Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based methods focus solely on encoding textual information, neglecting the long-tailed nature of knowledge graphs and their various topological structures, e.g., subgraphs, shorte...
Youmin Ko, Hyemin Yang, Taeuk Kim et al.· 0 citations
Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for selecting temperature typically involve fixed, heuristic global settings and context or token-level calibration, rather than a geometrically interpretable representation of t...
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission m...
Chu-Yang Wei, Hao-Xiang Guan, Ming-Hang Zhu et al.· 0 citations
CarryOn is introduced, an object-oriented context runtime that organizes continuation around reusable task objects that builds a context view from continuation carriers, complementary execution context, and a live tail of recent interactions.
Yu-Chen Lu, Julian J. McAuley, Lei Zhang· 0 citations
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Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows. Yet collaboration makes failures harder to diagnose: one agent may misinterpret a correct result while others carry the mistake forward. Failure attribution therefore see...
Combining pretrained models is a common strategy for building AI systems, whether by aligning a vision encoder with a language model or merging fine-tuned checkpoints. However, finding compatible pairs typically requires costly empirical search, as compatibility is known only after alignment or merging. We propose COMP...
Fabian Gr\"oger, Petar Damjanovi\'c, Maria Brbi\'c· 0 citations
Large language models increasingly tackle tasks that require multi-step reasoning, where errors in intermediate claims can affect the final answer. Self-consistency (SC) improves reasoning by voting over sampled final answers, but its vote leaves intermediate disagreements unresolved. To address this problem, we introd...
Haotian Liu, Yingjian Chen, Xiaohang Xu et al.· 0 citations
Tool-call judges evaluate argument values proposed by LLM agents. We find that they accept a correct value less often when their input holds no reference for it, that is, no text that says what the value should be. This happens when the value is a default value from the function schema that the user did not mention, wh...
Linhai Ma, Yan Wang, Lingfei Qian et al.· 0 citations
System One models answer typed questions in a single forward pass and promise the same answer for the same input. For choice questions, this promise fails: the answer depends on the order of the options, increasingly so as they grow in number, and averaging over their cyclic shifts reduces this dependence without remov...
Jian-Guo Huang, Yao-Long Teng, Yan-Chen Deng et al.· 5 citations· ⚡1
Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage cost. Despite their rising popularity, the analysis of looped LMs is still in its infancy: little is known about whether the iterative transformations actually improve th...
Pawel Olszowiec, Paulina Tomaszewska, Maciej \.Zelaszczyk et al.· 0 citations
Chain of Thought (CoT) monitoring is used by frontier labs to detect misbehaviour of AI systems. Yet, its reliability remains poorly understood. We attribute this to the lack of a successful organizing principle that leads to generalizable conclusions about monitoring reliability in different regimes. Recent work attri...
Lennart B\"urger, Piotr Komorowski, Moritz Weckbecker et al.· 0 citations
Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing information, and incorporating teammates'contributions into decisions. Training these behaviors is challenging because final task outcomes provide limited feedback on the value o...
Ke-Ru Chen, Sen-Fon Lin, Ying-Bin Liang et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
