Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor command (e.g., wrong package install) can change the environment in ways that hinder subsequent progress, even when the model could generate a better alternative. We investigate whether allocating test-time compute at the model-harness boundary can improve action reliability and trajectory success, and what makes this allocation effective.

The growing demand for accelerated computing is driving widespread deployment of multi-accelerator systems in the cloud and at the edge. These systems are temporally shared across processes that may not mutually trust one another and often include large pools of third-party, untrusted accelerators operating within a shared virtual address space. Due to the limited capability of the Input-Output Memory Management Unit (IOMMU) in supporting address translation, SoC designers are integrating large Shared Translation Lookaside Buffers (TLBs) that serve many accelerators and processes.

Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SOTA) acceleration methods heavily rely on variational score distillation (VSD) and adversarial losses to distill diffusion models into few-step generators. Albeit achieving high-quality video generation, these training losses are notoriously hard to optimize and suffer from mode collapse, leading to loss of video diversity and lack of motion.

• Fault-tolerant quantum programs thread together reusable logical operations, yet correcting errors requires comparing measurements across operation boundaries. Correlations tie each operation's error analysis to the computation around it. Adaptive computation makes this a runtime challenge: measurement results determine which operation comes next, so the error analysis must keep pace with execution.

Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are highly vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external data can hijack agent behavior. In this work, we present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks.

Large reasoning models (LRMs) extend large language models with explicit multi-step reasoning traces, but this capability introduces a new class of prompt-induced inference-time denial-of-service (PI-DoS) attacks that exploit the high computational cost of reasoning.

AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions. This position paper discusses our vision for system-level defenses against indirect prompt injection attacks.