The Most Common Questions About Agent Evals, Answered
Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down. Context & related coverage →
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Top signals · Oct 14–15, 2026 · Updated Oct 15, 19:00 UTC
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference
Reliable medical reasoning requires LLMs to dynamically access external biomedical evidence to improve factual trustworthiness and reasoning accuracy. However, indiscriminate retrieval can introduce noise and latency,... Context & related coverage →
P2Patch: Exploit-Guided Vulnerability Repair
Large language models are increasingly deployed as autonomous agents for automated program repair, patching software vulnerabilities through multi-step reasoning and tool use. Yet these systems typically validate a pa... Context & related coverage →
AI is mass decompiling games. That's both awesome and scary.
AI is mass decompiling games. That's both awesome and scary. Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and pr... Context & related coverage →
Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents
Frontier web agents using closed models have high inference and computational costs. Smaller open models can be an alternative, but to be performant, they require training supervision from a human or teacher model. To... Context & related coverage →
Tree Appreciator
Tool: Tree Appreciator Another game experiment with Claude. This one used the default Claude iPhone app (chat mode, not Claude Code mode) where I prompted Claude Opus 5.5 Max with: Build a three js game called "Tree a... Context & related coverage →
Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy
Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based m... Context & related coverage →
Delta Tokenizer: Reference-Identical Tokenization of Session Deltas and Segment-Parallelism for Long Prompts on Multicore CPUs
An agent that drives an LLM sends the model the same conversation again and again, and each turn adds only a little text at the end. Inference engines reuse the KV cache of the shared prefix, so only the new tokens pa... Context & related coverage →
New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents
Multi-session agents can switch between LLMs to balance performance and inference cost. Such switches require transferring a history for continuity, yet their effects on the recipient remain underexplored. Through con... Context & related coverage →
Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents
At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control. Context & related coverage →
Feature-Led AI Glasses: Alibaba Prices Qwen N1 Frames at $450
Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio
How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists. Context & related coverage →
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