In generalized few-shot 3D point cloud segmentation, support annotations are often compromised by sparsity and occlusion. Blindly adapting to such low quality samples risks learning spurious geometric patterns rather than robust novel concepts. Although 3D Vision-Language Models have been introduced to sup plement supe...
Yifei Zhao, Fanyu Zhao, Shengtang Wu et al.· 0 citations
Results show that a single LLM monitoring framework can provide useful, actionable quality signals across multiple product discovery surfaces in a large-scale e-commerce setting.
Alina Nesen, Prathyusha Kanmanth Reddy, David Relyea et al.· 0 citations
This work proposes PW-Pruning, a phase-aware extension of DO-ACP that constructs separate importance-weighted kernels for acoustic-prefill and teacher-forced answer tokens, then combines phase-weighted log-determinant marginal gains to select one static expert bank.
Jun-Jie Cao, Feng-Ping Tian, Wen-Jie Zhou et al.· 0 citations
Findings show that concurrent clinical assessments can identify interpretable, depression-specific acoustic markers in populations where cognitive and affective symptoms co-occur.
W. Jeon, Seunghee Ha, Sang-Kyu Lee et al.· 0 citations
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As AI agents become increasingly connected through networks and take on tasks that require negotiation and joint decision-making, a systematic approach is needed to assess how their views converge and what information supports their decisions. We present an evaluation framework that treats consensus formation as the ev...
It is demonstrated that empirical lab data is more valuable than existing computational proxies, showing how iterative experimental feedback enables AI agents to diagnose assay artifacts and refine molecular designs across rounds.
Zachary Cutts, John Coller, Rachel J. C. Chen et al.· 1 citation
It is found that model size matters less than expected: once fine-tuning and post-processing are applied, performance saturates at a few billion parameters, and Qwen3-4B outperforms larger models in the same family.
Xin-Yu Zhang, Ji-Xiang Li, Bin Shao et al.· 0 citations
This study reflects on the opportunities and challenges of the potential use of LLMs in ABM by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.
L. Vanhée, Melania Borit, P. Siebers et al.· arXiv.org· 6 citations
Multi-token prediction (MTP) can accelerate language-model decoding by drafting several tokens and verifying them in a single target-model pass. However, the best speculation horizon changes with context: longer drafts offer greater amortization when reliable, but can waste compute on suffixes discarded after a rejecti...
Modern distributed microservice deployments generate massive volumes of heterogeneous, multi-format observability logs across physically and logically partitioned infrastructure. Isolating root causes during cascading failures remains an arduous task for Site Reliability Engineers (SREs), bottlenecked by manual keyword...
Current evaluation of LLM-generated debate largely focuses on argumentative quality, leaving the value structure of generated arguments underexamined. We introduce a diagnostic metric framework for comparing how values are expressed and organized in human-reference and AI-generated debates. The framework separates valu...
Maoyuan Li, Haoyuan Li, Zhongsheng Wang et al.· 0 citations
Computer-use agents are limited by the interfaces humans give them. An agent may type a spreadsheet cell by cell when a single line of code would do, or print all two thousand lines of a file through a shell to find one function. Both the interfaces and the knowledge of when to use each are built by hand, which cannot...
S. Agashe, Yuzhe Yang, Gonzalo Gonzalez-Pumariega et al.· 0 citations