TJS Articles & News

Explaining Group Recommendations via Counterfactualscs.AI updates on arXiv.org arXiv:2601.16882v1 Announce Type: cross
Abstract: Group recommender systems help users make collective choices but often lack transparency, leaving group members uncertain about why items are suggested. Existing explanation methods focus on individuals, offering limited support for groups where multiple preferences interact. In this paper, we propose a framework for group counterfactual explanations, which reveal how removing specific past interactions would change a group recommendation. We formalize this concept, introduce utility and fairness measures tailored to groups, and design heuristic algorithms, such as Pareto-based filtering and grow-and-prune strategies, for efficient explanation discovery. Experiments on MovieLens and Amazon datasets show clear trade-offs: low-cost methods produce larger, less fair explanations, while other approaches yield concise and balanced results at higher cost. Furthermore, the Pareto-filtering heuristic demonstrates significant efficiency improvements in sparse settings.

 arXiv:2601.16882v1 Announce Type: cross
Abstract: Group recommender systems help users make collective choices but often lack transparency, leaving group members uncertain about why items are suggested. Existing explanation methods focus on individuals, offering limited support for groups where multiple preferences interact. In this paper, we propose a framework for group counterfactual explanations, which reveal how removing specific past interactions would change a group recommendation. We formalize this concept, introduce utility and fairness measures tailored to groups, and design heuristic algorithms, such as Pareto-based filtering and grow-and-prune strategies, for efficient explanation discovery. Experiments on MovieLens and Amazon datasets show clear trade-offs: low-cost methods produce larger, less fair explanations, while other approaches yield concise and balanced results at higher cost. Furthermore, the Pareto-filtering heuristic demonstrates significant efficiency improvements in sparse settings. Read More  

Will It Survive? Deciphering the Fate of AI-Generated Code in Open Sourcecs.AI updates on arXiv.org arXiv:2601.16809v1 Announce Type: cross
Abstract: The integration of AI agents as coding assistants into software development has raised questions about the long-term viability of AI agent-generated code. A prevailing hypothesis within the software engineering community suggests this code is “disposable”, meaning it is merged quickly but discarded shortly thereafter. If true, organizations risk shifting maintenance burden from generation to post-deployment remediation. We investigate this hypothesis through survival analysis of 201 open-source projects, tracking over 200,000 code units authored by AI agents versus humans. Contrary to the disposable code narrative, agent-authored code survives significantly longer: at the line level, it exhibits a 15.8 percentage-point lower modification rate and 16% lower hazard of modification (HR = 0.842, p < 0.001). However, modification profiles differ. Agent-authored code shows modestly elevated corrective rates (26.3% vs. 23.0%), while human code shows higher adaptive rates. However, the effect sizes are small (Cram’er’s V = 0.116), and per-agent variation exceeds the agent-human gap. Turning to prediction, textual features can identify modification-prone code (AUC-ROC = 0.671), but predicting when modifications occur remains challenging (Macro F1 = 0.285), suggesting timing depends on external organizational dynamics. The bottleneck for agent-generated code may not be generation quality, but the organizational practices that govern its long-term evolution.

 arXiv:2601.16809v1 Announce Type: cross
Abstract: The integration of AI agents as coding assistants into software development has raised questions about the long-term viability of AI agent-generated code. A prevailing hypothesis within the software engineering community suggests this code is “disposable”, meaning it is merged quickly but discarded shortly thereafter. If true, organizations risk shifting maintenance burden from generation to post-deployment remediation. We investigate this hypothesis through survival analysis of 201 open-source projects, tracking over 200,000 code units authored by AI agents versus humans. Contrary to the disposable code narrative, agent-authored code survives significantly longer: at the line level, it exhibits a 15.8 percentage-point lower modification rate and 16% lower hazard of modification (HR = 0.842, p < 0.001). However, modification profiles differ. Agent-authored code shows modestly elevated corrective rates (26.3% vs. 23.0%), while human code shows higher adaptive rates. However, the effect sizes are small (Cram’er’s V = 0.116), and per-agent variation exceeds the agent-human gap. Turning to prediction, textual features can identify modification-prone code (AUC-ROC = 0.671), but predicting when modifications occur remains challenging (Macro F1 = 0.285), suggesting timing depends on external organizational dynamics. The bottleneck for agent-generated code may not be generation quality, but the organizational practices that govern its long-term evolution. Read More  

Finite-Time Analysis of Gradient Descent for Shallow Transformerscs.AI updates on arXiv.org arXiv:2601.16514v1 Announce Type: cross
Abstract: Understanding why Transformers perform so well remains challenging due to their non-convex optimization landscape. In this work, we analyze a shallow Transformer with $m$ independent heads trained by projected gradient descent in the kernel regime. Our analysis reveals two main findings: (i) the width required for nonasymptotic guarantees scales only logarithmically with the sample size $n$, and (ii) the optimization error is independent of the sequence length $T$. This contrasts sharply with recurrent architectures, where the optimization error can grow exponentially with $T$. The trade-off is memory: to keep the full context, the Transformer’s memory requirement grows with the sequence length. We validate our theoretical results numerically in a teacher-student setting and confirm the predicted scaling laws for Transformers.

 arXiv:2601.16514v1 Announce Type: cross
Abstract: Understanding why Transformers perform so well remains challenging due to their non-convex optimization landscape. In this work, we analyze a shallow Transformer with $m$ independent heads trained by projected gradient descent in the kernel regime. Our analysis reveals two main findings: (i) the width required for nonasymptotic guarantees scales only logarithmically with the sample size $n$, and (ii) the optimization error is independent of the sequence length $T$. This contrasts sharply with recurrent architectures, where the optimization error can grow exponentially with $T$. The trade-off is memory: to keep the full context, the Transformer’s memory requirement grows with the sequence length. We validate our theoretical results numerically in a teacher-student setting and confirm the predicted scaling laws for Transformers. Read More  

What is Clawdbot? How a Local First Agent Stack Turns Chats into Real AutomationsMarkTechPost Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that
The post What is Clawdbot? How a Local First Agent Stack Turns Chats into Real Automations appeared first on MarkTechPost.

 Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that
The post What is Clawdbot? How a Local First Agent Stack Turns Chats into Real Automations appeared first on MarkTechPost. Read More  

Researchers tested AI against 100,000 humans on creativityArtificial Intelligence News — ScienceDaily A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling.

 A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling. Read More  

SAM 3 vs. Specialist Models — A Performance BenchmarkTowards Data Science Why specialized models still hold the 30x speed advantage in production environments
The post SAM 3 vs. Specialist Models — A Performance Benchmark appeared first on Towards Data Science.

 Why specialized models still hold the 30x speed advantage in production environments
The post SAM 3 vs. Specialist Models — A Performance Benchmark appeared first on Towards Data Science. Read More  

Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1Towards Data Science Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows
The post Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 appeared first on Towards Data Science.

 Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows
The post Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 appeared first on Towards Data Science. Read More  

How to Build a Neural Machine Translation System for a Low-Resource LanguageTowards Data Science An introduction to neural machine translation
The post How to Build a Neural Machine Translation System for a Low-Resource Language appeared first on Towards Data Science.

 An introduction to neural machine translation
The post How to Build a Neural Machine Translation System for a Low-Resource Language appeared first on Towards Data Science. Read More  

Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter CodeTowards Data Science Understand air quality: access the available data, interpret data types, and execute starter codes
The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science.

 Understand air quality: access the available data, interpret data types, and execute starter codes
The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science. Read More