Benchmarks

The Verge describes Peter Degen investigating unusual citations to a 2017 paper: it rose from a few dozen citations over several years to being cited every few days, while the RSS snippet does not disclose the full sample size or review findings.

A Reddit user tested Qwen3.6-35B-A3B MTP across three million-token-scale sessions, using 300k context and KV Q8_0, and reported about 1.5x the tok/sec of earlier tests.

Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large U.S. health systems, 28+ languages, and 50+ specialties, while its clinical documentation workflow reduces clinicians’ documentation burden by 10–20 hours per week.

IBM Granite released Granite Embedding Multilingual R2 on Hugging Face under Apache 2.0, with fewer than 100 million parameters, a 32K-token context length, and top same-scale retrieval performance on MTEB according to the post.

MiMo V2.5 Pro placed third on the DesignArena overall leaderboard; its Thinking version rose 8 spots over MiMo-V2.5 and matched Claude Sonnet 4.6 performance on frontend coding tasks.

Ramp says Anthropic reached 34.4% enterprise adoption, surpassing OpenAI at 32.3% for the first time; the index is based on credit-card and invoice spending from more than 50,000 companies.

Alibaba DAMO Academy introduced I²B-LPO, an RLVR post-training framework that branches rollouts at high-entropy nodes and filters them with an information-bottleneck self-reward, reporting up to 5.3% accuracy gains and 7.4% semantic-diversity gains on math benchmarks using Qwen2.5-7B and Qwen3-14B.

Ramp AI Index data shows Anthropic reached 34.4% adoption among U.S. enterprise customers, surpassing OpenAI’s 32.3% for the first time, while its business coverage grew fourfold over one year.

Ramp’s survey of client expense data shows 34.4% of participating businesses pay for Anthropic services, while 32.3% pay for OpenAI; the snippet does not disclose sample size, customer segments, or spend levels.

StepFun released the 3.5B-parameter Step Image Edit 2 model, which ranks first in KRIS-Bench overall, factual, and conceptual categories, and is now available on the Stepfun Open Platform.

Reddit user kexxty ran 288 structured-output calls through OpenRouter models, including Llama 3, Mistral, Command R, DeepSeek, and Qwen, and found similar JSON failure categories across local and API-only models. The MIT-licensed Python library outputguard validates against JSON Schema, applies 15 ordered repair strategies, includes 2,001 tests, and has no LLM provider dependency.

Timothy Gowers tested ChatGPT 5.5 Pro and said it independently solved an open additive number theory problem in 17 minutes with only a simple prompt, producing PhD thesis-level work; he warned that this pace threatens mathematics research training, while Terence Tao said human value lies in digesting and deeply understanding proofs.

OpenRouter says Pareto Code observes the Pareto frontier using real market demand; DeepSeek V4 Pro ranks first, followed by GPT 5.4 Mini and Gemini 3.1 Pro, while the post does not disclose the scoring formula or evaluation sample size.

Claw-Eval-Live evaluates 13 frontier models on 105 tasks, and the top model stays below a 70% pass rate, while HR tasks average only 6.8% pass rate.

Claude Mythos Preview reached a 50% success rate on METR Time Horizons tasks that take humans 16 hours, while only 5 of 228 tasks exceeded the 16-hour range, so the article says METR lacks enough samples to quantify longer-horizon performance.

Timothy Gowers tested ChatGPT 5.5 Pro on additive number theory problems, where it produced an optimal quadratic upper-bound construction in 17 minutes 5 seconds, then generated a LaTeX preprint in 47 minutes; the article says arXiv rejects AI-generated content, so the result remains on Gowers’s blog.

A five-week experiment tested about 1,400 daily work tasks, where local 35B models such as Qwen 3.6 35B handled about 50% and averaged 2.8-second responses, 2.1 times faster than Claude Opus 4.5, while the cloud model still led complex reasoning by about 20%.

A Reddit LocalLLaMA user ran 300+ tests on Qwen 3.6 27B MTP quants, finding coding draft acceptance at 79-89% and F16 coding speed up 171%, while Q4_K_M creative writing slowed down 9%.

A Science study reports that OpenAI o1 reached a 67% correct or near-correct diagnosis rate on real emergency department data, exceeding doctors at 50-55%, but the study did not cover long-term inpatient data or imaging diagnosis.

East China Normal University and Fudan University researchers proposed Next-ToBE, a training objective that keeps standard autoregressive inference while adding a soft target over future-token windows, and the article reports the method ranked best in 35 of 36 experiments across Qwen2.5-Math-1.5B, Qwen2.5-Math-7B, and Llama3.1-8B-Instruct.