arXiv AI
Aug 7
EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
BAGEN: Are LLM Agents Budget-Aware?
arXiv:2606. 00198v1 Announce Type: cross Abstract: While agents are increasingly spending more resources, today agent cost is mostly measured only after execution.
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arXiv Machine Learning
Jul 30
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
arXiv:2607. 26828v1 Announce Type: new Abstract: Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates.
Hugging Face Trending Papers
Jul 29
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers assign credit based only on score progress, even though prompt length, retries, and guidance calls cause search actions to incur different token costs.
Hugging Face Trending Papers
Aug 4
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before completion.
arXiv AI
Aug 5
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
arXiv:2608. 03222v1 Announce Type: cross Abstract: Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates.
arXiv AI
Jul 15
RCWT: Measuring Task-Budget Displacement from Coordination Content in LLM Calls
arXiv:2607. 12216v1 Announce Type: cross Abstract: Multi-agent and memory-augmented LLM systems often place coordination content, shared state, prior discussion, tool outputs, summaries, and role instructions, inside the same finite prompt used for the current task.
arXiv AI
Jul 16
When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents
arXiv:2602. 11619v2 Announce Type: replace Abstract: Running the same LLM agent on identical inputs yields 2.
arXiv AI
Jul 22
CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents
arXiv:2607. 19338v1 Announce Type: new Abstract: Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer.
arXiv Machine Learning
Jun 26
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
arXiv:2606. 26463v1 Announce Type: new Abstract: Deliberating takes time.
Hugging Face Trending Papers
Aug 11
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments.
arXiv AI
Jun 3
Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
arXiv:2606. 03965v1 Announce Type: cross Abstract: Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control.
Hugging Face Trending Papers
Jul 21
CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents
Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer. Existing cost-aware systems typically treat such failures as cascade decisions: try a cheap model first, then escalate hard cases to a stronger and more expensive model.