Author

Haoyue Wang

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#artificial intelligence Jun 2026

Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation

Test-time compute has emerged as an effective paradigm for improving large language model capability at inference time. Existing allocation strategies primarily prioritize tasks according to difficulty, uncertainty, or expected performance gain, implicitly treating prediction errors as equally costly. This assumption i...

Liang He, Jin Wen, Hao-Yue Wang et al. · arXiv.org · 2 citations

Preprint Aug 2026

From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents

Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially improved skill retrieval due to the increasing skill libraries, retrieving a plausible skill bundle does not guarantee that executing it is worthwhile. Since every ski...

Liang He, Jing Wen, Hong-Yu Gu et al. · 2 citations · ⚡1

Preprint Aug 2026

Not All Visual Tokens Are Equally Safe to Remove:Consequence-Sensitive Visual Token Compression

Visual token compression for vision--language models (VLMs) has largely relied on criteria such as attention, redundancy, and uncertainty to maximize average accuracy under a fixed compute budget, implicitly assuming that all errors carry equal cost. However, the consequence of an incorrect prediction on downstream tas...

Jing Wen, Liang He, Mingyu Cao et al. · 0 citations