arXiv AI
Jul 7
Out-of-Distribution Generalization of Risk Aversion in Language Models
arXiv:2607. 02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned.
Decision-Making under Combinatorial Risk
arXiv:2606. 10092v1 Announce Type: new Abstract: Decision-making under risk is typically studied through single-shot lottery choices.
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arXiv Machine Learning
Sep 16
Observational Multiplicity
The paper introduces the concept of observational multiplicity, where multiple probabilistic classifiers can perform similarly yet produce conflicting predictions, undermining interpretability and safety. It proposes measuring this arbitrariness through a regret metric that captures how predictions could shift with different training labels. The authors present a general method to estimate regret, show it varies across dataset groups, and discuss its use for safety via abstention and targeted data collection.
arXiv AI
Jul 14
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.
arXiv AI
Jun 9
Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
arXiv:2605. 19674v2 Announce Type: replace Abstract: Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcomes.
arXiv Machine Learning
Aug 4
BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition
arXiv:2608. 02305v1 Announce Type: new Abstract: Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget.
arXiv AI
Sep 2
LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting
LEAP (Likelihood Elicitation and Aggregation for Probabilistic forecasting) is a new approach that reorganizes how evidence is used in LLM-based forecasting systems. Instead of a monolithic prediction that aggregates all evidence at once, LEAP examines each evidence item separately, elicits likelihood parameters, and combines them with an explicit prior to produce a posterior distribution. The method supports continuous, single-choice, and multi-choice forecasts and has been shown to improve prediction and calibration metrics across models on a benchmark covering forecasting, information-seeking, and browsing tasks.
arXiv Machine Learning
Jun 26
Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees
arXiv:2606. 26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability.
arXiv AI
Jul 14
Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models
arXiv:2607. 10251v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in decision support, it is important to understand whether their choices under uncertainty exhibit stable and interpretable behavioural regularities.
Hugging Face Trending Papers
Jul 13
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns.
arXiv AI
3d ago
On the Complexity of Preference-Based Bandits
The paper investigates preference-based bandits where a learner selects pairs of arms and receives binary preference feedback modeled by Bradley–Terry. It introduces the locally sensitive eluder dimension, a new complexity measure for logistic preference feedback, and proposes the GINOP algorithm that uses log-loss confidence sets to balance optimism and exploration. The authors prove a first-order regret bound showing that learning with preference feedback can be as statistically efficient as learning from direct rewards, and they validate their theory with empirical experiments.
arXiv AI
Aug 13
SportD: How do VLMs physically strategize?
arXiv:2607. 14616v3 Announce Type: replace Abstract: Vision-language models (VLMs) can describe a scene, but can they act well within one?
arXiv AI
Aug 25
Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron
arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inv...