activescott's Notes

Squadrismo (Italian: [skwaˈdrizmo]) was the movement of squadre d'azione (English: action squads), the fascist militias that were organised outside the authority of the Italian state and led by local leaders called ras (a noble Ethiopian title). The militia originally consisted of farmers and middle-class people, who created their own defence from revolutionary socialists. Squadrismo became an important asset for the rise of the National Fascist Party, led by Benito Mussolini, and systematically used violence to eliminate any political parties that were opposed to Italian fascism.

The violence was not only an instrument in politics but also a vital component of squadrismo identity, which made it difficult for the movement to be tamed. That was shown in the various attempts by Mussolini to control squadrismo violence with the Pact of Pacification and later the Consolidated Public Safety Act. Squadrismo, which ultimately became the Blackshirts, served as a source of inspiration for Adolf Hitler's Sturmabteilung.

The Blackshirts, formally established as the Squadrismo in 1919, comprised numerous disgruntled demobilized soldiers. It was given the task of leading fights against their bitter enemies—the Socialists.

The Department of Justice is acknowledging it has removed from its website news releases about criminal cases related to the 6 January 2021 Capitol attack, calling the information about the prosecutions “partisan propaganda”.

The purge of news releases documenting criminal charges, convictions and sentencings is the latest step by the Trump administration to dramatically rewrite the history of the assault on the US Capitol, when hundreds of supporters of Donald Trump stormed the building in an effort to halt the congressional certification of his 2020 election loss to Democrat Joe Biden.

Trump, on his first day back in office in January 2025 , pardoned, commuted the prison sentences or vowed to dismiss the cases of all of the 1,500-plus people charged with crimes during the Capitol assault, including those convicted of attacking officers with makeshift weapons such as flagpoles, a hockey stick and a crutch.

Trump dismisses $10bn suit against IRS and creates $1.7bn ‘anti-weaponization’ fund Read more On Monday, the justice department announced the creation of a $1.776bn fund meant to compensate Trump allies who feel they were unjustly investigated and prosecuted. The acting attorney general, Todd Blanche, has not ruled out that rioters convicted of violence will be eligible for payouts, prompting bipartisan anger in Congress.

After a journalist on Friday observed on the social media platform X that the justice department was “quietly” removing news releases on its website that were related to the January 6 attack, including about a Texas man who pleaded guilty to assault and also faced separate state charges of soliciting a minor, the department responded through its “rapid response” account that there was “nothing ‘quiet’ about it”.

Among the releases removed from the site were those concerning seditious conspiracy cases against members of the Proud Boys and the Oath Keepers, far-right extremist groups. The justice department, in an unopposed motion last month, asked a federal appeals court to vacate those seditious conspiracy convictions, a request that was granted on Thursday. The department on Friday moved to dismiss the cases against the group members.

SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields conflicting, noisy, or unhelpful results. SealQA comes in three flavors: (1) Seal-0 (main) and (2) Seal-Hard, which assess factual accuracy and reasoning capabilities, with Seal-0 focusing on the most challenging questions where chat models (e.g., GPT-4.1) typically achieve near-zero accuracy; and (3) LongSeal, which extends SealQA to test long-context, multi-document reasoning in "needle-in-a-haystack" settings. Our evaluation reveals critical limitations in current models: Even frontier LLMs perform poorly across all SealQA flavors. On Seal-0, frontier agentic models equipped with tools like o3 and o4-mini achieve only 17.1% and 6.3% accuracy, respectively, at their best reasoning efforts. We find that advanced reasoning models such as DeepSeek-R1-671B and o3-mini are highly vulnerable to noisy search results. Notably, increasing test-time compute does not yield reliable gains across o3-mini, o4-mini, and o3, with performance often plateauing or even declining early.

benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam, a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. The dataset consists of 2,500 challenging questions across over a hundred subjects. We publicly release these questions, while maintaining a private test set of held out questions to assess model overfitting.

Accuracy. All frontier models achieve low accuracy on Humanity's Last Exam, highlighting significant room for improvement in narrowing the gap between current LLMs and expert-level academic capabilities on closed-ended questions.

Hahaha current state of the art is Gemini 3 Pro w/ 38.3:

Given the rapid pace of AI development, it is plausible that models could exceed 50% accuracy on HLE by the end of 2025.

Data and code for our paper FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation.

We believe that human evaluators possess the expertise and common sense required to detect issues like hallucinations, making them more reliable than automated evaluation metrics for assessing LLMs' factuality. However, researchers have the flexibility to adjust their evaluation methods if human evaluation proves challenging. An easily implemented alternative is to use standard metrics like F1/exact match or recall, which assess the overlap between the model response and the ground truth answer(s) (e.g., see You.com's recent blog where they report FreshQA recall). Researchers can also use LLM-based automatic evaluation metrics such as FactScore or our FreshEval metric below.

To facilitate future evaluations, we have developed FreshEval, a simple automatic metric that uses few-shot in-context learning to teach an LLM to judge model responses, which achieved high agreement with human raters (see Appendix B in our paper for details).

To use FreshEval under a specific evaluation mode (Relaxed or Strict), please follow the instructions below:

Make a copy of our latest data spreadsheet and store it in your Google Drive with a new filename (e.g., fresheval_relaxed or fresheval_strict).
Insert 3 new columns D, E, F in the new spreadsheet for model responses, evaluation rating, evaluation explanation, respectively and save your model's responses in column D (see our sample evaluation spreadsheet below).
Run the associated FreshEval notebook with the evaluation mode. Note that for demonstration purposes, we evaluated only the first 10 model responses. You can adjust the number as needed.

Note: Currently, we recommend gpt-4-1106-preview over gpt-4-0125-preview for FreshEval as it yielded slightly better agreement with human annotations in our small-scale evaluation.

we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked.

