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新论文 · v1arXiv:2611.11036

When Is High-Bandwidth Flash Cost-Effective for LLM Inference?

Zhiyu An, Yiping Li, Wan Du

原文摘要

Can high-bandwidth flash (HBF) make LLM inference cheaper than adding GPUs? By storing weights closer to the GPU than SSD, HBF may reduce the number of GPUs needed for model capacity. However, this integration is only justified if it meets service targets and its cost is recovered. We simulate this integration using public component data across nine dense and mixture-of-experts (MoE) models, five workloads, two GPU platforms, and 1--100 GPUs. At fixed demand, HBF reduces the minimum GPU count to meet a service target in 98 of 468 comparisons; majority of them are paused and short sessions, 63 remain cost-effective at half a GPU price per HBF stack. For single short-chat users, HBF enables large MoEs, e.g. DeepSeek V3 and Kimi K2, to meet a relaxed service target on one RTX~4090 GPU, where conventional serving fails. At fixed GPU count, HBF improves throughput in 48\% of matched comparisons below 16 GPUs and 13\% at larger counts. Even at 100 RTX~4090-scale GPUs, cascaded HBF raises offline throughput by 16.4--26.5$\times$ for three large MoEs. We found savings and throughput gains arise in different scenarios.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10182

ATLAS: A Multi-Procedure Benchmark and Context-Aware Model for Surgical Anatomy Segmentation

Ronald L. P. D. de Jong, Tim J. M. Jaspers, Raf A. H. Vervoort, Aron F. H. A. Bakker, Anna L. Dijk, Yiping Li, Jip L. Tolenaar, Jelle P. Ruurda 等 13 位作者

原文摘要

Recognizing anatomical structures during minimally invasive surgery (MIS) could support intraoperative navigation and improve patient safety, but progress is limited by small, single-procedure datasets and by segmentation methods developed for natural scenes. This work addresses both limitations through a new dataset and model. First, we introduce ATLAS-120k, a clip-level semantic segmentation dataset of 121,018 annotated frames from 100 surgical videos, covering 14 laparoscopic and robot-assisted procedures and 41 classes. Annotations were created with a pipeline that combines manual labeling, tracker-based propagation, and review by surgeons. Second, we propose ATLAS (Anatomy Recognition with Context Learning using Foundation Representations), a video semantic segmentation model for surgical anatomy recognition. ATLAS builds on in-domain foundation-model embeddings and adds context queries for the procedure and surgical phase, together with query propagation between frames for short-term memory. ATLAS outperforms state-of-the-art natural-image and in-domain foundation models in both segmentation accuracy and temporal consistency, achieving a Dice score of 0.70 with ViT-L while running in real time at 76.2 FPS. Ablations show that the context queries improve accuracy and that query propagation improves temporal consistency. ATLAS also generalizes well, matching a dedicated model zero-shot on CholecSeg8k and improving phase recognition on AutoLaparo over SurgeNet initialization. The model weights, dataset annotations, and annotation tool are publicly available at https://github.com/TimJaspers0801/ATLAS.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07486

MEvince: Knowledge-Constrained LLM Reasoning for Technique Identification from Malware Reports

Hongyu She, Yiling He, Mengda Xie, Yiping Liu, Xingzhi Qian, Zhan Qin, Lorenzo Cavallaro

原文摘要

Mapping behavioral intelligence in malware analysis reports to ATT&CK techniques supports threat analysis and defense, but reliable identification remains challenging. Supporting evidence is often scattered across long, heterogeneous reports, while closely related techniques share behavioral cues and differ only in subtle but decisive details. While large language models (LLMs) can use external knowledge to interpret reports, they may assign techniques based on plausible associations rather than sufficient evidence. We present MEvince, a knowledge-constrained LLM reasoning framework for report-level malware technique identification. Its core is a shared behavioral representation connecting report observations to technique definitions. This representation is built through agentic mining of technique documentation and supports two online stages: 1) primitive-guided extraction uses behavioral primitives to structure LLM-extracted observations, link them to source passages, and group dispersed evidence; 2) evidence-aligned retrieval matches this evidence to technique profiles through the same concepts, retaining likely candidates and confusable alternatives for LLM comparison of distinguishing behaviors. Controls at both stages validate evidence references and check technique-specific requirements. The representation also supports adaptation without parameter updates, where offline agentic audits diagnose concept weaknesses and refine extraction or retrieval policies. We evaluate MEvince against 11 baselines spanning four method families using a dataset of 610 public malware reports referenced by MITRE ATT&CK. MEvince improves exact-ID F1 over the strongest baseline by 17.4%, and its concept-guided retrieval increases gold-label candidate coverage from 31.6% to 47.9%. With a small adaptation set, offline refinement yields further gains and outperforms baselines that update model parameters.

cs.CR · cs.CL · cs.IR

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.896ACL Anthology · ACL 2026

CAML: A Conflict-Aware Molecular Language Model Merging Framework for Multi-Constraint Molecular Generation

Xuanbai Ren, Luoda Tan, Pei Liu Hunan University, Tengfei Ma Hunan University, Xiangzheng Fu Shenzhen University of Advanced Technology, Longyue Wang Alibaba Group, Yiping Liu Hunan University and Yuelushan Center for Industrial Innovation, Xiangxiang Zeng

原文摘要

Transfer learning has demonstrated efficacy in single-property constraint molecular generation. However, real-world drug discovery demands molecules to satisfy multiple property constraints. Existing paradigms often struggle with this challenge due to catastrophic forgetting or gradient conflicts. To address this, we propose a conflict-aware molecular language model merging framework (CAML). CAML generates multiple constraints molecular as a cooperative game among property-specific fine-tune models (expert models). Specifically, we formulate a Stability-Aware Covariance Matrix Adaptation Evolution Strategy (SACMA-ES) to dynamically optimize the fusion strategy. This algorithm searches for a Nash-equilibrium–like solution that minimizes conflicts among properties by exploring the optimal combination of the importance of the task parameter (intrinsic scale) and relative fusion weights of each expert (fusion coefficient), yielding a multi-constraint molecular property generation model without revisiting the training data. Extensive experiments demonstrate that CAML achieves state-of-the-art performance in complex multi-constraint scenarios. Our results validate that this training-free paradigm offers a robust and efficient solution for resolving intrinsic property conflicts in de novo molecular design.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表