In view of the urgent demand for rapid assembly and disassembly of temporary modular structures, this paper proposes a parametric generation algorithm that places disassembly feasibility as a core constraint of scheme generation, prior to the verification stage at the end of design. A linkage representation system for...
Jing Zhang· Journal of Digital Frontier· 0 citations
A neural reduced dynamics framework is developed that separates the state the model propagates from what can be supplied as an input or recovered analytically, trains policies entirely inside the frozen learned model, and validates them back in the high-fidelity simulator.
Harry Zhang, Dan Negrut· Research Square· 2 citations
It is demonstrated that the reinforcement learning-based approaches, namely Q-learning and SARSA (State-Action-Reward-State-Action), consistently outperform random selection in terms of total channel capacity, attacker detection accuracy, and performance stability.
This work proposes a new approach that quantifies the effectiveness of each interaction by the reduction in the assistant's uncertainty, measured via entropy over recommendations, to fine-tune the LLM, enabling strategic interaction generation.
Cedar Site Bai, Zhen-Yu Liao, Duan Li et al.· ACM International Conference...· 0 citations
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This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders and yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platfor...
Muhammad Waseem, M. Islam, Md Nasir Uddin Shuvo et al.· arXiv.org· 1 citation
COGTRL is proposed, a trajectory-level reinforcement learning framework that trains LLMs to emulate cognitively grounded reasoning by jointly optimizing cognitive traces and the scientific steps produced in an interleaved manner.
Shrinidhi Kumbhar, Santosh Mashetty, Divij Handa et al.· 1 citation
By reframing DODE as a sequential decision-making problem, this approach addresses the credit assignment challenge through a learned policy and provides a novel framework for calibration of microscopic traffic simulations.
Normalized Low-Rank Adaptation (NoRA) is introduced, a simple yet effective method that normalizes the down-projection matrices during training, improving standard LoRA without requiring repeated normalization throughout training.
Jia-Le Kang, Zi-Yin Yue, Zheng Zhan et al.· 0 citations
This model interleaves reasoning, tool calls, and returns in one left-to-right generation, trained by a supervised warm-up and then outcome-level reinforcement learning against a programmatic reward read directly off the gold call chain, which leaves no learned critic and no judge in the training loop.
Armin Dariani, Sifan Wu, Bang Liu et al.· 0 citations
Experimental results demonstrate that the proposed uncertainty-aware replay buffer enables an RL agent to obtain higher rewards during training compared to other existing uncertainty-aware RL frameworks.
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis J. Desell et al.· 0 citations
Participants on tokenized platforms (i.e., platforms with blockchain implementation) can simultaneously take multiple roles, such as user, investor, and laborer, and draw income from the last two roles. Unlike traditional markets that typically prioritize one means of profitable participation, participants on such plat...
Tianyi Li, Xiaoquan (Michael) Zhang· Journal of the Association f...· 0 citations
ProFlow leverages distributed telemetry signals and offline-trained reinforcement learning to identify precursor congestion conditions and proactively reroute protected flows before throughput degradation occurs, demonstrating the effectiveness of anticipatory congestion management.
Sourya Saha, Md. Nurul Absur, Saptarshi Debroy· IEEE Conference on Local Com...· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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