Through human evaluations involving more than 50K judgments, we shed light on limitations of these models and demonstrate significant room for improvement: for instance, all models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises.

we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA by incorporating relevant and up-to-date information retrieved from a search engine into the prompt. Our experiments show that FreshPrompt outperforms both competing search engine-augmented prompting methods such as Self-Ask (Press et al., 2022) as well as commercial systems such as this http URL. Further analysis of FreshPrompt reveals that both the number of retrieved evidences and their order play a key role in influencing the correctness of LLM-generated answers. Additionally, instructing the LLM to generate concise and direct answers helps reduce hallucination compared to encouraging more verbose answers.

FRAMES offers a unified framework for assessing LLM performance in end-to-end RAG scenarios. Our dataset comprises challenging multi-hop questions requiring integration of information from multiple sources. Baseline results show that even state-of-the-art LLMs struggle, achieving 0.408 accuracy without retrieval. However, our proposed multi-step retrieval pipeline significantly improves accuracy to 0.66 (>50% improvement).

Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.

If you are building with LLMs, creating high quality evals is one of the most impactful things you can do. Without evals, it can be very difficult and time intensive to understand how different model versions might affect your use case.

July 2025: simple-evals will no longer be updated for new models or benchmark results. The repo will continue to host reference implementations for HealthBench, BrowseComp, and SimpleQA.

Evals are sensitive to prompting, and there's significant variation in the formulations used in recent publications and libraries. Some use few-shot prompts or role playing prompts ("You are an expert software programmer..."). These approaches are carryovers from evaluating base models (rather than instruction/chat-tuned models) and from models that were worse at following instructions.

For this library, we are emphasizing the zero-shot, chain-of-thought setting, with simple instructions like "Solve the following multiple choice problem". We believe that this prompting technique is a better reflection of the models' performance in realistic usage.

We will not be actively maintaining this repository and monitoring PRs and Issues. In particular, we're not accepting new evals. Here are the changes we might accept.

Bug fixes (hopefully not needed!)
Adding adapters for new models
Adding new rows to the table below with eval results, given new models and new system prompts.

This repository is NOT intended as a replacement for https://github.com/openai/evals, which is designed to be a comprehensive collection of a large number of evals.

The OWASP GenAI Security Project is a global, open-source initiative dedicated to identifying, mitigating, and documenting security and safety risks associated with generative AI technologies, including large language models (LLMs), agentic AI systems, and AI-driven applications.

At Lasso, we have been building Intent Security, a runtime security framework that ensures every component in the agentic system behaves as intended. It monitors the behavior of each component and analyzes their alignment. Like auto mode, when alignment holds it allows actions to proceed. When misalignment is detected, it intervenes. When we read Anthropic's post, the overlap in core assumptions was hard to miss. This post provides a comparison of the two approaches.

Independent evaluation without cross-contamination is what enables misalignment detection.

‍Anthropic's input layer screens external content for injection attempts before it reaches the agent to determine whether tool outputs are safe. The output layer structurally evaluates whether the agent's tool calls are aligned with user intent. Critically, the output classifier never sees tool results, to prevent compromised external content from influencing the security decision.

Anthropic publishes the history of system prompts used on claude.ai and the mobile apps at https://platform.claude.com/docs/en/release-notes/system-prompts. That page is a single monolithic markdown document grouped by model, and each model lists one or more dated revisions.

Extracted system prompts from Anthropic - Opus 4.7, Opus 4.6, Sonnet 4.6. OpenAI - ChatGPT 5.5 Thinking, GPT 5.5 Instant, Codex. Google Gemini - 3.5 Flash, 3.1 Pro, 3 Flash, Antigravity. xAI - Grok. Github Copilot. Perplexity, and more. Updated regularly.

Dataset

We evaluated search providers against five open benchmarks covering complementary aspects of agentic search: BrowseComp (hard multi-hop questions that require navigating the live web), Frames (multi-document factoid reasoning), FreshQA (time-sensitive questions where the correct answer depends on recent web information), HLE (Humanity's Last Exam — expert-level academic questions spanning math, science, and humanities), SealQA (ambiguity-robust factoid QA with intentionally misleading snippets), WebWalker (tasks designed around following links across pages to find an answer).

Evaluation methodology

Every task is run through a shared deep-research harness: a single GPT-5.4 agent is given two tools (web search and web fetch) with an iterative budget of up to MAX_TOOL_CALLS=25 tool calls per question. The agent plans sub-queries, fans out searches, fetches specific pages when snippets are insufficient, and returns an answer when it exhausts the number of allowed tool calls or has sufficient information to answer the question. Each answer is then LLM-graded by GPT-5.4. We report accuracy of the final answer.

We measure accuracy and overall cost, which includes LLM token costs and tool call costs.

Testing dates

April 19-21, 2026

In cooperative game theory, the Shapley value is a method (solution concept) for fairly distributing the total gains or costs among a group of players who have collaborated. For example, in a team project where each member contributed differently, the Shapley value provides a way to determine how much credit or blame each member deserves.

In essence, it calculates each player's average marginal contribution across all possible coalitions.

The highest accuracy web search for your AI

Why use Parallel Search vs. the default search in Claude?

Parallel runs its own web-scale index (billions of pages, millions added daily) and returns dense, query-relevant excerpts instead of raw HTML or SEO-ranked snippets. On public benchmarks, Parallel outperforms the default search in leading frontier models. Your agent reaches the right answer in fewer round trips and with less wasted context. – https://parallel.ai/blog/free-web-search-mcp

The Push API gives web applications the ability to receive messages pushed to them from a server, whether or not the web app is in the foreground, or even currently loaded, on a user agent. This lets developers deliver asynchronous notifications and updates to users that opt in, resulting in better engagement with timely new content.