Parametric Computer-Aided-Design (CAD) modeling from human intent remains challenging, particularly during the conceptual design stage, where design goals are expressed through incomplete and unstructured modalities (e.g., hand-drawn sketches and textual descriptions). In this work, we rethink the human intent-to-CAD p...
The results suggest that dynamic conflict clustering, combined with lightweight strategic and learning-guided repair, improves both communication efficiency and robustness in incomplete DCSP solving.
ELECT, which instead settles cross-frame identity before final rendering through a language-guided scene-entity mapping and stationary anchors elected from the model's own drafts, shows that long-horizon consistency need not require increasingly expensive inter-frame conditioning as stories grow.
Mehrajul Abadin Miraj, M. Hasan, M. Farzana et al.· 0 citations
We investigate the problem of allocating indivisible chores among agents with arbitrary positive weights and additive nonnegative cost functions. An allocation is said to be $\alpha$-weighted-maximin-share (WMMS) fair if the cost incurred by each agent is at most $\alpha$ times her weighted maximin share. We establish...
Trung Thanh Nguyen, Khaled Elbassioni, Jörg Rothe· 0 citations
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Probabilistic ocean modeling is essential for risk-aware marine operations, climate analysis, and scientific decision-making, where downstream decisions depend on the conditional distribution of future ocean states rather than a single deterministic trajectory. Recent data-driven ocean models have made deterministic gl...
SARATHI achieves competitive perplexity and matches or exceeds existing structured pruning baselines on zero-shot accuracy, and corrects systematic activation drift in post-LayerNorm architectures that would otherwise cause perplexity collapse at high sparsity.
Sankar Behera, Dhruv Singh, Satyadev Ahlawat et al.· Proceedings of the 2026 Conf...· 0 citations
Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety training. Existing automated red-teaming methods either require privileged access unavailable in production, rely on manually engineered per-model templates, or optimize a single objective without population-le...
KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the context again. We study \emph{zero-prefill cross-model context handoff}: translating a sender cache into a receiver-compatible state so that the receiver can continue d...
Hao-Miao Qiu, Yi-Ning Tang, Zi-Jia Song et al.· 0 citations
Digital technologies can preserve records of wrongdoing indefinitely, and AI can give those records continuing influence over a person's future. We describe this condition as the eternal data present and argue that it threatens the possibility of forgiveness. In criminal justice, formal punishment has an end, whereas t...
Mark Fenwick, Paulius Jurcys, Sebastian Porsdam Mann· 0 citations
Large Language Models accelerate software development but introduce severe security vulnerabilities. This systematic review investigates security risks in automated coding, benchmark limitations, and the role of human developers. Findings indicate that models frequently generate insecure code and faulty patches due to...
LLM-based multi-agent systems (LLM-MAS) use specialized agents that exchange messages to accomplish complex tasks. Their reliance on networked protocols, remote tools, and model endpoints exposes communication to message loss, delay, and context expiration. Because agents often exchange open-ended natural language, ano...
Jin-Hao Zhou, Xiao-Yue Zhang, Jiongchi Yu et al.· 0 citations
Experimental results demonstrate that BridgeCodec achieves superior wideband reconstruction and high perceptual quality, successfully bridging heavily bottlenecked, heterogeneous codecs.
Zi-Jian Lin, Jing Yang, Jin-Hao Luo et al.· 0 citations
