Evaluating approaches that improve the security of LLM code generation requires datasets that jointly assess security and functionality and remain adapted to the capabilities of the models being studied. However, existing benchmarks are costly to produce and are generally not calibrated to a chosen range of model capab...
Zacharie Chenail-Larcher, Daniele Lunghi, Melissa Tessa et al.· 0 citations
Medium-voltage feeders have few real-time measurements, so distribution system state estimation (DSSE) rests on pseudo-measurements of the secondary substations, a prior in Bayesian terms. Weighted least squares keeps the power flow exact but reduces this prior to a mean and variance, while supervised deep learning est...
Nan Lin, Peter Palensky, Pedro Vergara Barrios· 0 citations
LLM agents are increasingly used to automate vulnerability detection, but reliable detection is challenging because agents must distinguish subtle, security-relevant fixes from benign code variation. While underlying LLM capability has improved significantly, the accuracy of the agent still depends heavily on the harne...
Ira Ceka, Arnav Garg, Ryan Lutz et al.· 0 citations
LLM agents operating in untrusted workspaces face the risk of untrusted code execution during routine tasks, yet final reports and workspace states alone cannot reliably establish whether execution occurred. We introduce an evaluation framework that codes observation, reasoning, execution, and response behaviors from a...
Shuai Yang, Enmeng Lu, Jinyu Fan et al.· 0 citations
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Autonomous LLM agents executing multi-step workflows over personal data often misalign with user privacy expectations and leak sensitive, out-of-context information. Although Contextual Integrity (CI) formalizes this problem, existing benchmarks and defenses rarely operationalize CI for realistic multi-step task sequen...
Robin Staab, Tom Wollschl\"ager, Vahab Mirrokni et al.· 0 citations
Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results. This paper proposes CausalGrade, a causal graph scoring method based on the probabilistic judgment model Jev. The method jointly provides Jev with evidence...
Jiangming Zhong, Jiawei Zhu, Guanting Wu et al.· 0 citations
AlphaFold 3-like models (e.g., Boltz, Protenix, etc.) have pushed the frontier of protein binder design. This architecture is used both for the generation of binder candidates (e.g., by hallucination or converting these models into backbone or full atom generative models) as well as for the screening of these candidate...
Lorenz Vaitl, Ivan Tanasijevic, Arian Rokkum Jamasb et al.· 0 citations
Recent work has shown that gradient updates in federated learning (FL) can unintentionally reveal sensitive information about a client's local data. This risk becomes significantly greater when a malicious server manipulates the global model to provoke information-rich updates from clients. In this paper, we adopt a de...
Agentic workflows are increasingly shifting towards long-horizon tasks, such as open-ended scientific work, where models benefit from long context windows. However, at ultra-long context (above 1M tokens), full attention is prohibitively expensive. A promising approach is sparse attention, in which a dedicated indexer...
Ales Kubicek, Andrea Caciolai, Liangzu Peng et al.· 0 citations
In real-world deployments, large language model (LLM) systems are increasingly evolving from single-model invocation toward heterogeneous deployments in which different models are dynamically invoked for inference. When the same context is reused across models, however, each newly selected target model typically has to...
CWAM (Causal WAM), which incorporates the sparsity constraint into a pretrained WAM by adding a sparse routing path to its action head, with the world modeling objective remaining unchanged, is proposed.
Jun-Tao Cheng, Jin-Zhou Tang, Fan Feng et al.· 0 citations
