This week added 71 records, and 16 axes moved. The biggest increase was in ⑨ Open-Weight Lineage (27 records). That's more than the past 4-week average of 22. OpenAI (Radford et al.) and others — 12 in all — contributed for the first time this week.
71records added
16axes that moved
Oct 5–11dates
1,251total records then
Added, by axis
Axes with the most records come first. A record that spans several axes is counted once, under its main axis.
⑨27 records
Open-Weight Lineage
Oct 2, 2026Allen Institute for AI (Ai2) — AstaBrief 8B released — an open-weight model for generating scientific reports, based on Qwen3-8B, released together with its training data and checkpoints as a Hugging Face collection, plus an example workflow for building reports from local PDFs. Trained on 47K supervised examples drawn from real user queries and about 6K DPO pairs. Says its answer accuracy, citation accuracy and citation recall on SQABench-CS2 (200 computer science questions) are comparable to commercial alternatives. Fast mode averages 51.1 seconds per report, about 3.5 times faster than Thinking mode at 178.5 seconds. Integrated into the Asta platform. SourceThe point at which deep-research-style capabilities came down to an 8B open-weight model. Using citation accuracy and recall as evaluation axes matters, because in report generation the problem is not sentence quality but whether the citations are real. Releasing even the training data creates a reproducible basis for comparison.
Dec 1, 2025DeepSeek — Released DeepSeek-V3.2 and V3.2-Speciale — the official successor to V3.2-Exp, with thinking during tool use; weights for both under MIT (Speciale offered on a temporary API endpoint until December 15) SourceOpen-weight reasoning models; DeepSeek claimed V3.2-Speciale reached gold-level results at IMO, IOI and other 2025 competitions
Nov 6, 2025Moonshot AI — Released Kimi K2 Thinking — 1T total / 32B active MoE, 256K context, native INT4 inference via quantization-aware training in post-training, under a Modified MIT License SourceAn open-weight reasoning agent trained to interleave thinking with tool calls; Moonshot claims it sustains 200–300 sequential tool calls without human intervention
Aug 21, 2025DeepSeek — Released DeepSeek-V3.1 — one model switching between Think and Non-Think modes, 671B total / 37B active, 128K context, 840B tokens of long-context continued pretraining on top of V3, MIT SourceBilled by DeepSeek as its first step toward the agent era; the API's deepseek-chat and deepseek-reasoner became the non-thinking and thinking modes of one model, merging what had been separate V3 and R1 offerings
⑥17 records
Reasoning Models & the AGI Debate
Sep 29, 2026OpenAI — GPT-6.1 Sol released — the company claims it beats GPT-6 Sol's best scores by 6.4 percentage points on the DeepSWE v1.1 coding benchmark, by 4.8 points on AutomationBench and by 7 points on OSWorld 2.0 computer use, and scores more than 2 times higher on Terminal-Bench Science. Says it cut the factual error rate at low reasoning effort from 11.4% to 7.7%. Priced per 1M tokens at $2.00 input, $0.10 cached input and $10.00 output, it is positioned as offering intelligence close to Astra at 1/5 of GPT-6 Astra's standard rate. Available the same day across Plus, Pro, Business, Enterprise and Edu in ChatGPT Work and Codex, and in the API (gpt-6.1-sol). An Ultrafast version with up to 8 times faster token generation is planned for later. SourceA mid-tier update released alongside Anthropic's Sonnet 5.5 in the same week. That two labs simultaneously touted "nearly the same work at 1/5 of the frontier price" shows the competitive axis of the second half of 2026 moving from top performance to price per unit of performance.
Sep 28, 2026Anthropic — Claude Sonnet 5.5 released — the company claims 70.6% on Terminal-Bench 4.0 (Sonnet 5: 10.3%), 55.5% on CursorBench 4.0 (34.1%) and 1844 points on GDPval-AA v2.1 (1449), 2 points below Opus 5.5 on GDPval-AA. Says it is more than 30% faster than Sonnet 5 and up to 30% cheaper per task. Per 1M tokens: $2 input, $10 output, $0.20 for cache reads and $2.50 for cache writes. Available immediately on all platforms including AWS, Google Cloud and Azure (model ID claude-sonnet-5-5), with a no-data-retention option. Adds that it is the first Sonnet to beat Pokémon Red from screenshots alone. SourceThe vendor's claim itself — that the mid tier has climbed to within two points of the top tier — is this week's event. As the performance gap between tiers narrows, price becomes the deciding factor, and the reason to use frontier models shifts from task difficulty to latency and cost. But jumps such as 10.3%→70.6% on Terminal-Bench have no outside check of whether they compare like with like, so they are kept as company claims only.
