arXiv AI
TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents
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An Empirical Cost Attribution of Context-Compression Gateways in Multi-Turn Coding Agents
arXiv:2609.22114v1 Announce Type: new Abstract: Context compression is widely proposed as a way to cut the token bill of LLM coding agents, and public benchmarks report that aggressive compression pr...
arXiv AI
Oct 15
LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering
LoopArena is a benchmark that evaluates how effectively a model (the Controller) can guide a separate coding agent (the Worker) through long-running tasks by monitoring progress, assigning work, and deciding when to stop. It consists of three settings: Type I with 160 low‑cost control questions, Type II with 80 task slices, and Type III with 80 full tasks. In full tasks, the best observed strict success rate is 36.25%, and Controllers can reduce inference cost by an average of 60.6% while maintaining a strong correlation in performance ranking.
arXiv AI
Jul 21
TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
arXiv:2607. 18161v1 Announce Type: cross Abstract: Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping.
arXiv Machine Learning
Aug 4
Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale
arXiv:2608. 00101v1 Announce Type: cross Abstract: AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots.
arXiv AI
Oct 5
What Does a Token Cost? A Mixture-of-Agents Measurement of Sufficient Per-Token Compute
The paper introduces a Mixture-of-Agents (MoA) approach to quantify the per-token compute required by large language models. By having a panel of fifteen models of varying sizes attempt to reproduce each token, the authors define the smallest successful agent’s inference cost as the token’s sufficient compute, providing an upper bound on necessary computation. Experiments on benchmarks show that a 0.5B model can reproduce most tokens, and that the MoA-derived compute map can reduce latency in model routing and drafting tasks while improving or maintaining accuracy.
arXiv AI
Jul 15
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.