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Weichao Wang

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Computation and Language cond-mat.mtrl-sci Cryptography and Security

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preprint2026arXiv

CL-bench Life: Can Language Models Learn from Real-Life Context?

Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional settings into everyday life, the nature of the contexts they must handle also shifts. Real-life contexts are often messy, fragmented, and deeply tied to personal and social experience, such as multi-party conversations, personal archives, and behavioral traces. Yet it remains unclear whether current frontier language models can reliably learn from such contexts and solve tasks grounded in them. To this end, we introduce CL-bench Life, a fully human-curated benchmark comprising 405 context-task pairs and 5,348 verification rubrics, covering common real-life scenarios. Solving tasks in CL-bench Life requires models to reason over complex, messy real-life contexts, calling for strong real-life context learning abilities that go far beyond those evaluated in existing benchmarks. We evaluate ten frontier LMs and find that real-life context learning remains highly challenging: even the best-performing model achieves only 19.3% task solving rate, while the average performance across models is only 13.8%. Models still struggle to reason over contexts such as messy group chat histories and fragmented behavioral records from everyday life. CL-bench Life provides a crucial testbed for advancing real-life context learning, and progress on it can enable more intelligent and reliable AI assistants in everyday life.

preprint2022arXiv

IoTMonitor: A Hidden Markov Model-based Security System to Identify Crucial Attack Nodes in Trigger-action IoT Platforms

With the emergence and fast development of trigger-action platforms in IoT settings, security vulnerabilities caused by the interactions among IoT devices become more prevalent. The event occurrence at one device triggers an action in another device, which may eventually contribute to the creation of a chain of events in a network. Adversaries exploit the chain effect to compromise IoT devices and trigger actions of interest remotely just by injecting malicious events into the chain. To address security vulnerabilities caused by trigger-action scenarios, existing research efforts focus on the validation of the security properties of devices or verification of the occurrence of certain events based on their physical fingerprints on a device. We propose IoTMonitor, a security analysis system that discerns the underlying chain of event occurrences with the highest probability by observing a chain of physical evidence collected by sensors. We use the Baum-Welch algorithm to estimate transition and emission probabilities and the Viterbi algorithm to discern the event sequence. We can then identify the crucial nodes in the trigger-action sequence whose compromise allows attackers to reach their final goals. The experiment results of our designed system upon the PEEVES datasets show that we can rebuild the event occurrence sequence with high accuracy from the observations and identify the crucial nodes on the attack paths.

preprint2011arXiv

Field emission from atomically thin edges of reduced graphene oxide

Point sources exhibit low threshold electron emission due to local field enhancement at the tip. The development and implementation of tip emitters have been hampered by the need to position them sufficiently apart to achieve field enhancement, limiting the number of emission sites and therefore the overall current. Here we report low threshold field (< 0.1V/um) emission of multiple electron beams from atomically thin edges of reduced graphene oxide (rGO). Field emission microscopy (FEM) measurements show evidence for interference from emission sites that are separated by a few nanometers, suggesting that the emitted electron beams may be coherent. Based on our high-resolution transmission electron microscopy, infrared spectroscopy and simulation results, field emission from the rGO edge is attributed to a stable and unique aggregation of oxygen groups in the form of cyclic edge ethers. Such closely spaced electron beams from rGO offer prospects for novel applications and understanding the physics of linear electron sources.