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20242026
most citedEmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety

2 citations · 7 across the 26 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

Same Space or Not: Benchmarking Spatial Intelligence Through Cross-View Contradictions

Huizi Cao, Guanyu Xu, Zhixuan Liang +5

Spatial intelligence, the capacity to represent and reason about physical space, underlies embodied perception and real-world visual understanding. Existing benchmarks probe it thr…

cs.AI2026

EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution

Python Song, Zhixuan Liang, Kelsey Fu +3

Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require p…

cs.AI2026

Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses

Zhaochen Yu, Yingcheng Wu, Zhenfei Yin +5

Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive…

cs.CL2026

Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning

Yinghui He, Ling Yang, Jiarui Liu +6

Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result t…

cs.CL2026

PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents

Shuhan Xue, Zixin Ding, Yichen Shen +6

Recursive self-improvement requires agents to turn accumulated experience into better future behavior. Personal AI agents offer a concrete setting for studying this capability beca…

cs.LG2026

EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents

Weixian Xu, Shilong Liu, Mengdi Wang

In this paper, we propose EEVEE, the first multi-dataset test-time prompt learning framework for LLM agents, enabling test-time prompt learning under real-world task streams. Exist…

cs.AI2026

Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

Jui-Hui Chung, Ziyang Cai, Zihao Li +14

We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of defin…

cs.AI2026

AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?

Zhangchen Xu, Junda Chen, Yue Huang +16

Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifac…

cs.CV2026

MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory

Minghao Guo, Qingyue Jiao, Zeru Shi +14

Long-term agent memory is increasingly multimodal, yet existing evaluations rarely test whether agents preserve the visual evidence needed for later reasoning. In prior work, many…

cs.LG2026

Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

NVIDIA, :, Aakshita Chandiramani +544

We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…

cs.CL2026

OpenClaw-RL: Train Any Agent Simply by Talking

Yinjie Wang, Xuyang Chen, Xiaolong Jin +2

Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system…

cs.LG2026

RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System

Yinjie Wang, Tianbao Xie, Ke Shen +2

We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, amplifying learning signa…