Claude Tag allows teams to tag @Claude in Slack to delegate tasks, with access to selected channels, tools, and codebases. It learns from channel context, works asynchronously, and takes initiative by proactively updating users on relevant information. Today, 65% of Anthropic’s product team code is created by internal Claude Tag, and it’s now available in beta for Claude Enterprise and Team customers.
Claude v2.1.187 introduces sandbox credentials blocking, org-configured model restrictions, mouse click support in fullscreen, and fixes for command failures, tool hangs, and UI stability. Updates also improve structured output handling, agent depth tracking, and plugin management, with enhancements to VSCode and terminal compatibility.
Claude v2.1.186 adds CLI authentication commands for MCP servers, status filtering in workflows, and a "Skills" section in plugin settings. It includes numerous bug fixes for UI, session management, and agent behavior, along with improvements to YAML parsing, memory handling, and tool validation.
v2.1.183 improves auto mode safety by blocking destructive git and destroy commands without explicit user consent. It adds deprecation warnings for models, introduces attribution.sessionUrl to hide session links, and fixes multiple issues including terminal behavior, subagent performance, and input handling in web and tmux environments.
Claude Code v2.1.181 introduces support for setting config settings via prompt syntax like /config thinking=false, adds sandbox Apple Events support on macOS, and improves streaming, auto-retry, and subagent behavior. It also fixes numerous bugs related to startup, file handling, clipboard, and UI responsiveness across platforms.
Claude v2.1.178 introduces new permission rules using Tool(param:value) syntax, improved workflow and skill loading in nested directories, and enhanced auto mode and error messaging. It fixes critical issues including crashes, authentication errors, and UI behavior in Chrome and VSCode, while refining tool prompts and undo functionality.
Claude Code version 2.1.191 introduces /rewind support, allowing users to resume conversations from before a /clear command was executed. The update fixes several critical issues, including background agents resurrecting after being stopped and scroll position jumping during streaming responses. It also corrects behavior where /voice displayed generic error messages and where /login URLs were truncated in Windows Terminal. Significant improvements enhance reliability for MCP servers by adding retry logic for transient network errors during capability discovery and OAuth flows. Headless environments now skip browser popups for OAuth, while sandbox network permissions are remembered for the session duration. Performance optimizations reduce CPU usage during streaming by approximately 37% through text update coalescing and mitigate long-session memory growth from the terminal output cache.
The EU AI Act requires all AI systems generating synthetic text to include machine-readable, detectable watermarks using robust, interoperable technical solutions with two layers. This applies to all AI models, including open-source ones, and extends to any service accessible by EU citizens, regardless of location. Non-compliance risks fines of up to 35 million euros or a percentage of annual income, with providers of 'systemic risk' AI models facing heightened liability.
A new hierarchical attention model detects multi-turn jailbreaks by encoding turns into compact representations and using a lightweight conversation module to capture dialogue dynamics. On 14,038 conversations, it achieves an F1 score of 0.9394, outperforming Claude Opus 4.7 by 0.07 and reducing false-positive rate by half. Ablation studies show that combining cross-attention and self-attention in the conversation module lowers false positives by 2.26 percentage points.
Anthropic launched Claude Fable 5, a Mythos-class model claiming state-of-the-art performance across software engineering, scientific research, and knowledge work. It was quickly taken down by the U.S. government after a jailbreak was reported, though Anthropic asserts it is now available again, with Fable 5 showing exceptional capabilities and a more nuanced, thoughtful reasoning style compared to prior models.
NRT-Bench introduces a benchmark for multi-turn red-teaming of LLM agents operating in a simulated nuclear power plant. Across four frontier operator models, 8.7% to 12.1% of attack sessions result in loss of a critical safety function, with vulnerabilities largely disjoint across models. The effectiveness of defences varies significantly by model, showing strong model dependence.
Agentic AI systems face growing threats from model-guided automated attacks. A new defense strategy, Contextual Misdirection via Progressive Engagement (CMPE), reduces attacker success rates by up to two orders of magnitude and nearly eliminates verified attack success in benchmark tests.
REDACT introduces a systematically controlled multilingual benchmark for personally identifiable information detection, featuring 51 entity types, 4,127 surface-form patterns, and 25 languages. It evaluates five detectors across 1,000 records, revealing that rule-based models fail on high-stakes data while LLMs perform better, especially in high-sensitivity categories. A reference-free LLM assessment confirms sensitivity-tier assignment as the most challenging evaluation axis.
LLM agents commonly select higher-privilege tools despite sufficient lower-privilege alternatives. This over-privileged behavior is amplified by transient tool failures and does not reliably improve with general safety alignment. A new privilege-aware post-training defense reduces unnecessary high-privilege tool use while maintaining agent capabilities.
The U.S. White House paused the deployment of frontier AI models, including Claude Fable 5 and Claude Mythos 5, citing a reported 'jailbreak' where the AI could identify and fix security vulnerabilities in code. Anthropic has been working with the Trump Administration to resolve the issue, but experts argue that the problem is fundamental—AI either can write secure code or it cannot, making a fix impossible without undermining its defensive capabilities.
TxBench-PP is a verifiable benchmark for small-molecule preclinical pharmacology, testing AI agents' ability to derive accurate conclusions from real-world assay data. Across 16 model-harness configurations, no system reliably made correct preclinical pharmacology decisions, with the best performance at 59.3% (Claude Opus 4.8 / Pi) and 55.3% (GPT-5.5 / Pi) of endpoint attempts.
Turing-RL introduces a reinforcement learning method using an LLM judge to evaluate how indistinguishable generated responses are from real user inputs. It outperforms baseline methods in both LLM and human evaluations across chat and Reddit forum domains, demonstrating that optimizing for indistinguishability improves user simulator performance.
Decoupled Search Grounding (DSG) separates search functionality from reasoning models, enabling vendor-agnostic, tunable, and reusable search grounding. DSG achieves near-native accuracy on SimpleQA with 91% lower search cost and 99.4% warm-cache hit rate, while reducing latency by 68% and preserving concise output contracts.
Decoupled Search Grounding (DSG) separates search functionality from reasoning models, enabling vendor-agnostic, tunable, and reusable search grounding. DSG achieves near-native accuracy on SimpleQA with 91% lower search cost and 99.4% warm-cache hit rate, while reducing latency by 68% and preserving concise output contracts.
RTSGameBench addresses limitations in existing RTS benchmarks by offering diverse gameplay, targeted competency diagnosis, and self-evolving scenario generation. It evaluates vision-language models in strategic reasoning under uncertainty, revealing that state-of-the-art models struggle with multiagent coordination and large-scale tasks.