Tootfinder

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership
Martin Lou, Jackie Crowley, Samuel Dodson, Dongwook Yoon
https://arxiv.org/abs/2509.16128 ht…

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership
Generative AI is increasingly integrated into writing support, yet current chat-based interfaces often obscure referential context and risk amplifying automation bias and overreliance. We introduce AnchoredAI, a novel system that anchors AI feedback directly to relevant text spans. AnchoredAI implements two key mechanisms: (1) an Anchoring Context Window (ACW) that maintains unique, context-rich references, and (2) an update-aware context retrieval method that preserves the intent of prior comm…

Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
V\'itor N. Louren\c{c}o, Aline Paes, and Tillman Weyde
https://arxiv.org/abs/2508.10040

Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
The global spread of misinformation and concerns about content trustworthiness have driven the development of automated fact-checking systems. Since false information often exploits social media dynamics such as "likes" and user networks to amplify its reach, effective solutions must go beyond content analysis to incorporate these factors. Moreover, simply labelling content as false can be ineffective or even reinforce biases such as automation and confirmation bias. This paper proposes an expl…

Based AI improves human decision-making but reduces trust
Shiyang Lai, Junsol Kim, Nadav Kunievsky, Yujin Potter, James Evans
https://arxiv.org/abs/2508.09297 https://

Based AI improves human decision-making but reduces trust
Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conducted randomized trials with 2,500 participants to test whether culturally biased AI enhances human decision-making. Participants interacted with politically diverse GPT-4o variants on information evaluation tasks. Partisan AI assistants enhanced human performance, increased engagement, and reduced evaluative bias compa…

Optimal Autonomous MLIP Dataset Building
Vincent G. Fletcher, Albert P. Bart\'ok, Livia B. P\'artay
https://arxiv.org/abs/2508.08864 https://arxiv.…

Optimal Autonomous MLIP Dataset Building
We propose a novel approach for constructing training databases for Machine Learning Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealing due to its ease of automation, its suitability for iterative learning, and its independence from prior knowledge of stable phases, avoiding bias towards pre-existing structural data. The approach uses Nested Sampling (NS) to explore the configuration space…

Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
Tonghe Li, Jixin Liu, Weili Zeng, Hao Jiang
https://arxiv.org/abs/2509.03550 https://

Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
In the context of continuously rising global air traffic, efficient and safe Conflict Detection and Resolution (CD&R) is paramount for air traffic management. Although Deep Reinforcement Learning (DRL) offers a promising pathway for CD&R automation, existing approaches commonly suffer from a "unimodal bias" in their policies. This leads to a critical lack of decision-making flexibility when confronted with complex and dynamic constraints, often resulting in "decision deadlocks." To overcome thi…

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
https://arxiv.org/abs/2508.07617

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI predictions, can result in worse decisions. Selective prediction, in which potentially unreliable model predictions are hidden from users, has been proposed as a solution. This approach assumes that when AI abstains and informs the user so, humans make decisions as they would w…