Persistent memory in AI agents creates a new attack surface where prompt injections survive across sessions and re-enter context on every retrieval. Security researcher Johann Rehberger demonstrated SPAIware writing malicious instructions into ChatGPT's long-term memory, and the MemoryGraft paper shows poisoned experience records causing durable behavioral drift in autonomous agents like MetaGPT. Three key defenses apply: scope memory per instance, validate writes for injection patterns, and maintain per-entry provenance to enable surgical removal of poisoned entries.

Two recent Federal Circuit decisions—Teva v. Eli Lilly and In re Xencor—clarify what patent specifications must disclose for method-of-use claims involving known compound genera under 35 U.S.C. § 112. Teva upheld claims where the genus was well-known and all members worked for the specific therapeutic use, while Xencor rejected broad, undifferentiated treatment language lacking evidentiary support. The article distills five practical drafting and prosecution lessons emphasizing claim specificity, documented known-in-the-art status, mechanistic rationale, careful claim format selection, and evidentiary record preservation.

A former Google DeepMind employee proposes a governance framework for AI companies contracting with government entities, establishing two red lines: human control over targeting and use of force, and no untargeted AI profiling. The framework includes a seven-person Defense AI Review Body that assesses contract compliance with yearly transparency reports to prevent quiet dismantling. The author invites discussion on improving the framework as corporate governance and its potential to inform future legislation.

PlanFlip introduces four planning-phase prompt injection attacks against multi-agent LLM systems that corrupt downstream sub-tasks via the Planner agent. Testing nine frontier LLMs across 3,479 episodes reveals that stronger models like GPT-5 are more vulnerable (ASR=0.68), homogeneous pipelines have a correlated-agent blind spot, and reasoning-augmented models like DeepSeek-R1 resist injections. The authors propose two defenses—GoalAnchorCheck and CrossAgentConsensus—achieving detection rates up to 1.00, concluding that heterogeneous model diversity is a security prerequisite.

A VentureBeat survey of 157 enterprises reveals a critical agent evaluation gap: 50% have shipped AI agents that passed internal evaluations but then failed in production, and only 5% fully trust automated evaluation today. Despite this, 66% already allow or are engineering toward zero-human-in-the-loop deployment for low-risk agents. The core problem is not evaluation coverage but reality alignment — evaluations pass agents that fail real customers, and autonomy is scaling faster than assurance.

"Weights to Words" is a method that automatically discovers domain-relevant preference dimensions described in natural language and paired with model vectors, enabling users to inspect and edit preference model inferences. Validated across moral dilemmas, movies, wines, and LLM responses with two pre-registered experiments (N=450, N=449), it shows that regularizing toward learned dimensions and incorporating user edits both improve prediction accuracy. Participants preferred its inferred profiles and endorsed its predictions as more accurate in head-to-head comparisons.

ASCIITermDraw-Bench is a new benchmark evaluating Vision Language Models on their ability to generate and edit ASCII-based diagrams across 80 tasks in four categories: basic layouts, network topologies, software architecture, and image-conditioned editing. Each response is scored structurally and semantically via an LLM judge with 95% confidence intervals. The current leaderboard is led by Gemma-4-31B-IT at 73.8%, with the benchmark and methodology publicly available on Hugging Face.

OpenLanguageModel (OLM) is an MIT-licensed PyTorch library for building and pretraining small language models with transparent, readable code. It supports tokenizers, streaming datasets, mixed precision, and CPU through multi-GPU execution, with 27 presets across nine model families. Validation shows 90.6% four-GPU weak-scaling efficiency for a 348M-parameter workload and close agreement with reference implementations.

Relativity's newly appointed president Chris Brown discusses the company's acquisition of Gavel and its strategic decision to integrate Claude into its product ecosystem. He highlights the rapid growth of aiR, Relativity's AI-powered review product, as a key revenue and adoption driver. The interview covers product strategy, marketing realignment, and how generative AI is reshaping the legal e-discovery market.

A hands-on tutorial demonstrating how to fine-tune the OpenVLA robot AI model using LoRA on Google Colab. Covers dataset preparation, Colab environment setup, training metrics, and Weights & Biases logging for reproducibility. Aimed at practitioners who want a reproducible 100-step fine-tuning workflow.

Meta has open-sourced Astryx, the React and StyleX design system used internally across 13,000+ apps for eight years. It includes 150+ accessible components, seven themes, dark mode, templates, and an agent-ready CLI under the MIT license. React 19+ is required.

Google launched Gemini 3.5 Flash Cyber, a cost-efficient AI security model for finding and patching vulnerabilities, positioned as a cheaper alternative to larger models like Anthropic's Mythos. The model is available first to governments and trusted partners via CodeMender, Google's security-focused coding agent. It enables high-speed, low-cost scanning of more code paths by AI agents.

Over the past nine months, Amazon, Microsoft, and Google have each launched or rebranded enterprise agent platforms that are converging on a shared architectural pattern. This trend signals a maturing market where multi-agent orchestration, tool integration, and enterprise guardrails become standard. Leaders evaluating agent platforms should watch this convergence as a sign of emerging industry standards.

Vercel's AI Gateway now supports Gemini 3.6 Flash and Gemini 3.5 Flash-Lite. Gemini 3.6 Flash improves coding, agentic tasks, and web development output while consuming fewer tokens. Gemini 3.5 Flash-Lite upgrades agentic capabilities for scoped subagent tasks. Both are accessible via the AI SDK with no platform markup on inference.

MIT Technology Review highlights how advanced materials science is the foundational layer enabling next-generation AI progress. While most AI discourse focuses on algorithms, compute, and fab investments, materials innovation drives improvements in processing power, memory, and energy efficiency. The article argues this underappreciated layer is critical to sustaining AI's trajectory.

Tradeshift replaced its legacy BI tool with Amazon Quick's agentic AI capabilities, achieving query response times up to 30x faster and a 40% reduction in total cost of ownership. The shift also turned embedded analytics into a revenue-generating product. The post provides a concrete case study of migrating from traditional BI to agentic AI.

Hut 8 signed a second 15-year lease worth $9.8 billion, fully commercializing its 1-gigawatt Beacon Point AI data centre campus in Texas. The deal doubles the unnamed tenant's contracted footprint, bringing Hut 8's total contracted AI data centre capacity to 949 MW. The first Phase 2 data hall is expected to deliver in 2028.

Alex Lieberman, founder of Morning Brew, shares a Claude-powered content workflow that interviews him before drafting, encodes his voice in Markdown, and runs a six-persona revision loop before publishing. The system is designed to produce high-quality content at scale without sounding generic or AI-generated. This is a practical playbook for creators and marketers building repeatable AI content pipelines.

Agentic AI deployments require robust integration infrastructure to enable agents to take action, not just answer questions. Many enterprises are repeating past mistakes by focusing on data lakes rather than integration architecture. Organizations must prioritize integration to move AI agents from experimentation to real production value.

IBM announced preliminary results that rattled the software market, but the real story is IBM-specific. Ben Thompson examines IBM's mainframe franchise as its core moat and the company's many AI-related problems. The piece frames IBM's struggles as a convergence of legacy dependency and AI strategy failures.