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813 papers
A token-level analysis of this failure mode is presented by viewing decoding as a dynamical process that enters and persists in a small set of recurrent contexts and shows that persistence is controlled by the escape mass assigned to plausible alternatives within the token sampling set.
This work identifies shared rank-one reuse as the root cause of the leakage exploited by the authors' attacks, and proposes LatticeLeak, which exploits the resulting hidden lattice.
This work plants a controllable latent variable inside natural-looking text and arranges the 8 states themselves on a ring, in the exact order of the Markov chain, which is supporting evidence that a concept's geometry can be formed by the statistical dynamics of the latent variable behind it.
Language-model post-editing produced fluent semantic substitutions that rose with corruption, confidence did not reliably flag, and no interface policy removed, and this does not demonstrate clinical harm; prospective human-in-the-loop evaluation is needed.
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The findings indicate that machine learning is the dominant AI technology in SME supply chains, primarily used for forecasting, inventory management, process monitoring, logistics optimization, anomaly detection, and operational decision support, and economic and environmental sustainability dimensions receive substantially greater attention than social sustainability.
The complex multi-energy coupling characteristics inherent to integrated energy system (IES) present unprecedented challenges for the implementation of low-carbon scheduling. Existing optimization methods often exhibit limitations in system scalability, algorithm adaptivity, and carbon reduction efficacy for complex IES. This paper proposes a Large Language Model (LLM)-Embedded Multi-Agent Reinforcement Learning (LEMARL) to address the aforementioned issues. The proposed method integrates the global perception capability of LLMs with the dynamic optimization capability of MARL. Specifically, the LLM-Embedded module generates high-quality reward functions and policy frameworks from a global perspective, while the MARL module leverages these LLM-generated strategies for distributed interactive iterations—greatly enhancing computation efficiency and scalability. Simulation results demonstrate that LEMARL reduces carbon emissions by 7.76% and simultaneously decreases operating costs by 4.49% in a small-scale IES. Furthermore, LEMARL also exhibits superior applicability and scalability in large-scale IES of the IEEE 141-bus power grid integrated with 51-node thermal system.
Transformer-based large language models (LLMs) primarily consist of weight-intensive fully connected (FC) layers and cache-dependent attention layers. While batching significantly enhances the throughput of FC layers, it paradoxically increases the cache demands of attention layers. This provides no performance benefit and creates substantial memory pressure. Consequently, existing graphics processing unit (GPU)-based LLM acceleration systems face throughput limitations from batch size constraints. Even when DRAM-based processing-in-memory (PIM) is employed to accelerate attention, the utilization remains extremely low under small batch sizes, which is unsuitable for low-batch scenarios. Fortunately, the emerging nonvolatile resistive random access memory (RRAM) technology offers batch size-insensitive acceleration for FC layers through highly parallel in situ computations by eliminating weight loading overhead. This insight leads us to propose a hybrid approach: RRAM for FC layers and DRAM PIM for attention layers to overcome batch size limitations. However, merely scaling existing RRAM architectures misaligned with LLMs’ computation and storage demands will result in prohibitive overheads. Meanwhile, existing DRAM-based PIMs suffer from poor resource utilization due to the computational pattern of attention layers. Implementing an effective scheduling strategy is equally crucial to harness the potential of the hybrid PIM system. To address these challenges, we present DuoPIM, a novel RRAM–DRAM hybrid PIM architecture optimized for LLM decoding. We introduce novel architectural innovations for both the RRAM and DRAM PIM components to address the challenges posed by LLMs. Specifically, we decouple RRAM’s storage and computing capabilities within a hierarchical architecture, implement minimal modifications to DRAM PIM to support online softmax, and devise dedicated strategies across multiple architectural levels to enhance overall resource utilization. Evaluations demonstrate DuoPIM’s ability to fully leverage computing capacity across various batch sizes.
A preliminary study on the adaptation of Whisper for Automatic Speech Recognition in Baniwa, an indigenous Arawakan language spoken in Brazil, Colombia, and Venezuela, demonstrating that multilingual foundation models can be successfully adapted to extremely low-resource indigenous languages.
This work proposes a Localize-Then-Decide framework, which restores the monotonic relationship between confidence and disagreement risk and enables high-probability agreement guarantees in large language models.
The constrained case of this"model size vs. inference compute"trade-off, in which the model outputs are constrained by a strict grammar at inference time, is examined, which demonstrates that the constrained trade-off behaves differently from the unconstrained trade-off.
RTLGuard leverages a teacher-student framework designed to sanitize compromised RTL generation models by fine-tuning a small-scale,"clean"teacher model on a limited set of trusted RTL data, and incorporating feature alignment and knowledge distillation to suppress malicious behaviors.
Spectral-Aware Muon is introduced, which holds the head at the Muon scale and amplifies the bulk using a static spectral prior, and both variants outperform tuned AdamW and Muon (Scion implementation) baselines in all evaluated model-scale and batch-size configurations.
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