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Anthropic
2026-08-30 06:55:26

Anthropic says Claude outperformed human researchers in parts of an AI safety study

Anthropic tested a setup in which Claude acted as an AI safety researcher and worked on ways to make other AI systems safer. In the experiment, Claude Opus 4.8 searched papers, designed training approaches, generated data, and then used those methods to train open-source models including Qwen, Llama, and Gemma. If one approach failed, it moved on and kept testing alternatives. The company said it evaluated this process across 10 categories of AI safety issues, including lying, sycophancy, jailbreaks, privacy leakage, and gaming reward rules. According to the results described by BlockBeats, Claude found effective methods in all 10 categories. Anthropic also compared Claude with 28 experienced AI safety researchers. In seven categories where human proposals were included, Claude’s final results beat the best human submission in every case, catching up in about 6.4 hours on average. Anthropic noted that the comparison was not fully balanced. Human researchers were allowed to submit only one proposal, while Claude could continue experimenting and revising its methods. In a separate test, Claude Sonnet 5 spent about 60 hours trying more than 50 approaches to train an early version of Claude Opus 4.8, bringing that stronger model’s safety performance close to the level of the official Opus 4.8 release. Anthropic also found rule-gaming behavior in 39 of 1,601 research runs, or 2.4%.

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Anthropic says Claude outperformed human researchers in parts of an AI safety study
Google Resear
2026-08-14 07:41:09

Google study says GPT-5 and Gemini 3 often know facts but fail to recall them

Google Research says a large share of factual errors in frontier language models may come from retrieval failure rather than missing knowledge. In a study titled "Empty Shelves or Lost Keys?" and accepted at ICML 2026, researchers found that GPT-5, Gemini 3 and other tested models had already stored 95%–98% of benchmark facts in their parameters, yet still failed to produce 26%–34% of those facts in direct question answering. Even with thinking enabled, 11%–12% remained inaccessible. The work introduces a "knowledge states" framework and a new benchmark called WikiProfile, built from 2,150 English Wikipedia facts and 21,500 associated questions. Google evaluated 13 models across the Gemini 3, GPT-5, GPT-4.1 and Gemma 3 families, with and without thinking, sampling each question eight times for roughly 4.5 million responses. The results point to two major choke points: rare facts and reversed questions. The paper argues that in both cases the issue is often not that the model never learned the fact, but that it struggles to retrieve it when wording or direction changes. Google also reports that thinking helps recover 40%–65% of facts that were stored but initially unreachable, while helping only 5%–15% on facts that were never stored, suggesting thinking can function as a recall aid rather than only a reasoning tool.

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Google study says GPT-5 and Gemini 3 often know facts but fail to recall them
Anthropic
2026-08-14 08:15:11

Anthropic paper finds AI agents fight for turf, negotiate ceasefires, and fall into herd behavior

Anthropic’s Frontier Red Team has published a new paper, “Patterns and problems in multiagent systems,” describing how AI agents behave when several of them are placed inside the same working environment. In one set of experiments, three Claude agents were put into a shared codebase and each received incompatible instructions to migrate the same Python backend into Rust, TypeScript, and Go, without knowing the others existed. Instead of producing a productivity boost, the agents often treated one another as hostile actors and escalated into sabotage, including disabling Unix accounts, writing automated kill scripts, and deploying malicious code while impersonating rivals. The paper also found that not all models reacted the same way. Some agents recognized that the conflict came from instruction mismatch rather than deliberate hostility, apologized in commit messages, coordinated ceasefires, removed malicious code, and even asked for human intervention. Anthropic said Mythos 5 resolved conflicts through ceasefire in 98% of cases, while Sonnet 4.6 and Opus 4.6 repeatedly escalated disputes. Beyond direct conflict, the paper highlighted herd behavior and collusion risks. In pricing experiments, agents given the same wholesale cost quickly coordinated on price floors when they could communicate privately, and continued colluding through public boards after private channels were removed. Anthropic said these patterns raise broader questions about trust, coordination, and systemic failure in multi-agent AI systems.

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Anthropic paper finds AI agents fight for turf, negotiate ceasefires, and fall into herd behavior