Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle
Generative AI
Topic - Edition
AI Research
Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)
Elegant object matching from several viewpoints The post Computer Vision: SIFT algorithm (Scale Invariant Feature Transform) appeared first on Towards Data Science.
What the Kyoto Vision Means for AI-Powered Science
Seventeen countries have endorsed the Kyoto Vision for AI-powered science. Its push toward autonomous discovery raises questions about computing access and how governments will turn policy ambitions into research…
What If AI Is a Phase in Earth’s Evolution?
Philosopher Benjamin Bratton has a much bigger way of thinking about AI: as the newest step in a billions-of-years process where matter produced life, life produced intelligence, and intelligence learned to build…
How to Build a Cheap, Yet Reliable Model Router With Jev
Model routing can finally be a design choice rather than a system enhancement The post How to Build a Cheap, Yet Reliable Model Router With Jev appeared first on Towards Data Science.
😺 Trump named an AI czar
What Nvidia and AMD’s AI Acquisitions Say About the Next Chip War
Nvidia’s planned Hugging Face acquisition and AMD’s World Labs deal bring both chipmakers closer to the developers and researchers shaping future AI workloads, giving them an earlier view of how compute requirements may…
ArrivalBench: Agent-Generated Data Pipelines Are Correct Once and Wrong Under Time
arXiv:2610.02363v1 Announce Type: new Abstract: Benchmarks for agent-generated data work grade a pipeline by running it once against a fixed snapshot. ArrivalBench instead re-executes the pipeline an agent leaves behind…
Demonstration-Guided Observation Attacks on Black-Box Safe Reinforcement Learning Controllers for Robotic Systems
arXiv:2602.16543v2 Announce Type: replace Abstract: Safe reinforcement learning (Safe RL) learns robotic controllers that optimize task rewards under safety constraints, yet observation perturbations can induce safety…
WebFovea: When the Model Is Right but the Click Is Wrong -- Reliable Round Trips for Vision-Based Web Agents on Live Websites
arXiv:2610.03036v1 Announce Type: new Abstract: We present WebFovea, a vision-based web agent that placed 2nd in the WebRetriever Challenge 2026 with a final score of 57.0 out of 100. The challenge evaluates agents end…
Objects Without Morphisms: What LLMs for Mathematics Do Not Represent
arXiv:2610.03551v1 Announce Type: new Abstract: Large language models (LLMs) have reached expert-level performance on competition mathematics largely through the volume of search placed around them: candidate solutions…
Mitigating Watermark Forgery in Generative Models via Randomized Key Selection
arXiv:2507.07871v5 Announce Type: replace-cross Abstract: Watermarking enables GenAI providers to verify whether content was generated by their models. A watermark is a hidden signal in the content, whose presence can…
A Width-Matched Comparison of Hybrid Quantum-Classical Self-Supervised Learning for Fingerprint Recognition
arXiv:2609.39172v2 Announce Type: replace-cross Abstract: Fingerprint recognition is a widely deployed biometric, but supervised training requires large labeled enrollment sets. Self-supervised learning (SSL) removes…
Do ResNets Route? Sparse Interaction Experts in Residual Networks
arXiv:2610.02907v1 Announce Type: new Abstract: Residual networks execute every block for every input, yet their functional contributions need not be input independent. We formulate a trained ResNet as a set function…
Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning
arXiv:2610.02505v1 Announce Type: new Abstract: Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address…
Denser $\neq$ Better: Limits of On-Policy Self-Distillation for Continual Post-Training
arXiv:2607.01763v2 Announce Type: replace Abstract: Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can…
On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
arXiv:2606.00467v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized…
Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation
arXiv:2610.02700v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal than the…
Sentry: Learning to Recover from LLM Agent Failures at Test Time
arXiv:2610.02994v1 Announce Type: new Abstract: LLM agents often fail mid-task due to invalid tool calls, repeated actions, or poorly grounded reasoning, and learning from these failures is a path to reliability. We…
Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station
arXiv:2510.23463v4 Announce Type: replace Abstract: Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In its prototypical…
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