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Australia's CBA flags surging AI costs as tasks grow complex, slams 'work slop'
Australia's CBA flags surging AI costs as tasks grow complex, slams 'work slop' SYDNEY, June 2 : The cost of using AI will rise in less predictable ways as companies deploy the technology for complex tasks, the head of Australia's biggest bank said on Tuesday, calling the expense a key emerging management challenge. Commonwealth Bank of Australia CEO Matt Comyn said businesses globally are likely to tighten scrutiny of artificial intelligence-related spending through 2026 as adoption...
AI Rater Discrimination Depends on Scoring Protocol in Complex Clinical Decision-Making
arXiv:2606.03198v1 Announce Type: new Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluation conditions has not been quantitatively characterized. We address this gap through a factorial study of AI rater behavior in adult type 2 diabetes (T2D) pharmacotherapy at 12-month outpatient follow-up, a clinical task involving complex decision-making operationalized across seven evaluation...
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
arXiv:2606.04328v1 Announce Type: new Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM. AI-driven approaches can learn complex nonlinear relationships, generalize across diverse network conditions and enable real-time, scalable and autonomous decision-making....
CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model
arXiv:2606.06099v1 Announce Type: new Abstract: Whether Large Language Models (LLMs) exhibit covert psychological manipulation in complex human-AI interactions has garnered increasing safety concerns. However, existing AI safety benchmarks remain largely restricted to explicit rule compliance and static prompts, failing to capture the dynamic and covert nature of manipulative strategies in multi-turn dialogues. We introduce CogManip, a comprehensive benchmark that evaluates 15 manipulation...
Perplexity CEO tells CNBC one metric will determine who wins the AI race
The companies that can provide the most economic value from the power their AI uses will ultimately command the highest valuations, Perplexity CEO Aravind Srinivas told CNBC on Wednesday. Perplexity is stepping up its focus on agentic AI, a term that refers to AI systems capable of handling more complex tasks beyond simple queries. In February, the company announced Perplexity Computer, an agent it says can execute complex tasks over long periods of time.
The Case for Model Science: Verify, Explore, Steer, Refine
arXiv:2606.01189v1 Announce Type: new Abstract: We argue that the AI community is now ready to move beyond benchmarking and consolidate scattered efforts in model analysis into a systematic discipline, a direction we term Model Science. Complex AI models now serve billions of users, yet our understanding of how they work lags far behind our ability to deploy them. Decades of benchmark-driven research have delivered remarkable progress: extensive leaderboards, a wide range of performance...
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers. Existing frameworks typically assume server-class resources or continuous connectivity, leaving a gap for deeply embedded systems.
PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback
Announce Type: new Abstract: Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex environmental force fields still relies entirely on manual expert intervention, creating a severe bottleneck for large-scale simulation data generation. Existing automated methods primarily focus on material optimization and exhibit severe modality gaps and technical flaws when applied to the vastly more complex force...
Toward Trustworthy Digital Twins in AI Agent-based Wireless Network Optimization: Challenges, Solutions, and Opportunities
arXiv:2511.19961v2 Announce Type: replace Abstract: Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the AI agent powered by reinforcement learning (RL) offers a promising solution, its practical application is limited by prohibitive exploration costs and potential risks in the real world. The emerging digital twin (DT) technology provides a safe and controlled virtual environment for agent training, but its effectiveness...
Breaking the Tuning Barrier: Zero-Hyperparameters Yield Multi-Corner Analysis Via Learned Priors
Announce Type: replace Abstract: Yield Multi-Corner Analysis validates circuits across 25+ Process-Voltage-Temperature corners, resulting in a combinatorial simulation cost of $O(K \times N)$ where $K$ denotes corners and $N$ exceeds $10^4$ samples per corner. Existing methods face a fundamental trade-off: simple models achieve automation but fail on nonlinear circuits, while advanced AI models capture complex behaviors but require hours of hyperparameter tuning per design iteration, forming...