The paper introduces Thomson, a frontier AI model developed through continual learning on open-weight models, aiming to democratize access to high-performance AI. It argues that institutions with limited resources can achieve frontier-level performance by applying a modern mid- & post-training stack, preserving model plasticity and stability while minimizing high-impact interventions. Thomson demonstrates competitive performance across agentic tasks, safety, legal, tax, multilingualism, and large-scale deep research, exhibiting a distinctive π-shaped improvement pattern and effectively mitigating the forgetting problem seen in narrow domain adaptation.
By Shengzhuang Chen, Jerrod Parker, Yejin Bang, Andrew M. Bean, Nabeel Seedat, Stefan Winzeck, Daniil Glazko, Jannik Zgraggen, Fangyi Yu, Scott Arnott, Dietrich Trautmann, Luca Ciuffreda, Guglielmo Bonifazi, Davide Romano, Bradley Bell, Kirsty Fielding, Daniele Giofr\`e, Tom Zielund, Ipshita Chatterjee, Sneha Murthy Ghantasala, Manpreet Nanreh, John Scoville, Maciej Sakowicz, Wassim Seifeddine, Lukas Thede, Jonathan Richard Schwarz
The paper "Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance" presents a taxonomy of twenty inference‑time mechanisms for monitoring, verification, and enforcement, each evaluated on a four‑point readiness scale using evidence from four vendors. It applies this taxonomy to a two‑dimensional adversary model and maps the mechanisms to four governance scenarios, finding that most mechanisms are commercially available but only adequate against cooperative or low‑to‑medium‑capability users, not high‑capability state‑level deployers. The study also links inference‑stage controls to hardware‑stage mechanisms through a substitution principle and reports a second‑rater reliability of 0.74.
whyItMatters":"The work identifies the current gaps and readiness of inference‑time governance tools, highlighting that existing mechanisms are insufficient against powerful adversaries and thus informing future regulatory and technical development."
By Samar Ansari
Compute governance today is a governance of training: the thresholds, reporting requirements, and frontier-AI regimes now in force attach to training compute and treat the trained model as the regulat...
arXiv:2607. 00913v1 Announce Type: new Abstract: As exponential compute scaling continues, will the capabilities of frontier AI models outstrip what is accessible to developers on a small fixed budget?
By Alex Fogelson, Zachary A. Brown, Hans Gundlach, Jayson Lynch, Neil Thompson
arXiv:2608. 13272v1 Announce Type: new Abstract: A small number of firms based in two states produce the most capable frontier AI models.
By Alan Woodward, Andrew Rogoyski
The paper "Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement" highlights a gap in enterprise AI governance, where 78% of organizations lack auditable evidence of policy enforcement. It introduces AGIL, a five-layer adaptive governance architecture that uses machine learning for real-time detection, risk classification, sub-100ms policy enforcement, continuous attestation, and policy evolution. The authors argue that the failure is organizational and architectural, not technical, and call for future empirical validation of AGIL.
By Sandeep Bokkasam, B. Durgalakshmi