Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, rewrite queries, and retrieve information under domain-specific constraints. Although reinforcement learning (RL) can train such agents from the conseq...
Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content. The neural basis of this cross-language semantic commonality remains unclear. Here we developed a multilingual brain decoding framework and analysed brain recordings from 227 participants spanning Chinese, English and...
A physics-guided residual learning framework that combines the computational efficiency of analytical modeling with the accuracy of MC simulation is introduced and it is demonstrated that learning corrections to an analytical physical baseline improves both prediction accuracy and data efficiency.
Guo-Jun Tang, P. Teal, Alexander Doronin· 0 citations
Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their behavior to individual users. Existing approaches in pluralistic alignment often assume access to annotator identities and rely on powerful pre...
Akansha Kalra, Ellen R. Novoseller, Vernon J. Lawhern et al.· 0 citations
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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
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
