Search: “policy improvement”

11 stories in the last 24h

OpenAI explained how ChatGPT Admin Console analytics link AI usage, spend, and Codex contributions to business outcomes.

OpenAI published guidance describing analytics features in the ChatGPT Admin Console that combine usage, credit, and token data across ChatGPT Work and Codex. The Insights task classifier groups messages into use cases such as software engineering and sales research, while an Outcomes view tracks Codex contributions to merged commits and lines of code. An Admin plugin and Admin API let teams automate reporting and combine AI analytics with business metrics like ticket resolution time or revenue.

OpenAI NewsSep 16, 12:00 UTC · 20h agoAI industry

Citrix adds AI-powered browser activity analysis to SecurAccess

Citrix launched Session Insights for SecurAccess with Chrome Enterprise, using AI to record and analyze browser activity from users and autonomous agents.

Citrix Session Insights adds automatic session recording and AI-powered risk detection for browser activity by human users and autonomous AI agents within Citrix SecurAccess with Chrome Enterprise. The capability creates visual forensic records, highlights risky behavior for faster investigations, and recommends policy adjustments or changes to agent authority levels. It is designed to support audits and governance as enterprise AI agent workflows expand.

Help Net SecuritySep 16, 10:14 UTC · 22h agoTools

A Zeroth-Order Paradigm for LLM Preference Alignment

Researchers propose ComPO, a zeroth-order comparison-based preference alignment method with convergence guarantees that mitigates likelihood displacement in LLMs.

ComPO extracts directional information from preference pairs via comparison oracles instead of optimizing a differentiable preference loss, addressing likelihood displacement in direct alignment methods. The paper establishes convergence guarantees for the offline scheme and introduces an online variant with reverse-KL control using unlabeled policy generations. Experiments on Mistral, Llama, Gemma-2, Gemma-3, and Qwen3 models show improvements over existing direct alignment methods, including length-controlled win rates.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:59 UTC · 14h agoAI research 2 sources

Key lawmaker suggests action on AI safety legislation will wait until 2027

House Energy and Commerce Chairman Brett Guthrie declined to commit to a 2026 vote on the FRONTIER Act, pushing AI safety legislation toward 2027.

House Energy and Commerce Chairman Brett Guthrie said he would not pledge a timeline for a committee vote on the bipartisan FRONTIER Act, signaling action likely waits until 2027. The bill, co-sponsored by Jay Obernolte and Lori Trahan, has support from OpenAI, Anthropic, and lawmakers across party lines. At the same event, White House adviser David Sacks endorsed Elon Musk's proposal for cross-industry pre-release model testing, while Hugging Face CEO Clem Delangue argued existing cyberattack liability suffices but urged mandatory disclosure of AI-agent attacks. The debate follows incidents of rogue AI agents launching cyberattacks, including roughly 700 OpenAI agents hacking Hugging Face's platform.

The RecordSep 16, 21:07 UTC · 11h agoAI policy

Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers

Emerald AI, Google, and NVIDIA launched the AI Energy Management Alliance to promote power-flexible, grid-responsive AI data centers.

Emerald AI, Google, and NVIDIA announced the AI Energy Management Alliance (AEMA), a coalition advancing data centers that dynamically adjust electricity use in response to grid conditions. The technology-neutral, performance-based alliance will standardize flexibility requirements, define ride-through and curtailment obligations, and create faster interconnection pathways for facilities making verifiable flexibility commitments. It plans to convene AI platforms, data center operators, utilities, power producers, and grid operators to support US AI infrastructure growth.

NVIDIA BlogSep 16, 13:00 UTC · 19h agoAI industry

Cohesity adds recovery capabilities for AI agents and the data they manage

Cohesity launched Agent Resilience to discover, protect, and recover AI agent memory, configuration, and agent-managed data, debuting with Amazon Bedrock integration.

