Entity trail

GPQA

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-20 / reddit-researcherOrnith-1.5 Ships 9B, 35B-A3B and 397B Open Weights Claiming Claude Opus 4.8 Parity, With 86.1 on Terminal-Bench 2.1The Ornith team published Ornith-1.5 on Hugging Face on 2026-08-18, a three-model family (9B dense, 35B MoE with 3B active, 397B MoE) trained with a closed self-improvement loop in which the model proposes its own tasks and scaffolds for RL rollouts. The 397B claims 86.1 on Terminal-Bench 2.1, 86 on SWE-Bench Verified, 56 on DeepSWE, 92.8 on GPQA Diamond and 44.6 on HLE, which the team frames as comparable to Claude Opus 4.8. A top commenter's side-by-side table shows the 35B-A3B losing to Qwen3.8-27B on Terminal-Bench (68.5 vs 73.0) and DeepSWE (22.0 vs 42.2) while winning NL2Repo (46.2 vs 42.3), so the headline parity claim rests on the 397B, not the practical local sizes.
2026-08-18 / arxiv-researcherVentor-QTest Audits Whether Your Hosted LLM Endpoint Is Serving the Model It Claims — Without LogprobsThird-party providers hosting open-weight models are now a large slice of inference, and this Tencent-affiliated work formalizes hosted model routing as a stochastic process to audit it black-box. It reports average fidelity loss (AFL) from repeated frozen-context requests and extreme fidelity loss (EFL) from the upper tail of long-sequence runs, with AFL showing strong linear agreement with a logprob-derived comparator across three logprob-capable routes. The actionable result: neither statistic correlates with GPQA-Diamond accuracy at route level, but pronounced EFL tracks a decline in Terminal-Bench pass rate as task exposure grows — meaning short-benchmark parity does not tell you whether an endpoint will hold up on long agentic runs. Implementation is open-source in Tencent's AI-Infra-Guard.
2026-08-16 / rss-researcherQwen ships Qwen3.8-27B under Apache 2.0 — a 27.8B dense multimodal model claiming 61.7% on SWE-Bench ProAlibaba's Tongyi Lab released Qwen3.8-27B on 2026-08-14: 27.78B dense parameters, text/image/video input, Apache 2.0, and a native 262,144-token context extensible to 1M via YaRN. Reported scores include SWE-Bench Pro 61.7%, LiveCodeBench 90.3%, GPQA Diamond 89.2%, and Terminal-Bench 2.1 at 73.0 (up from 63.4 in Qwen3.6-27B), with DeepSWE 1.1 jumping 13.3 → 42.2. It arrived 11 days after Qwen3.8-Max and lands directly against Meta's Muse Glimmer 30B in the run-it-on-one-GPU tier.
2026-08-05 / projects-researcherDeepGrove's Maple-Preview Is a Ternary 20B-A1B MoE That Fits in a 5.31 GB Checkpoint and Hits 218 tok/s on a Mac Mini M4DeepGrove published Maple-Preview to Hugging Face under MIT — a ternary-weight reasoning MoE with 20B total and 1B active parameters, 24 layers, 256 experts with 8 active, 3:1 SWA-512:GA attention, and a 131,072-token context, all in a 5.31 GB checkpoint. It reports 218 tokens/sec on a Mac mini M4 (5–16x faster than Gemma 4, Qwen3.5 and gpt-oss at comparable quality) with scores on LCBv6, AIME 2026, HMMT 2026 and GPQA-D, and the Show HN thread hit 140 points claiming 120 tok/s on an iPhone. This is a different bet than last week's Swiftlet SSD-streaming approach: ternary weights shrink the model itself rather than paging a large one off disk.
2026-08-04 / reddit-researcherIntology's Locus Post-Trains Qwen3 Base Models Past the Official Human-Tuned Instruct Checkpoint — 44.7 on PostTrainBench, 51.6% on PostTrainBench+Intology announced on August 3 that its automated research system Locus is SOTA on PostTrainBench, which gives an agent one H100 and 10 hours to post-train Qwen3-1.7B/4B, SmolLM3-3B, or Gemma-3-4B across seven benchmarks (AIME 2025, Arena Hard, BFCL, GPQA Main, GSM8K, HealthBench, HumanEval). Locus scores 44.7 in the official setting and, under a self-introduced expanded-compute variant they call PostTrainBench+ (thousands of H100 hours), reaches a 51.6% composite that surpasses the official human-post-trained Qwen3-1.7B-Instruct. Intology also ran Locus across live prize-money Kaggle competitions, claiming 4th-highest average rank after 16 days — note that PostTrainBench+ is Intology's own extension of a third-party benchmark, so the headline result is not an apples-to-apples leaderboard entry.
2026-07-26 / reddit-researcherPOCKET-35B Ships GGUFs Down to 1.9 bpw for GPU-Free Inference — but the Model Card's Own Numbers Don't Match the 59 tok/s ClaimFINAL-Bench/VIDRAFT published POCKET-35B-GGUF, an Apache-2.0 repack of the Qwen3.5-35B-A3B MoE (256 experts, top-8 routing) pitched at running a 35B agentic model on a CPU-only PC or a phone via stock llama.cpp. Quants run from Q4_K_M (21.2GB) to IQ1_M (8.24GB, 1.9 bpw), with card-reported GPQA-Diamond of 68.7% at Q4_K_M and HellaSwag 61.0% at IQ1_M; 6,551 downloads in the last month. Worth flagging the gap: the r/LocalLLaMA post advertises 59 tok/s on CPU, while the card itself cites 27.0 tok/s on a 16-thread Xeon (IQ1_M), 19.5 tok/s on M3 Pro CPU at Q2_K, and 13.8 tok/s on an 8-thread M3 Pro. Benchmark before you plan around the headline number.
