Presenting my grand unified theory of ML researcher impact: Your impact is directly proportional to how much pain you cause to infra. Fundamentally, you can only inflict pain upon infra if your approach actually works. And the better your approach works the more pain infra is forced to endure. So, to give some examples: - MoE's add a ton of data-dependent computation => pain (shazeer++) - GDN/KDA are the most complex architecture I've been forced to care about and a very annoying matrix inversion => pain (sonta++) - Muon is much more annoying than Adam and causes annoying restrictions on parallelism => pain (keller/jeremy++) - RL scaling forced many researchers to care about LLM inference and RL infra as a category => pain (tworek++) Even papers like Attention Is All You Need have lead to significant pain! Before transformers were invented everyone was running small jobs and I never needed to think about kv-caches or 6D parallelism.

Announcing Artificial Analysis Intelligence Index v4.2. We are accelerating elements of our upcoming v5 release with interim updates to keep pace with the frontier. Index v4.2 has more complex and realistic tasks, and more private test sets to prevent gaming Intelligence Index v4.2 changelog: + AA-Briefcase, our agentic knowledge work evaluation with a private test set + @HelloSurgeAI's GDP.pdf, long context document reasoning across 4,592 PDF pages - GPQA Diamond, an exceptional scientific reasoning evaluation that has now been saturated … plus greater weighting on held-out test sets to prevent gaming, and grading infrastructure upgrades to increase robustness This update brings the Index closer to real-world use cases with more challenging, complex and realistic tasks and private test sets to prevent gaming. We have been planning and building elements of Index v5 for months - it’s been 8 months since we launched Index v4 in January. We have deliberately held back updates to keep the Index stable through recent major model launches. However, with the frontier moving so quickly in the past weeks, we feel it is important to deliver an immediate interim update to ensure our Index remains as relevant and useful as ever to users. Beyond this interim update, our team is hard at work on v5 of the Index. We are planning more incremental releases in the near future. Stay tuned! Intelligence Index v4.2 changes in detail: ➤ Adding AA-Briefcase: Our in-house evaluation with a private held-out test set, AA-Briefcase tests models on realistic agentic knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files. AA-Briefcase combines rubric and pairwise grading to evaluate verifiable task success, analytical quality, and presentation quality, giving a holistic view of overall agentic capability in knowledge work. ➤ Adding GDP.pdf: Created by @HelloSurgeAI, GDP.pdf evaluates single-turn professional document reasoning across 100 PDFs and ten domains. Models must synthesize evidence distributed across 4,592 pages, including text, tables, charts, footnotes, and exclusions. Responses are graded against 1,275 expert-authored atomic criteria; the headline All-pass Rate credits a task only when every criterion is satisfied. ➤ Weighting to measure real-world use and prevent gaming: 40% of our Index weighting is now private, held-out test sets - double the figure from v4.1. Held-out data includes AA-Briefcase, AA-Omniscience, and solutions for CritPt. This reduces the ability for labs to game evaluations. The held-out percentage will increase further in Index v5. ➤ Improving our grading infrastructure: In AA-LCR v1.1, we have added a grading system prompt and corrected errors and ambiguities in answer keys, improving scoring accuracy. For GDPval-AA v2 and AA-Briefcase, we have improved our sampling and re-anchored the Elo scale, making ratings more stable as new models are added. For SciCode we have improved robustness of grading sandboxes to ensure slow but correct code does not count as a failure. Key results: ➤ Anthropic and OpenAI lead the Index: Anthropic’s Claude Fable 5.1 leads the Index, followed by OpenAI’s GPT-6 Astra, which shows a 4pt gain over GPT-5.6 Sol. Meta is the third-ranked lab on the leaderboard, followed by SpaceXAI, Moonshot/Kimi, Z AI, and Google ➤ Cost per Task Pareto frontier shared by four labs: Anthropic, OpenAI, Meta and Z AI occupy the updated Cost per Task Pareto frontier ➤ GPT-6 Astra dominates the output token Pareto frontier: GPT-6 Astra is more token efficient than almost every other model near the intelligence frontier, with Claude Fable 5.1, Grok 4.5 and Gemini 3.5 Flash-Lite at either end of the curve (excludes models below 25 on the Index)

If you calculate out comms the dispatch takes time input_dim*comm_time_per_byte and the matmul takes time input_dim*intermediate_dim*time_per_flop. If you divide these, you find that the ratio that determines whether you can overlap compute with comms is comm_time_per_byte/(intermediate_dim*time_per_flop). Since latent_moe shrinks input_dim by 2 but keeps intermediate_dim the same, there's no impact on whether you're network bound.

Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

every research team needs to spend some time learning how their modeling code lowers down to gpu kernels example: rms norm vs layernorm industry assumes rms norm is cheaper. you don't need the x̂, it looks simpler, so it must be faster, so it got adopted but that's not true. both rms and layernorm are memory bound kernels (the amount of time it takes to get the data to gpu cores is longer than the amount of time it takes to do the computation on said cores) both take the same amount of time e2e so can probably train the model right with either, but maybe it makes a difference (e.g why diffusion models still use norms with an affine shift e.g adaLN) i get why. as open-source models get better and, inevitably, commoditized across the inference providers, overall serving speed determines user experience determines which model gets adopted but there's no reason to superstitiously avoid free things