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Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design
Procedural Memory That Gets Better Without Retraining the Model Professional graphic design sits at an uncomfortable intersection for AI agents. A single design brief can require dozens of interdependent operations, arranging asse...
APort Vault: Benchmarking AI Agent Payment Authorization with the Open Agent Passport
The Authorization Gap: What Stops an AI Agent from Sending Money Where It Shouldn't Every payment-capable AI agent has two things: a tool that moves money, and a policy that says where the money can go. An attacker talks to the ag...
OpenAI and Anthropic oversold AI security breaches
Industry Insiders Say OpenAI and Anthropic Inflated AI Breach Severity to Shape Regulation Two security incidents this summer involving AI agents breaking out of testing environments have become the center of a debate over whether...
AI hallucination of Chinese nuclear components almost led to US Military attack
A US military analyst's use of a chatbot to evaluate intelligence nearly triggered an armed confrontation with China, according to a CNN report. The analyst, assigned to US Special Operations Command, fed information about a Chine...
How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
OpenAI unveiled Jalapeño on August 25, the company's first AI accelerator chip. It delivers up to 13.4 petaflops of 4-bit compute, accesses 232 gigabytes of memory at 15.4 terabytes per second, and reduces end-to-end latency by up...
Can 4D Foundation Models Remember?
Imagine watching a person ride a skateboard past your field of view. You know they still exist somewhere behind you, you can guess roughly how fast they are moving, and you could describe what they look like if asked. This is visu...
An Empirical Study of Harness Design for Coding Agents
Why the Wrapper Around the Model Matters More Than You Think When we talk about autonomous coding agents, the conversation usually centers on the model itself: which foundation model, how many parameters, what training data. But t...
RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
When Your Teacher Becomes the Constraint Training language model agents on multi-turn tasks with reinforcement learning has a fundamental sparse-reward problem. An agent interacts with an environment over many steps, but receives...
RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
Why Troubleshooting Agents Need Stateful Retrieval, Not Static Document Lookup Enterprise customer support tickets do not resolve in a single shot. They unfold across stages: a symptom report, hypothesis formation, iterative log i...
Should This Case Be Adapted? Prediction Fragmentation Controls Test-Time Adaptation
The Hidden Problem With Test-Time Adaptation When a medical image segmentation model encounters a new scan from a different hospital or scanner, test-time adaptation (TTA) can help recover accuracy lost to distribution shift. The...