Search “RAG”

New study says AI model token consumption varies by up to 17 times depending on language

Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption not only drives up costs but also slows response times and compresses the available context. The researchers recommend that AI labs publish evaluation results for each language and improve tokenizer design in the next generation.

MoleculeMind MMDesign Technical Report: 18 of 19 Projects Successful, All 6 GPCR Targets Hit

MoleculeMind has released a technical report on its AI protein de novo design platform MMDesign, covering 19 projects across 18 targets and two modalities—nanobodies and macrocyclic peptides—with 18 yielding experimentally confirmed binders, a project success rate of 94.7%; of 453 tested candidates, 139 were hits, a candidate hit rate of 30.7%. All six GPCR targets succeeded, with best affinities reaching the nanomolar level: GPR68 achieved a best KD as low as 12.1 nM, and all 30 candidates for NK3R were confirmed to bind. The report also shows that when redesigning against 6 targets that already had binders, 5 achieved improvements, and it presents results for the structure prediction model MMFold on the FoldBench antibody–antigen interface benchmark.

Harvard study: AI review comments comprehensively outperform high-level human peer reviewers across seven dimensions

Two researchers from Harvard University published a paper on October 22 comparing 134 pairs of AI and human review comments, with AI leading comprehensively across seven evaluation dimensions. The study used Claude Opus 4.8 to generate review comments and GPT-5.5 as the blind-review judge; in all 134 comparisons GPT-5.5 chose the AI. In 30 blind reviews by three former academic journal editors-in-chief, 26 considered the AI better. The factual error rate of AI review comments was 5.5%, versus 15.9% for humans, and all 651 references cited by the AI with DOI or arXiv numbers actually exist.

UK AISI evaluates frontier AI agents' performance in multi-step cyberattack scenarios

The UK AI Security Institute evaluated seven models released between August 2024 and February 2026 on a self-built cyber range, finding that each generation of models outperformed the previous one under a fixed token budget: in a 32-step enterprise cyberattack range, the average number of completed steps under a 10M token budget rose from 1.7 steps for GPT-4o to 9.8 steps for Opus 4.6. Raising inference compute from 10M to 100M tokens yielded gains of up to 59% with no plateau observed, and at current prices a single 100M token attempt with Opus 4.6 costs about 80 USD. In a 7-step industrial control system range, model progress was limited: under 10M tokens most models completed zero steps, and under 100M tokens Opus 4.6 averaged 1.4 steps.

Microsoft AI releases the MAI-DxO diagnostic system and the SDBench benchmark, with results published in Science

Microsoft AI has released the AI diagnostic system MAI-DxO and the step-by-step diagnostic benchmark SDBench, with the related results published in Science. On 304 complex NEJM cases, MAI-DxO achieved a diagnostic accuracy of 83%, compared with 36% for doctors with external reference materials and 22% without reference materials; on common emergency cases in MIMIC-IV-ED, MAI-DxO scored 80%, compared with 47% for doctors. The system selects examination items step by step through multi-round debates among multiple virtual doctors, and still achieves 81% accuracy under a cost-optimized configuration at less than half the cost of comparison systems, and it has open-sourced the benchmark and scoring code for the SDBench subset of NEJM cases.

Sierra launches 𝜏-i18n multilingual voice agent evaluation benchmark

Sierra releases 𝜏-i18n, extending 𝜏-Voice to five languages: Spanish, Brazilian Portuguese, Hindi, Korean, and Mandarin, retaining the same airline, retail, and telecom tasks, policies, tools, and success criteria to enable cross-language comparison. Across 2,700 main calls, task completion rates for Spanish, Hindi, and Portuguese lag English by an average of 2.4, 3.8, and 6.4 percentage points, while Korean and Mandarin lag by 22.6 and 9.1 percentage points respectively; Korean is the weakest language among the three agents. In addition to task completion, 𝜏-i18n separately reports interaction metrics (response timing, selectivity, tool-call efficiency) and generation quality (naturalness, speech fidelity), and has open-sourced the codebase and the language pack framework LanguageFactory.

Microsoft Research proves the 31-year-old unsolved KLS conjecture

Microsoft Research researcher Xinzhi Zhang and co-authors proved the KLS conjecture proposed in 1995, giving a dimension-independent fixed bound; the previous best result only allowed the bound to grow slowly with dimension. The proof came from a human-guided agent search process, in which the team explored more than 100 candidate paths, with researchers evaluating, pruning, and redirecting them; an independent team also gave a complete proof at the same time, followed by Lean 4 formal verification. The authors say earlier unguided end-to-end attempts with multiple frontier models failed to achieve the goal.

Google Research uses AI to assist blind-sweep ultrasound assessment of gestational age, with error comparable to experts

Google Research published a study in JAMA Network Open; its AI gestational age assessment model based on blind-sweep ultrasound achieved a mean absolute error of 4.18 days in prospective testing in Chicago (n=192) and Nairobi (n=193), reaching non-inferiority compared with expert sonographers' 4.48 days. The model was fine-tuned only on data from 120 participants in Chicago and did not use Nairobi data; the errors in the two locations were 4.10 days and 4.27 days, respectively. The study used a Clarius C3 handheld probe, with standardized blind sweeps performed by operators without professional ultrasound training.

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