Complete orthogonal predictive coordinates do not by themselves bind a latent direction to a named intervention. We present a mathematical and synthetic audit motivated by vascular device-vessel suitcordance. Capacity-matched least-squares predictors were exactly equivalent under complete fixed output transforms, where...
Lingsen You, Yujun Guo, Xinyu Zhong et al.· 0 citations
Puzzles are widely used to evaluate the reasoning capabilities of artificial intelligence (AI) systems for sequential decision making, yet approaches originating from different paradigms are rarely compared under unified conditions. To address this gap, we introduce a three-dimensional characterization framework that e...
Deep reinforcement learning agents reach strong performance in real-time strategy games but can be brittle against opponents outside their training distribution. Separating strategic command selection from learned unit control allows different strategies to be selected for different opponents while reusing the same exe...
Nick Leenders, R. Lindelauf, J. van Oijen et al.· 0 citations
Autonomous driving capabilities depend strongly on the distribution of scenarios encountered during training. Existing methods commonly construct or dynamically adapt training scenario distributions using surrogate criteria such as realism, difficulty, or risk. However, these predefined surrogates may misrepresent trai...
Yichun Ye, He Zhang, Ye Tian et al.· 0 citations
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Improving a long-horizon LLM agent means evolving the harness around a frozen model or training its weights. We let a self-evolving harness make the system stronger first, then cross seed and evolved harnesses with base and trained weights to learn which gains the trained model keeps and which still need the runtime. W...
Automated interviewers and conversational agents are increasingly used in research, recruitment, customer service, and education. However, many existing systems rely on fixed question sequences and provide limited context-based personalization without considering participants'knowledge, which can lead to repetitive or...
Aisvarya Adeseye, J. Isoaho, Adeyemi Adeseye et al.· 0 citations
Parameter-efficient fine-tuning (PEFT) enables large language models (LLMs) to adapt to specialized tasks, but often at the cost of degrading general capabilities acquired during pretraining. Existing approaches primarily mitigate this trade-off through data replay or regularization, relying on additional data or expli...
Zhi-Qian Pang, Zi-Hong Sun, Qi Xie et al.· 0 citations
An agent can complete a task without improving how it works. Turning task experience into reusable capability requires connecting the changed component to its evaluation and subsequent use. We present AgentEvolver, a system for developing capabilities during task execution while keeping the foundation model fixed. Eigh...
Wen-Tao Zhang, Fu-Chao Yang, Yi-Lei Zhao et al.· 0 citations
LLM-based multi-agent systems (MASs) are increasingly used to solve complex tasks through coordinated reasoning, tool use, and interaction with external resources. However, attributing failures in such systems remains challenging because the observed outcome often does not directly reveal the error responsible for the...
Jia-Qi Liao, Yuan-Zhao Zhai, Huan-Xi Liu et al.· 0 citations
This work proposes a novel memory framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model, and proposes a novel retrieval framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model.
Yi Wen, De-Rong Xu, Peng-Yue Jia et al.· 0 citations
In this paper, we argue that Looped Transformers need their own residual connections to prevent performance degradation as the number of iterations grows. We first introduce an interesting phenomenon we observed in Looped Transformers, called latent overthinking: noisy state updates overwrite correct intermediate deduc...
Peng-Xiang Li, Dilxat Muhtar, Di He et al.· 1 citation
LLM-based agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long-term memory. Existing safety research has examined these influences, but largely as separate agent components. How such factors jointly shape an agent's safety behavio...
Hanjun Luo, Junting Mao, Yuhan Lu et al.· 0 citations