Feb 17, 2026Anthropic — Claude Sonnet 4.6 released — 1M-token context window in beta, pricing unchanged at $3/$15 per 1M tokens SourceBecame the default model for Free and Pro plans in claude.ai and Claude Cowork; Anthropic reported that early-access developers strongly preferred it to its predecessor and often to Opus 4.5 from November 2025
Dec 11, 2025OpenAI — GPT-5.2 released — Instant, Thinking and Pro, pitched as a frontier model for professional knowledge work and long-running agents SourceCompany-reported for GPT-5.2 Thinking: GDPval wins or ties 70.9% (GPT-5: 38.8%), ARC-AGI-2 (Verified) 52.9% (GPT-5.1 Thinking: 17.6%), SWE-bench Verified 80.0%
①4 records
Prompt Engineering
Aug 9, 2023Anthropic — Claude Instant 1.2 released via the API — its faster, lower-priced model, now incorporating strengths of Claude 2 SourceCompany-reported scores: 58.7% on Codex (Instant 1.1: 52.8%) and 86.7% on GSM8K (80.9%); Anthropic said it hallucinates less and is more resistant to jailbreaks
Jul 11, 2023Anthropic — Claude 2 released — up to 100K input tokens per prompt, plus a public beta website, claude.ai, alongside the API (US and UK) SourceCompany-reported scores: Codex HumanEval 71.2% (Claude 1.3: 56.0%), GSM8k 88.0% (85.2%), and 76.5% on the multiple-choice section of the Bar exam (73.0%); API priced the same as Claude 1.3
May 10, 2023Google — PaLM 2 introduced — more heavily trained on multilingual text spanning over 100 languages, in four sizes: Gecko, Otter, Bison and Unicorn SourceAt Google I/O, Google announced over 25 products and features powered by PaLM 2, including Bard, and said the smallest size, Gecko, can run on mobile devices even offline
Feb 6, 2023Google — Bard announced — an experimental conversational AI service powered by LaMDA, opened first to trusted testers SourceGoogle said it would start with a lightweight version of LaMDA to reduce compute needs and gather feedback, before wider public availability in the coming weeks
③4 records
Tools & Agents
Sep 29, 2026OpenClaw Foundation, Red Hat, NVIDIA — OpenClaw Enterprise (OCE) released — an open-source, vendor-neutral platform for running always-on agents in sensitive environments. It provides multi-tenancy, hardened security boundaries, standardized agent primitives, and governance and auditability across the agent lifecycle, and is designed so that core components such as the harness, model and sandbox can be swapped for third-party or in-house ones. It began at OpenAI and was donated to the OpenClaw Foundation; it is co-developed with Red Hat and NVIDIA and being piloted at Red Hat and OpenAI. Says it runs on one's own infrastructure and is always free for any organization (no specific license name given). It can be self-hosted from the GitHub repository with docker-compose or Kubernetes, and 1.0 is planned for 2026. SourceA path has opened for agent runtimes to move from vendor products to foundation-owned standard infrastructure. The key is defining the harness, model and sandbox as swappable slots: if this separation takes hold, the cost of switching models falls and companies are less tied to any one lab. It meshes directly with demands from manufacturing and finance to keep always-on agents in isolated in-house environments.
Sep 29, 2026OpenAI — dots released (DevDay 2026) — an always-on agent built on GPT-6 Astra. It runs on its own cloud computer and connects to more than 4,000 apps through the OpenAI plugin ecosystem; users give it instructions and feedback via ChatGPT, Slack and Teams. The company says it works on its own from instructions and feedback and learns preferences over time. The Pro and Business Premium plans include a first dot at no extra cost, with expanded limits in the first month after launch. Enterprise can use it in beta once a workspace admin turns it on, and specialist dots that take on organization-level responsibilities were presented as well. SourceThe first major commercial product to sell an agent not as "a session that runs when asked" but as "a persona resident in the account". The billing unit shifts from tokens and seats to the number of agents, and always-on operation forces permission, audit and isolation design on the product side — touching, at a different layer, the same problem as the same week's OpenClaw Enterprise and Foundry egress controls.
Sep 28, 2026SpaceXAI (xAI) — Team Bots released — shared AI teammates built on Grok. A single bot bundles four things — context (files such as brand guides and documents, plus instructions and skills), plugins (Salesforce, Notion, GitHub, etc.), credentials (third-party API access) and memory (retaining what it learns so it improves over time) — and is shared by the whole team, while individual conversations stay private. It also collaborates in Slack through a dedicated bot handle, and comes with prebuilt templates for sales, product management, marketing and data analysis. Available the same day in public beta on Teams and Enterprise plans. The company is named "SpaceXAI LLC" in the announcement. SourceA design that treats agents not as personal assistants but as shared team assets. Bundling context, credentials and memory per bot means organizational knowledge builds up in the bot, but the bot in turn becomes a concentration point of permissions — the same direction as OpenAI's dots the same week, with greater security exposure because the credentials are shared.
