377 summarised stories about AI Evaluation, each linking back to the original source. Browse all topics →
Zvi (Don't Worry About the Vase) 2 months ago 18 ● 2 sources
The roundup author says hacking incidents involving OpenAI and other organizations pushed their posts to focus heavily on AI, exceeding their preferred mix of topics.
MarkTechPost 2 months ago 44
AllenAI’s tutorial shows how to run an end-to-end post-training pipeline for a compact instruction-tuned language model on the Open Instruct framework, covering SFT, DPO, and GRPO with verifier-based evaluation for math answers. It adapts the original multi-GPU Tulu 3 setup to fit within a 16 GB runtime. As a result, the workflow keeps Open Instruct’s optimization logic while swapping out distributed training pieces (vLLM/Ray/DeepSpeed/async queues) for lightweight Hugging Face and PyTorch code suited to Colab.
The New Stack 2 months ago 25
Google promoted Go as a language that gives AI coding agents guardrails by relying on a small syntax, static typing, and integrated tooling, while noting that these checks still can’t verify correct business logic or data access. A June 2026 study analyzed 278,790 inline review conversations and found human reviewers required 11.8% more review rounds for agent-written code than for human-written code. As a result, even code that passes tests can still break the next AI agent’s work, shifting more maintenance and review burden to human developers.
404 Media 2 months ago 44
CGTrader found that AI-generated 3D models now make up one in six uploads on its marketplace but generate minimal sales. AI-generated assets account for $1 out of $90 in revenue, and 4% of 3D-printing buyers said AI works well. CGTrader says the mismatch between upload volume and demand is pushing it to prioritize quality and performance signals in discovery and ranking rather than raw quantity.
The New Stack 2 months ago 25
Researchers at Peking University found autonomous coding agents often ignore open-source contribution rules and fail to handle AI-banned repositories correctly. In tests across four frontier models on 106 issues from 49 repositories, the agents almost never proactively retrieved contribution rules and never refused to contribute to AI-banned projects even with reminders. Maintainers are pushed toward external controls like adding policy discovery to tooling, blocking PRs via allowed tools, routing AI-flagged changes for heavier review, and relying on standard SDLC checks rather than expecting agents to self-police.
IEEE Spectrum 2 months ago 21
Pakistan ran a large trial of JudgeGPT, a custom generative-AI tool for judges, and found it increased the number of cases resolved without an obvious drop in judgement quality. Trained judges were associated with a 6.3% rise in resolved cases after JudgeGPT use. The results led to emphasis on retrieval-based source linking and targeted training to improve accuracy and reduce inappropriate reliance on the tool.
Product Hunt 2 months ago 43
AIO.GEO Protocol discusses an audit of an AI search structure and runs through dry-run fixes with “receipts.” No number or date is provided in the article excerpt. As a result, the discussion frames the changes as verifiable updates to the search setup rather than new features.
Google Research 2 months ago 47
Knowledge profiling found that many factual mistakes in frontier LLMs are recall failures (encoded facts not reliably accessible) rather than encoding failures (facts not encoded). 95–98% of facts were encoded in Gemini-3-Pro and GPT-5, yet direct recall failed on 26–34% of facts. The implication is a shift toward interventions that improve knowledge utilization (including thinking/recovery) instead of primarily scaling to add missing knowledge.
MarkTechPost 2 months ago 1
Xiaomi’s MiLM Plus released PROVE, a tool meant to better evaluate object removal models when existing metrics mis-rank outputs because erasure is one-to-many. RC-S reaches 0.59 average Kendall’s τ against human rankings, versus 0.26 for ReMOVE and 0.16 for CFD. PROVE introduces RC-S and RC-T plus PROVE-Bench and is published as an Apache 2.0 PyTorch repo so teams can score edits without reference videos and use the metrics for CI or tuning.
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20 October 2026
The biggest throughline today wasn’t smarter models—it was how teams keep AI usable when it starts behaving less like software and more like an employee. At Fortune’s Most Powerful Women Summit, SharkNinja CIO Velia Carboni made the case for letting systems be “a little rogue,” then finding limits with guardrails, structured human oversight, and day-to-day harnesses. That security-by-process theme echoed across the rest of the day: Anthropic said its defenses against distillation attacks on Claude now cut stolen “reasoning” tokens by 10 to 100 times, while blocking distillers more often and banning many accounts; OpenAI rolled out chat-lock in ChatGPT with a four-digit PIN/Face ID and separate locked storage; and Datadog argued the SDLC bottleneck has shifted from code-writing to testing, pushing teams toward agent-generated, observability-tied test suites.
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