Governance, safety and regulation
The constraints forming around AI: financial-services regulation, model risk, and the code paths nobody is watching.
Research sweeps
2026-08-03 · deep
Security Research into Chinese Open-Weight Models
Independent security research into Chinese open-weight models (DeepSeek R1 and V3, Alibaba Qwen, Moonshot Kimi K2, Zhipu GLM, MiniMax, Baidu Ernie) from February 2025 to August 2026: who is testing them, what red-teaming and provenance methods they use, which results survive independent replication, and how regulated, defence and military buyers are assuring models whose training data and training objectives are never disclosed
GPT-5.6-sol- financial
- frontier
- academic
- +2
2026-07-15 · deep
Enterprise AI Transformation Programmes (2025–2026)
Enterprise AI transformation programmes from July 2025 to July 2026: reported success and failure rates and who measures them, delivery frameworks borrowed from and diverging from classic digital transformation, token cost economics and budgeting under consumption pricing, adoption strategy including leadership over-provisioning of access, and AI governance across AI-Ops, financial business cases, security and regulatory controls as they scale with sector risk tolerance, referencing MIT, McKinsey State of AI, DORA, ThoughtWorks Technology Radar, NIST AI RMF, ISO 42001, and the EU AI Act.
Claude Fable 5- financial
- tech
- academic
- +3
2026-06-07 · deep
AI on Deterministic Rails
AI on deterministic rails: how AI and traditional deterministic software are forming a symbiotic stack from January 2025 through June 2026: the enterprise "PoC-opalypse" and the shift from token consumption to durable agentic adoption patterns, AI leveraging software-encoded workflows as guardrails (variance and error control) rather than replacing them, the frontier moving from raw model capability to model orchestration and harness design (Claude Code, OpenCode, Pi), right-sizing with smaller and open-weight models (Llama, Qwen, DeepSeek, Mistral) for cheap routine automation and private inference, and the token-pricing economics behind enterprise sticker-shock over agentic spend versus delivered value
Claude Opus 4.8- financial
- frontier
- academic
- +3
2026-05-19 · deep
AI Regulation and the Regulated Enterprise - Trajectory to 2030
The trajectory of AI regulation across the EU AI Act, the UK's pro-innovation and contextual approach, and the financial-services regulatory regime (FCA, PRA, Bank of England) from January 2023 to May 2026, including the FCA Mills Review, GPAI obligations, model-risk and accountability rules, and what they demand of technology leadership in regulated firms
Gemini 2.5 Pro- frontier
- academic
- vc
2026-04-13 · deep
AI Dark Code - Organisational Accountability and Control
AI-generated and agent-produced code ("dark code") in enterprise settings June 2025–April 2026: organisational accountability structures, failure and adaptation of established management frameworks, technical and governance controls, observability and discoverability of agent logic, and documented outcomes from early enterprise adoption.
Claude Opus 4.8- financial
- frontier
- academic
- +2
Explainers
- Research Explainer · West (2026)
Free models agree with the expensive verifier when it says yes, but miss most of its real failures
Eight free candidates, five local open-weight models and three cloud free-tier routes, were retro-graded against frontier verdicts on ten real stages of agent-written code. The best caught 77% of genuine failures. Five caught almost none, while agreeing with passing work up to 98% of the time.
- Research Explainer · McCain (2026)
AI agents are running for longer, but good oversight is becoming more active
Anthropic's study of Claude Code sessions and public API tool calls finds growing practical autonomy, paired with a shift from approving every action towards monitoring, interruption and agent-initiated clarification.
- Research Explainer · Acharya (2026)
Governance maturity makes agent fleets safer, but the evidence is still simulated
A 750-run multi-agent simulation finds that higher governance maturity sharply reduces agent sprawl and risk incidents while improving task completion and composite Net Business Value. Its pivotal claim is that Level 3 is the minimum viable standard, not a decorative middle rung.
- Research Explainer · Cambridge CCAF (2026)
Finance has gone all-in on AI, but the supervisors watching it have not
A 628-organisation, 151-jurisdiction survey finds 81% of financial firms now using AI, while regulators trail on adoption, data collection and the supervisory tools needed to keep up.
- Research Explainer · Fokou (2026)
Prompt guardrails can't protect AI agents that act on the world, so Parallax builds a wall between thinking and doing
A new security paradigm structurally prevents AI reasoning systems from executing actions, interposing an independent four-tier validator that blocks 98.9% of adversarial attacks with zero false positives, even when the agent is fully compromised.
- Research Explainer · Okpala (2025)
AI agent crews can build and validate financial models, but they still need human oversight to stay safe
Researchers at Discover Financial Services built two collaborating multi-agent crews, one for modeling and one for model risk management, that autonomously handle the full ML pipeline on credit risk, fraud detection, and card approval datasets, matching or beating top Kaggle solutions while stress-testing their own outputs.
- Research Explainer · Gabison & Xian (2025)
LLM agents act on your behalf, but the law still holds you responsible when they fail
A principal-agent analysis of liability in LLM-based agentic systems reveals that delegation to AI agents creates legal exposure for users, providers, and platforms, with multiagent systems amplifying the problem far beyond what single-agent frameworks can handle.
- Research Explainer · International AI Safety Report authors (2026)
Frontier AI is improving at speed, but the evidence on real-world risk still lags behind the hype
This report is not a single experiment but a large expert synthesis of what researchers knew before December 2025 about frontier general-purpose AI. Its core message is plain enough: capabilities are climbing fast, misuse is already visible, and the tests people rely on still flatter the systems more than real life does.
- Research Explainer · Xia, Lu, Zhu et al. (2025)
Most LLM agent evaluation stops at launch; so the same failures keep recurring in production
A multivocal review of 161 sources reveals that academic evaluation overwhelmingly focuses on pre-deployment benchmarks. The authors propose EDDOps, a process model and reference architecture that make evaluation a continuous, governing function across the entire agent lifecycle.