Agentic-Priors uses large language models to generate informative priors for preference learning agents, addressing the cold start problem by translating a new user's trade-offs into a prior over multinomial logit coefficients. The Bayesian learner refines this prior as choices are observed, improving cold start accuracy by 5–40 percentage points across seven datasets. Compared to state‑of‑the‑art methods, Agentic-Priors outperforms AutoElicit on three datasets, surpasses in‑context learning by up to 18 points, and is valued at roughly 6–40 surveyed users in log‑loss terms, making it especially useful for early deployment users.
arXiv:2604.03925v2 Announce Type: replace Abstract: Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a mann...
arXiv:2606. 05828v1 Announce Type: new Abstract: As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm.
arXiv:2604. 18576v4 Announce Type: replace Abstract: We present the Bayesian Linguistic Forecaster (BLF), an agentic system for binary forecasting that achieves state-of-the-art performance on the ForecastBench benchmark.
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.
arXiv:2601.17609v3 Announce Type: replace Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that st...
arXiv:2608. 03382v1 Announce Type: cross Abstract: Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation.
arXiv:2607. 03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings.
arXiv:2607. 19739v1 Announce Type: cross Abstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks.
The paper introduces the Pitfall Anticipation World Model (PAWM), a selective guidance system for black-box large language model agents. PAWM predicts imminent failures in a bounded multi-step future and intervenes only when a potential pitfall is detected, reducing costly and often uninformative step-by-step predictions. Trained first with supervised fine-tuning on synthetic pitfall-labeled data and then co-trained with a black-box LLM, PAWM boosts Claude Sonnet 4.5’s performance by 24.3% on ScienceWorld and 7.7% on WebArena-Lite, outperforming several baselines.
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
arXiv:2605. 00696v2 Announce Type: replace-cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets.
arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.