Kernel @kernelInv
Joined June 2025-
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new paper writes each weight as a chain of tiny choices. sign, power-of-two shift, zero gate, index into a shared table. on Llama-3-8B it gets 7.78 WikiText-2 perplexity vs 8.03 for GPTQ. check the bits though, 3.33 per weight vs 3.25, MLP layers only. arxiv.org/abs/2610.31931
paper: Cache Engineering for Agents, arxiv.org/abs/2610.28577 code: github.com/thu-nics/AKV I'm building margin to read papers this way. predict first, then check the table: readmargin.pages.dev/?ref=x
caveats. +1.7 is an average and it loses a few cells (mirothinker swe-verified 7.4 vs 7.8). and the authors built the serving engine they benchmark on. I'd want to see it in someone else's harness before quoting the cost number.
most agent harnesses summarize old history once the context fills up. a new paper says that's the expensive way to do it, and the reason is the KV cache. guess first. summarize at 96K tokens, or drop old tool outputs from the cache. which scores higher, which is cheaper?
someone ran a checker over 300 runs of AI research agents, looking for integrity problems. it flagged 299. 13.2 per run. most common: misreporting what was actually run, and inflating the contribution. the final report is the part I'd trust least. arxiv.org/abs/2610.31372
lora with actual numbers, since it shows up in half the papers I read. one 4096x4096 attention matrix is 16.8M weights. lora freezes it and trains B (4096x16) and A (16x4096). 131k params, 0.78%. B starts at zero, so at step 0 you have the exact pretrained model.
adam's bias correction in numbers, since I skipped it for years. step 1, β1=0.9, β2=0.999, gradient g: m = 0.1g v = 0.001g² uncorrected, the step scales with m/√v ≈ 3.2, so your first update is ~3x your lr. divide m by 0.1 and v by 0.001 and it's 1. that's all section 3 is.
@QuantaCortex yes. r across configs only says the knobs are independent, it says nothing about whether the same queries fail both. joint pass rate per query is the number I'd want
a RAG paper ran a simple test. take the 3 best single tweaks for retrieval: cross-encoder reranking, turning off MMR, document-awareness prefixes. each measured alone. add up their gains and you'd predict +47% NDCG@10. guess what the combined pipeline got.
new live-web deep search benchmark (OpenDS-World, 200 tasks). best model 39%. humans 21.1%. before you quote that: 21.1 is per attempt. 5 people tried each task with just a browser, and at least one of them solved 115 of 200. that's 57.5%. arxiv.org/abs/2610.30181
paper: arxiv.org/abs/2610.29722 code: github.com/dolev31/lasagna I'm building margin around this kind of reading. guess the result first, then check it against the table: readmargin.pages.dev/?ref=x
they pre-declared the hypotheses before running anything, which almost no RAG ablation does. it's why I trust their +17 more than most +47s I see. habit worth stealing: when a paper reports a gain in isolation, ask what it does next to everything you already run.
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