The paper investigates whether small large language model agents can operate within explicit wall‑clock time budgets while using the allotted time productively. Experiments with Qwen3.6‑27B on MLE‑Bench Lite and Qwen3‑4B on Zork I show that agents fail to translate budget statements into controlled time use, largely due to lack of timing feedback and poor anticipation of action durations. Interventions such as harness‑based timing exposure, enforcement hooks, and reinforcement learning with budget‑aware rewards improve budget adherence but do not lead to better task performance, revealing a persistent gap between respecting time limits and effectively allocating extra time.
By Aaron Wang, Neelabh Madan, Vlad Sobal, Matthew Trager, Michael Kleinman, Elman Mansimov, Wei Xia, Stefano Soatto
arXiv:2607. 28399v1 Announce Type: new Abstract: Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed.
By Zihan Dong, Rui Qian, Qishi Zhan, Dongshen Peng, Kaixin Li, Yu Li
Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed. We identify the main cause as expensive autoregressive decoding on the decision-time critical path.
arXiv:2609.17416v1 Announce Type: new
Abstract: Voice agents built on LLMs follow a rigid listen-think-speak loop that inserts seconds of dead air before every reply. We show that continuous-time cog...
By Bojie Li, Noah Shi
The paper introduces NP‑Bench, a benchmark and a proactive scheduling planner that coordinates parallel large‑language‑model coding agents. By partitioning work scopes and ordering merges ahead of time, the planner improves clean‑integration rates from 1/9 to 9/9 and eliminates merge conflicts, outperforming both no‑coordination and reactive‑detection baselines. It also demonstrates that cross‑session memory can eliminate repeated mistakes and that routing facts to agents does not improve long‑context accuracy at scale.
By Sumanyu Muku
ReLiveGym is a diagnostic environment that evaluates long‑lived language‑model agents over weeks of chronologically replayed real‑world streams such as news, market data, and social media. The tasks vary in time sensitivity, reasoning depth, and recurrence, and the study tests eight base language models to see how model choice and harness design—especially action timing—affect performance. Continuous learning from hindsight feedback is also examined to address failure modes in these long‑term tasks.
By Xisen Jin, Jingheng Li, Zhenglun Chen, Junyi Du, Xiang Ren