Dec 8, 2025

ML Times

• Google Titans architecture, helping AI have long-term memory

  • The Titans architecture and MIRAS framework enable AI models to efficiently manage long-term memory and adapt in real-time, enhancing their ability to process extensive contexts without offline retraining.

• Nested Learning: A new ML paradigm for continual learning

  • Nested Learning introduces a paradigm that treats machine learning models as interconnected, multi-level optimization problems, effectively addressing catastrophic forgetting by allowing simultaneous optimization of various learning tasks.

• Over fifty new hallucinations in ICLR 2026 submissions

  • GPTZero identified over 50 hallucinations in ICLR 2026 submissions, revealing that these errors were overlooked by 3-5 peer reviewers, raising concerns about the integrity of the peer review process.

• GitHub Actions Has a Package Manager, and It Might Be the Worst

  • GitHub Actions lacks essential package management features such as lockfiles, integrity hashes, and dependency visibility, which are critical for ensuring software supply chain security, leaving users vulnerable to unverified third-party code.

• IBM to Acquire Confluent

  • IBM has agreed to acquire Confluent for $31.00 per share, aiming to create a unified platform that enhances data accessibility for enterprises, thereby accelerating AI integration and operational modernization.

• The "confident idiot" problem: Why AI needs hard rules, not vibe checks

  • The "Confident Idiot" problem highlights the danger of relying on AI models that exhibit high confidence despite inaccuracies, necessitating a shift towards deterministic programming practices to ensure reliability.

• Deep dive on Nvidia circular funding

  • NVIDIA's Q3 Fiscal 2026 earnings reveal a $31.9 billion net income, yet a concerning $8 billion cash flow gap suggests potential liquidity issues, raising questions about the sustainability of their "virtuous cycle" in AI.

• Jepsen: NATS 2.12.1

  • NATS JetStream 2.12.1 exhibits significant vulnerabilities, including data loss due to file corruption and power failures, with tests revealing that acknowledged messages can be lost even when a majority of nodes remain operational.

• Alignment Is Capability

  • Alignment is not merely a constraint; it is the essence of capability in AI, as demonstrated by Anthropic's integration of alignment researchers into every phase of model training, leading to superior performance in real-world tasks.

• Vibe Coding: Empowering and Imprisoning

  • Vibe coding, which utilizes LLM tools for coding assistance, has rapidly gained traction among developers, enabling the creation of applications that might otherwise be unattainable, yet it poses significant risks to coder autonomy and job security.

• We collected 10k hours of neuro-language data in our basement

  • Conduit collected ~10,000 hours of neuro-language data, the largest dataset of its kind, to train models that decode semantic content from noninvasive neural data, enabling zero-shot predictions from participants' thoughts before they articulate them.

• Has the cost of building software just dropped 90%?

  • The cost of building software may have dropped by 90% due to advancements in agentic coding, which significantly reduces labor and coordination overhead, allowing projects that once took a month to be completed in a week.

• Client-side GPU load balancing with Redis and Lua

  • By implementing a client-side load balancer using Redis and Lua, we achieved a ~40% increase in average GPU utilization and reduced tail latency by 70%, addressing inefficiencies in GPU resource allocation during AI inference tasks.

• The state of enterprise AI

• A contract-driven agent runtime: separating workflows, state, and LLM contract generation

  • The proposed architecture treats the LLM as a compiler that generates a typed contract, enabling a deterministic interpreter to execute agent behavior, thus enhancing reproducibility and replayability in ML systems.