PAWM steers black-box LLM agents by anticipating pitfalls
Original title: Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
Authors report Claude Sonnet 4.5 gains of 24.3% on ScienceWorld and 7.7% on WebArena-Lite.
Pitfall Anticipation World Model selectively intervenes when a potential failure is near and predicts that failure so a black-box LLM agent can avoid it, rather than forecasting every next observation. It is trained with supervised fine-tuning on synthetic pitfall labels, then GRPO co-training with a black-box LLM in the target environment. The authors say it improves Claude Sonnet 4.5 by 24.3% on ScienceWorld and 7.7% on WebArena-Lite and beats several baselines.
Unconfirmed: The percentage gains are the authors' measurements and are not independently confirmed.
- Guidance is invoked only when a bounded multi-step pitfall is anticipated.
- Training uses synthetic pitfall labels, then GRPO co-training.
- Reported gains are 24.3% on ScienceWorld and 7.7% on WebArena-Lite.
- The target agents remain black-box frontier LLMs.
From the original article
1 min read at huggingface.co
Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they have no internalized knowledge of how that particular environment unfolds.
By Jiongxiao Wang, Han Ding, Youzhi Luo · huggingface.co
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