen, Zaiyuan Wang +38

Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, Futu…

cs.AI2026

CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations

Huan-ang Gao, Zikang Zhang, Tianwei Luo +9

Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robu…