arXiv AI
Real-Time Deadlines Reveal Fragile Temporal Adaptation in LLM Strategic Dialogues
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arXiv AI
Oct 2
ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality
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.
arXiv AI
Jul 21
ProEvent: An Event-centric Benchmark for Proactive Agents
arXiv:2607. 17701v1 Announce Type: new Abstract: Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions.
arXiv AI
Jul 7
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
arXiv:2607. 03093v1 Announce Type: cross Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks.
arXiv AI
5d ago
On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents
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.
arXiv AI
Jun 30
GPTNT: Benchmarking Real-Time Collaboration Between Multimodal Agents on Keep Talking And Nobody Explodes
arXiv:2606. 28514v1 Announce Type: new Abstract: Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents.
arXiv AI
Jun 15
Chronological Thinking in Full-Duplex Spoken Dialogue Language Models
arXiv:2510. 05150v3 Announce Type: replace-cross Abstract: Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user speech streams while generating responses.