Entity trail

AIME25

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

Briefing refs

Findings

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Sources

Corpus findings

2026-08-29 / reddit-researcherNew Qwen3.8-27B GGUFs Claim BF16-Level AIME25 at 2.75 bpw by Learning the Quantizer and the Search TogetherA release post on r/LocalLLaMA (98 upvotes) publishes Qwen3.8-27B GGUFs built with two methods: GSQ (Gumbel-Softmax Quantization), post-training scalar quantization that jointly learns grid assignments and scales at 2-3 bits, and RCO (Riemannian Constrained Optimization), which assigns a quant type per tensor under a strict size budget by gradient descent on the task loss. The 3.00 bpw build (10.1 GB) matches the BF16 base on AIME25 at 100.00, lands within about a point on GPQA-Diamond (88.89 vs 89.90) and LiveCodeBench v6 (84.57 vs 85.71), and the 2.75 bpw build (9.3 GB) still hits AIME25 100.00 with a zero-shot average exceeding BF16. All three files run unmodified in llama.cpp, Ollama and LM Studio.
2026-08-26 / arxiv-researcherTrial Parallelism Is 65.5% of Parallelizable Reasoning Compute, and Parason Converts It Into 1.7x Wall-Clock SpeedupPrior parallel-reasoning systems focus on subtask parallelism, decomposing a task into independently solvable chunks, and miss what the authors measure as the larger share: trial parallelism, where multiple speculative attempts explore, verify and aggregate competing hypotheses at once, accounting for 65.5% of DeepSeek-V4's parallelizable reasoning steps on HLE and growing more dominant on harder problems. Parason converts sequential traces into structured parallel trajectories using a context-free grammar, trains with Parallelism-Aware GRPO whose reward balances accuracy, latency and both parallelism ratios, then executes the learned structure through tool calls at inference. On AIME24 and AIME25 it averages roughly 1.7x acceleration with competitive accuracy.
2026-08-07 / arxiv-researcherSelf-Distillation Without Ground Truth Matches GRPO: U-OPSD Gains 8.5-10.7% on AIME/MATH Using Only the Model's Own RolloutsU-OPSD (arXiv 2608.06296, Aug 6) removes the last external dependency from on-policy self-distillation — no ground-truth signals, no environment feedback, no larger teacher. It samples multiple rollouts, builds a pseudo-solution by majority vote under a self-consistency threshold, conditions a teacher distribution on the shortest pseudo-solution, and distills it into prefixes of the model's longest incorrect completion, correcting the model exactly where it is confidently wrong. Across AIME24, AIME25, HMMT25, MATH500, and AMC23 it improves Qwen3 non-thinking mode by 8.5% at 4B and 10.7% at 8B, beating supervised OPSD by 3.2% and 2.3% and surpassing GRPO by 0.7-1.1% in thinking mode.
2026-08-06 / rss-researcherLiquid AI's LFM2.5-2.6B beats an 8B Gemma on tool use while running in under 2.5 GBLiquid AI released LFM2.5-2.6B, a 2.6B-parameter on-device agentic model with a 128K context that fits in under 2.5 GB of memory. It posts 51.87 on AIME25, 59.17 on IFBench, 56.88 on BFCLv4 and 62.85 on Claw-Eval — beating the 8B Gemma-4-E4B across all four and trailing the 9.7B Qwen3.5-9B narrowly. Throughput is 220 tok/s on an M5 Max CPU, ~30 tok/s on phones, with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, which makes local tool-calling agents genuinely viable on consumer hardware.
2026-03-30 / reddit-researcherkernel-anvil: 2x Decode Speedup on AMD GPUs by Auto-Tuning llama.cpp Kernels Per Model ShapeA new tool called kernel-anvil profiles GGUF model layer shapes on AMD GPUs and generates optimal kernel configurations that llama.cpp loads at runtime — no recompilation needed. The problem: llama.cpp's default Metal/HIP kernels use one-size-fits-all configs, leaving significant performance on the table for AMD hardware. The tool auto-tunes per-layer kernel parameters to match the specific model architecture, claiming 2x decode speedup on AMD GPUs. High comment-to-score ratio (39 comments on 86↑ = 0.45) indicates deep technical engagement. Reddit: r/LocalLLaMA (86↑, 39 comments).
2026-03-30 / reddit-researcherllama.cpp KV Rotation PR Shows Q8 Quantization Tanks AIME25 — But Rotation Mostly Recovers ItA llama.cpp pull request (#21038) by ggerganov reveals that existing Q8_0 KV cache quantization significantly degrades math reasoning performance on AIME25, but applying activation rotation before quantization mostly recovers quality (31.7% → 37.1%). The finding means anyone running large models with Q8 KV cache to save memory has been silently losing quality on reasoning-heavy tasks. The fix requires no model changes — it's a runtime rotation applied in llama.cpp. Debate continues on whether to make it opt-in or default. Reddit: r/LocalLLaMA (225↑, 75 comments).

Graph relationships

BENCHMARKED AGAINST
Qwen3.8-27B
Qwen3.8-27B achieved 100.00 on AIME25 with advanced quantization.
Source finding
BENCHMARKED AGAINST
llama.cpp
llama.cpp KV cache quantization performance was tested on AIME25.
Source finding
BENCHMARKED AGAINST
Qwen3.8-27B -> AIME25
Qwen3.8-27B achieved 100.00 on AIME25 with advanced quantization.
Source finding
BENCHMARKED AGAINST
llama.cpp -> AIME25
llama.cpp KV cache quantization performance was tested on AIME25.
Source finding

Source trail

r/LocalLLaMA arXiv 2608.24658 U-OPSD 128K context, 2.6B parameters r/LocalLLaMA r/LocalLLaMA / GitHub

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