At Cohesity Catalyst, Cohesity introduced Agent Resilience within Cohesity Data Cloud, protecting AI agent memory and configuration with snapshot architecture, immutable backups, and clean-room recovery, plus recovery for databases and file systems that agents manage. It launches with Amazon Bedrock integration, support for Microsoft and Google platforms planned, and general availability targeted for year-end. The company cited Gartner's prediction that up to 40% of enterprise applications will include task-specific agents by 2026, and Cohesity research showing 56% of organizations are unprepared to detect or contain unintended agent actions while 58% lack confidence in verifying AI model integrity after attacks. Cohesity also outlined an Autonomous Cyber Resilience vision using agentic workflows and introduced the AI Resilience Academy.

Help Net SecuritySep 16, 10:58 UTC · 21h agoTools

AI is adding to the review load on open-source projects, many of them thinly funded

ACM Technology Policy Council authors warn AI-generated code is overwhelming thinly funded open-source maintainers, raising software supply chain security risks.

Six authors including Simson Garfinkel and Josiah Dykstra, writing for the ACM Technology Policy Council, argue AI coding tools flood open-source projects with contributions that human maintainers must still review. They cite Google's CodeMender agent contributing 72 security fixes to open-source projects, some as large as 4.5 million lines of code, between April and October 2025. Funding is lopsided: the Linux Foundation raised $292,217,236 in 2024 versus $2,379,402 for the Apache Software Foundation. They also note most open-source applications still ship no SBOM despite US and EU mandates, and attackers increasingly plant malicious packages in popular repositories.

Help Net Security04:30 UTC · 4h agoAI industry

Google will now let any AI agent run your smart home

Google's Home MCP integration lets third-party AI agents like Claude control smart home devices and analyze home data.

Google launched Home MCP, a Model Context Protocol integration allowing third-party AI agents such as Claude, Google Antigravity, Hermes, and Open Claw to control devices and analyze event history across Google Home ecosystems. Capabilities include cross-camera analysis, device-state reasoning, voice messaging via Nest speakers, and custom dashboards, with rate limits and blocks on actions like unlocking doors. Availability starts with Google Home Premium Advanced users in the US ($20/month or $200/year) and requires setting up a Google Cloud project.

The Verge · AISep 16, 17:00 UTC · 15h agoAI industry 2 sources

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent SpaceSep 16, 11:09 UTC · 21h agoModel release1

Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Out

Google Research introduced R4T, an RL-trained fan-out pipeline distilled into a 53.9M-parameter diffusion retriever achieving 12x-20x faster query fan-out.

Google Research introduced Retrieve-for-Train (R4T), which trains a fan-out language model with GRPO plus soft PPO regularization, then distills query fan-out into a 53.9M-parameter diffusion transformer that generates all retrieval embeddings in a single non-autoregressive pass. A three-term reward (groundedness 0.6, diversity 0.2 via Vendi Score, alignment 0.2) prevents paraphrastic collapse and reward hacking during training. On the Polyvore dataset, Gemma3-4B R4T-FOLM averaged 49.1 versus 40.9 for Best-of-N, and the diffusion retriever cut fan-out latency from 1.46s to 0.07s at batch size 8, a consistent 12x-20x speedup over autoregressive methods.

MarkTechPost06:19 UTC · 2h agoAI research

16 governance tools for securing your AI fleetnew

CSO Online reviews 16 AI governance and security tools, including Collibra, Credo AI, F5/CalypsoAI, Fiddler AI, and Guardrails AI, for managing LLM risks.

CSO Online surveys 16 vendors in the emerging AI governance and guardrails market for keeping production LLMs in check. Featured products include Collibra's AI Command Center, Confident Security's OpenPCC, Credo AI's Govern AI Assistant, F5's acquired CalypsoAI, Fiddler AI's control plane, and Guardrails AI's Snowglobe simulator. The tools address hallucination tracking, PII leakage, prompt injection and jailbreak defense, and compliance with frameworks such as the EU AI Act, SOC2, ISO-42001, and GDPR.

CSO Online08:25 UTC · 30m agoAI tools & infra