arXiv AI By Seyed Morteza Emadi

When does a scaling result justify a different allocation? A critical review of resource-allocation evidence for AI systems

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arXiv AI
Aug 7

HarnessOpt-Bench: Evaluating LLMs at Harness Optimization

arXiv:2608. 06301v1 Announce Type: new Abstract: As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them.

By Varun Ursekar, Apaar Shanker, Yash Maurya, Shehab Yasser, Vijay S. Kalmath, Veronica Chatrath, Yuan Xue
arXiv AI
Sep 18

The Organization of Inference: Information, Resource Constraints, and AI Production

The paper investigates how the distribution of capacity and task information across stages of AI production affects the economic value of inference. Using controlled workflow experiments on software‑engineering tasks, it finds that direct execution achieves a 59.6% success rate at token ceilings of 12,000 and 24,000, while information‑constrained planning improves from 36.2% to 51.2%. The study also shows that giving planners access to task issues boosts success, and that scaling token limits changes the balance between planning and execution workloads.

By Yukun Zhang, Kemu Xu, Yishen Chen
arXiv AI
Aug 11

The Scaling Paradox in Human-AI Collaboration

arXiv:2608. 00818v2 Announce Type: replace Abstract: The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably.

By Anyan Qi, Mengxin Wang