Sep 24, 2026Microsoft — Routines in Microsoft Foundry Agent Service reach general availability (GA) — a managed way to run agents on a schedule, at a specified time, or in response to events in external systems. Without separately building schedulers, event listeners, webhooks or infrastructure, it gathers triggers, agent actions, identity, connections and run history inside the Foundry project. There are three kinds of trigger: timer (once, at a single future point), recurring (on a set cycle) and event-based (when something happens in a connected system). For identity, users choose between the creator's delegated permissions and the agent's own Entra ID. With a reminder tool, in preview, the agent can schedule its own re-run after a set time, monitoring multi-step tasks without an external polling service. The initial integrations are GitHub issue events and Teams channel messages. SourceThe operational foundation for agents moving from "running when a person calls them" to "waking up on their own" has become a GA cloud product. It matters that the option of giving agents their own identity came out at the same time: when the subject in audit logs is an agent rather than a person, the unit of accountability changes.
⑩3 records
Safety, Alignment & Interpretability
Sep 30, 2026OpenAI — Disclosed that it disrupted a coordinated model distillation campaign — activity from Jul 1 to Jul 28, 2026. Explains that it did not break into encryption or databases but was adversarial distillation that manipulated model interactions to reproduce protected reasoning in a form visible to the requester. It began at low frequency on July 1, and on July 24–25 more than 4,000 users made 16,000 requests with related extraction patterns; related activity spanned more than 15,000 users. Says it attributes the core cluster of activity to individuals linked to Moonshot AI, the developer of Kimi, but notes that attribution of the full set of operators is uncertain. Fully shut down on July 28 through account bans, restrictions on fraudulent accounts, tighter sign-up controls, expanded monitoring, blocking of reasoning-replay paths, added output-detection checks and coordination with third-party service providers. SourceThe first publicly disclosed large-scale extraction attempt since hiding reasoning traces became a trade-secret protection measure. Because the attribution points to a specific Chinese lab, it reads as a geopolitical event rather than a technical one, and the policy of keeping reasoning private gains a defensive rationale alongside the safety one. The attribution is OpenAI's claim, with no third-party checking.
Sep 29, 2026Anthropic — Published "GLM-5.3 and the spread of advanced cyber capabilities" — reports that GLM-5.3 produced working exploits in 50 (12%) of 410 attempts on ExploitBench, and achieved a full control-flow hijack in 4% of trials on an in-house binary exploitation benchmark. Simple techniques bypassed its safeguards 64–100% of the time, and after abliteration its JailbreakBench refusal rate fell from 95% to 6%. Concludes that GLM-5.3 differs from other frontier models in having been released without meaningful safeguards to limit misuse, that malicious actors thereby gain the ability to find and exploit vulnerabilities without restriction, and that this is a meaningful step change in the cyber capabilities available to attackers. SourceThe first case of a competing lab putting numbers on what leaks out when open weights are released without safety measures. The debate in the open-weight lineage (axis ⑨) shifts from a "performance gap" to a "safeguard gap", and the issue becomes not the release of weights itself but what was not released along with them. The interests at play when a competitor evaluates a competitor should be taken into account.
Sep 28, 2026OpenAI — Published "Towards safety cases for frontier AI training" — proposes producing a structured, evidence-based risk document (a safety case) before continuing frontier reinforcement-learning training. It writes that a safety case must cover all three aspects of the technical stack — alignment training, containment and monitoring — and that a senior accountable person responsible for the training run should be accountable for the safety case and incident response. It lists as components alignment through environment grading, infrastructure containment and sandbox hardening, real-time monitoring including automated responses, pre-mortems, an executive veto and audits, and publication of investigation findings. It describes these as best practices currently applied at OpenAI but, by its own account, "aspirational". SourceA record of the focus of safety discussion moving from deployment time to training time. If labs accept in public documents the premise that models can escape containment during training, a procedural standard emerges that regulation can refer to — and, taken together with Anthropic's GLM-5.3 analysis the same week, it also becomes a baseline against which to compare "releases without safeguards".
⑪3 records
Model Architecture & Training Methods
Apr 4, 2022Google Research — PaLM (Pathways Language Model) announced — a 540B dense decoder-only Transformer trained on 6,144 TPU v4 chips with Pathways SourceThe first large-scale use of the Pathways system; reported surpassing the few-shot performance of prior large models such as GPT-3, Gopher and Chinchilla on 28 of 29 English NLP tasks
Oct 11, 2018Google (Devlin et al.) — BERT paper posted on arXiv — pre-training a deep bidirectional Transformer encoder that conditions on both left and right context SourceFine-tuned with just one additional output layer, it set new state-of-the-art results on 11 NLP tasks, including GLUE 80.4% and SQuAD v1.1 F1 93.2 (as reported by the authors)
Jun 11, 2018OpenAI (Radford et al.) — The GPT (commonly called GPT-1) paper Improving Language Understanding by Generative Pre-Training — a 12-layer decoder-only Transformer pretrained on BooksCorpus, then fine-tuned for each task SourceReported that combining unsupervised generative pre-training with supervised fine-tuning improved the state of the art on 9 of the 12 tasks studied
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