Hidden-state probes can flag doomed LLM-agent episodes from the first round, and a recall-calibrated cascade of abort gates cuts generated tokens by up to 60% while preserving a chosen success-recall target.
BAGEN: Are LLM Agents Budget-Aware?
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
While agents are increasingly spending more resources, today agent cost is mostly measured only after execution. A Budget-Aware Agent (BAGEN) should treat budget as an active control signal, rather than a passive cost metric. We first systematically define budget estimation as internal budgets (from agent computation) and external budgets (from agent actions). We then formalize budget-awareness as progressive interval estimation: at each step of a plan, an agent should predict an upper and lower bound on remaining budget, and alert when completion is unlikely. Scoring with a rollout-replay protocol, we find consistent failure patterns on four environments and five frontier agents: (1) strong agents do not necessarily have strong budget-awareness, with correlation r=0.35. (2) frontier models are consistently over-optimistic, continue spending on tasks that are unlikely to succeed, instead of alerting the user early. (3) budget-aware signal is actionable and trainable. Early stop saves 28-64% tokens on failed trajectories, and SFT+RL strengthens early stop and alert behavior. (4) precise interval calibration remains challenging, with interval coverage capping at 47% after SFT+RL. Project page: https://ragen-ai.github.io/bagen/
years
2026 2representative citing papers
LLM agent systems accumulate disorder leading to silent failures, formalized by the exponential Entropy Principle S(t) = S0 * e^(alpha * t) with empirically measured alpha, countered by proposed PIG Engine and ADE protocols.
citing papers explorer
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Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Hidden-state probes can flag doomed LLM-agent episodes from the first round, and a recall-calibrated cascade of abort gates cuts generated tokens by up to 60% while preserving a chosen success-recall target.
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Silent Failure in LLM Agent Systems: The Entropy Principle and the Inevitable Disorder of Autonomous Agents
LLM agent systems accumulate disorder leading to silent failures, formalized by the exponential Entropy Principle S(t) = S0 * e^(alpha * t) with empirically measured alpha, countered by proposed PIG Engine and ADE protocols.