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.
Abstract
Trusted Execution Environment (TEE)-shielded partitioning of Large Language Models (LLMs) accelerates on-device inference by offloading obfuscated linear layers to an untrusted accelerator while retaining only a small correction inside the TEE. However, earlier lightweight obfuscation schemes preserved weight-vector directions and were broken by ArrowMatch. To defend against this attack, ArrowCloak injects scalar multiples of the same hidden direction into all weight vectors, enabling lightweight trusted correction. We show that this reuse leaves a rank-one relation across the complete accelerator-visible matrix. For the released real-valued scheme, we propose SpectralLeak, which estimates and removes the shared component. Across 12 task settings, its surrogates achieve $87.98\%$ mean accuracy versus $89.85\%$ for the victims. In our defense-favorable mod-$Q$ realization of ArrowCloak's published modular security formulation, mod-$Q$ arithmetic suppresses this spectral signal but retains the algebraic rank-one relation modulo $Q$. We therefore propose LatticeLeak, which exploits the resulting hidden lattice. In our BERT-Base and GPT2-Base experiments, it reconstructs every protected fixed-point parameter exactly; across all evaluated architectures, the reconstructed models retain victim-level task accuracy without victim queries, labels, or fine-tuning. These findings identify shared rank-one reuse as the root cause of the leakage exploited by our attacks. Guided by this insight, we design ButterflyCloak, a keyed maximal-rank butterfly mask that replaces the reused direction with distinct mask rows while retaining fast trusted correction...
FLARE is proposed, a novel framework that endows VLAs with robust error recovery capabilities through a ``Retry" and ``Reset" Paradigm, and significantly improves task success and robustness.
Ganlong Zhao, Zijia Tang, Xingping Chen et al.· 3 citations
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of executable file, search, and code environments, while \emph{Agentic Coordination Scaling} trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a \emph{Heavy-Duty Solver} for ambitious, long-running tasks.
Apodex Team B. An, B. Li, B. Wang et al.· 1 citation
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
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.
Chen Xia, Tong Gou, Yinliang Xu et al.· IEEE Transactions on Smart G...· 1 citation
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
