Entity trail

AIME25

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

Briefing refs

Findings

Edges

Sources

Corpus findings

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
llama.cpp
llama.cpp KV cache quantization performance was tested on AIME25.
Source finding
BENCHMARKED AGAINST
llama.cpp -> AIME25
llama.cpp KV cache quantization performance was tested on AIME25.
Source finding

Source trail

128K context, 2.6B parameters r/LocalLLaMA r/LocalLLaMA / GitHub

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