Tootfinder

Cognition releases SWE-1.5, a new coding model in Windsurf, saying it partnered with Cerebras to serve SWE-1.5 at speeds up to 13x faster than Claude Sonnet 4.5 (Cognition)
https://cognition.ai/blog/swe-1-5

Cognition | Introducing SWE-1.5: Our Fast Agent Model
Today we’re releasing SWE-1.5, the latest in our family of models optimized for software engineering. It is a frontier-size model with hundreds of billions of parameters that achieves near-SOTA coding performance. It also sets a new standard for speed: we partnered with Cerebras to serve it at up to 950 tok/s – 6x faster than Haiku 4.5 and 13x faster than Sonnet 4.5. SWE-1.5 is now available in Windsurf!

Artificial phantasia: Evidence for propositional reasoning-based mental imagery in large language models https://arxiv.org/abs/2509.23108 on the representation of visual imagery in humans; more information in the Bluesky thread

Artificial Phantasia: Evidence for Propositional Reasoning-Based Mental Imagery in Large Language Models
This study offers a novel approach for benchmarking complex cognitive behavior in artificial systems. Almost universally, Large Language Models (LLMs) perform best on tasks which may be included in their training data and can be accomplished solely using natural language, limiting our understanding of their emergent sophisticated cognitive capacities. In this work, we created dozens of novel items of a classic mental imagery task from cognitive psychology. A task which, traditionally, cognitive…

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Dong Hyun Roh, Rajesh Kumar
https://arxiv.org/abs/2509.24807 https://

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classific…

Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
Vanessa Figueiredo
https://arxiv.org/abs/2508.21204 https://arxiv.org/…

Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
We study how architectural inductive biases influence the cognitive behavior of large language models (LLMs) in instructional dialogue. We introduce a symbolic scaffolding mechanism paired with a short-term memory schema designed to promote adaptive, structured reasoning in Socratic tutoring. Using controlled ablation across five system variants, we evaluate model outputs via expert-designed rubrics covering scaffolding, responsiveness, symbolic reasoning, and conversational memory. We present …

How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations
Yoshiki Takenami, Yin Jou Huang, Yugo Murawaki, Chenhui Chu
https://arxiv.org/abs/2508.21137

How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations
Cognitive biases, well-studied in humans, can also be observed in LLMs, affecting their reliability in real-world applications. This paper investigates the anchoring effect in LLM-driven price negotiations. To this end, we instructed seller LLM agents to apply the anchoring effect and evaluated negotiations using not only an objective metric but also a subjective metric. Experimental results show that LLMs are influenced by the anchoring effect like humans. Additionally, we investigated the rel…

Understanding Cognitive States from Head & Hand Motion Data
Kaiang Wen, Mark Roman Miller
https://arxiv.org/abs/2509.24255 https://arxiv.org/pdf/2509.2…

Understanding Cognitive States from Head & Hand Motion Data
As virtual reality (VR) and augmented reality (AR) continue to gain popularity, head and hand motion data captured by consumer VR systems have become ubiquitous. Prior work shows that such telemetry can be highly identifying and reflect broad user traits, often aligning with intuitive "folk theories" of body language. However, it remains unclear to what extent motion kinematics encode more nuanced cognitive states, such as confusion, hesitation, and readiness, which lack clear correlates with m…

Rotatable Antenna-Enabled Spectrum Sharing in Cognitive Radio Systems
Yanhua Tan, Beixiong Zheng, Yi Fang, Derrick Wing Kwan Ng, Rui Zhang, Jie Xu
https://arxiv.org/abs/2509.25656

Rotatable Antenna-Enabled Spectrum Sharing in Cognitive Radio Systems
Rotatable antenna (RA) technology has recently drawn significant attention in wireless systems owing to its unique ability to exploit additional spatial degrees-of-freedom (DoFs) by dynamically adjusting the three-dimensional (3D) boresight direction of each antenna. In this letter, we propose a new RA-assisted cognitive radio (CR) system designed to achieve efficient spectrum sharing while mitigating interference between primary and secondary communication links. Specifically, we formulate an …

DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
Rui Jia, Yuang Wei, Ruijia Li, Yuang-Hao Jiang, Xinyu Xie, Yaomin Shen, Min Zhang, Bo Jiang
https://arxiv.org/abs/2509.24821

DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
While cognitive diagnosis (CD) effectively assesses students' knowledge mastery from structured test data, applying it to real-world teacher-student dialogues presents two fundamental challenges. Traditional CD models lack a suitable framework for handling dynamic, unstructured dialogues, and it's difficult to accurately extract diagnostic semantics from lengthy dialogues. To overcome these hurdles, we propose DiaCDM, an innovative model. We've adapted the initiation-response-evaluation (IRE) f…

Designing Smarter Conversational Agents for Kids: Lessons from Cognitive Work and Means-Ends Analyses
Vanessa Figueiredo
https://arxiv.org/abs/2508.21209 https://

Designing Smarter Conversational Agents for Kids: Lessons from Cognitive Work and Means-Ends Analyses
This paper presents two studies on how Brazilian children (ages 9--11) use conversational agents (CAs) for schoolwork, discovery, and entertainment, and how structured scaffolds can enhance these interactions. In Study 1, a seven-week online investigation with 23 participants (children, parents, teachers) employed interviews, observations, and Cognitive Work Analysis to map children's information-processing flows, the role of more knowledgeable others, functional uses, contextual goals, and int…

Think Socially via Cognitive Reasoning
Jinfeng Zhou, Zheyu Chen, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang
https://arxiv.org/abs/2509.22546 https://

Think Socially via Cognitive Reasoning
LLMs trained for logical reasoning excel at step-by-step deduction to reach verifiable answers. However, this paradigm is ill-suited for navigating social situations, which induce an interpretive process of analyzing ambiguous cues that rarely yield a definitive outcome. To bridge this gap, we introduce Cognitive Reasoning, a paradigm modeled on human social cognition. It formulates the interpretive process into a structured cognitive flow of interconnected cognitive units (e.g., observation or…