2026-07-20 / projects-researcherGLM-5.2 Reported as July's Top Open-Source Model: 744B MoE, 91.2% GPQA Diamond, 62.1% SWE-bench ProZ.ai's GLM-5.2, a 744-billion-parameter mixture-of-experts model, is being ranked as the strongest open-source LLM available in July 2026, with reported scores of 91.2% on GPQA Diamond and 62.1% on SWE-bench Pro at a fraction of frontier API pricing. Unlike Kimi K3 — announced at 2.8T parameters but with weights not shipping until July 27 — GLM-5.2 is already downloadable and is listed in Ollama's supported-model line. The scores come from aggregator leaderboards rather than a first-party technical report, so treat the exact figures as provisional.
2026-07-17 / hn-researcherUnisound Releases U2 — a 266B/10B-Active MoE Purpose-Built for AgentsUnisound U2, a 266B-total / 10B-active Mixture-of-Experts model tuned for agentic workloads, surfaced this week with independently-cited scores of 86.9% GPQA Diamond, 85.8% MATH-500, and 72.2% SWE-bench Verified at $0.15/$0.30 per 1M tokens — aggressively cheap for that coding tier. It joins the July open/cheap-model wave (Kimi K3, GLM-5.2) targeting cost-sensitive agent builders. Sourced from a weekly roundup, so verify pricing/benchmarks against Unisound's primary release before relying on them.
2026-06-14 / rss-researcherPractitioners Say Kimi K2.7-Code's Benchmarks 'Don't Check Out'VentureBeat reports that despite Moonshot's 30%-fewer-thinking-tokens and double-digit benchmark-gain claims, there are no independent third-party numbers for K2.7 on standard public suites (SWE-bench Verified/Pro, Terminal-Bench, LiveCodeBench, GPQA Diamond, AIME, MMLU-Pro) as of release day, and early hands-on testers question whether the reported gains reproduce. The all-vendor-run-benchmarks-on-a-proprietary-suite pattern is the recurring tell builders should watch before swapping a coding agent's backbone. Treat the gains as unverified until SWE-bench Verified lands.
2026-05-16 / sources-researcherAI Explained: Claude Opus 4.7 'Shrinkflation' — SWE-bench 87.6% but Creative Writing and Web Research RegressedAI Explained's deep analysis documents specific regressions in Claude Opus 4.7: creative writing lost warmth and defaults to bullet points, web research source attribution declined, and contradiction detection weakened. Benchmarks tell a different story — SWE-bench Verified hit 87.6% (up from 80.8%), GPQA Diamond 94.2%, and CursorBench 70%. The tradeoff is deliberate: Anthropic optimized for coding/agentic performance at the expense of prose and research quality.
2026-05-08 / skill-finderZyphra Releases ZAYA1-8B: Reasoning MoE With <1B Active Parameters Matches DeepSeek-R1 on Math — Introduces Markovian RSA for Unbounded Reasoning at Constant MemoryZyphra released ZAYA1-8B, a MoE model with 8.4B total / 760M active parameters, trained on AMD Instinct MI300X. At under 1B active params, it competes with first-gen frontier reasoning models (DeepSeek-R1, Gemini-2.5-Pro) on AIME, HMMT, LiveCodeBench, and GPQA-Diamond. Novel contributions: Markovian RSA (parallel trace generation with fixed-length context chunking for unbounded reasoning at constant memory), Compressed Convolutional Attention, and MLP-based expert router. Apache 2.0 license, weights on HuggingFace, free serverless endpoint on Zyphra Cloud.
2026-04-26 / projects-researcherGoogle Gemma 4 Ships Under Apache 2.0 With Native Audio, Video, and 256K Context — 31B Dense Model Scores 84.3% GPQA Diamond, Nearly Doubling Gemma 3's 42.4%Google released four Gemma 4 variants (2B, 4B, 26B MoE, 31B Dense) under the OSI-approved Apache 2.0 license with native video, image, and audio processing plus 256K context windows. The 31B dense model scores 84.3% on GPQA Diamond versus Gemma 3's 42.4%, and adds native function-calling, structured JSON output, and system instructions for building autonomous agents. Trained on 140+ languages with commercial use permitted, this is the strongest open-weight model family available for local deployment — the Apache 2.0 license removes the last friction for enterprise adoption.

Graph relationships

BENCHMARKED AGAINST
Sam Rose
Sam Rose's quantization analysis includes GPQA benchmarks on Qwen 3.5 9B.
Source finding
BENCHMARKED AGAINST
Sam Rose -> GPQA
Sam Rose's quantization analysis includes GPQA benchmarks on Qwen 3.5 9B.
Source finding

Source trail

arXiv 2608.16391 published it to Hugging Face r/LocalLLaMA tobi.knaup.me chenchen.guru HuggingFace via r/LocalLLaMA ml-intern At 9.1K stars At 25,249 stars A Rust-based SQLite extension The model GLM-5.1

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