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Paper of the Week
All Issues →Paper of the Week #7 — Told to Skip the Reasoning, It Kept Working in English and Mostly Stopped in Korean
This week's roundup. We gave Qwen3-8B the Thinking Inertia paper's "no reasoning, just the answer" instruction in English and in Korean: on 30 open-ended math questions it answered directly 0% of the time in English and 63.3% in Korean, where accuracy fell to 36.7%. Also: what a passing test guarantees, how much of an uncertainty score is answer length, agent cost tracking turns, who checks Lean's checkers, the conditions behind SSD-streaming speeds, decision models when their own server is at stake, three ways to write context down, and grading choices that reorder rankings.
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Claude Code, Codex, OpenCode, Hermes: How Many of the Eight Harness Parts Do They Open Up?
We compared four agent harnesses (Claude Code, Codex, OpenCode, Hermes Agent) against the eight parts named in the Harness² paper, checking in each tool's official documentation whether a part is exposed as a file or setting, and opening some of the installed tools ourselves. In the docs we read, none of the four opens all eight.
What Is an Agent Harness, and What Does Harness Engineering Do?
An agent is a model plus a harness: the prompts, rules, memory, tools and scripts around it. Where the term harness engineering comes from, the eight parts Google's Harness² paper edits, and what the practice looks like in a repository you already have.
DeepSeek's Config Says 16 KV Heads, Its Cache Holds a Ninth of That: Seven Ways LLMs Handle Attention, and Which Models Use Each
DeepSeek-V2-Lite lists 16 KV heads, which predicts 8,640 MiB of cache at 32K; llama.cpp allocated 972 MiB. A guide to MHA, MQA, GQA, sliding window, MLA, sparse attention and linear-attention hybrids: what each stores, which models use it (checked against their config.json), and what it gives up.
Implementing the Attention Methods Used in LLMs: From Multi-Head Attention to Gated DeltaNet
MHA, MQA, GQA, sliding window, attention sinks, MLA with weight absorption, DSA-style sparse attention and Gated DeltaNet, each as a short PyTorch function with a check that it does what it claims. The MLA check runs on DeepSeek-V2-Lite's real layer-0 weights; the Qwen3.5-9B state size from the code matches llama.cpp's 50.25 MiB.
Four Times the Context, Less Memory: How Six Models Store the KV Cache, Measured in llama.cpp and vLLM
In llama.cpp, Qwen3.5-9B with a 128K context allocated 4,146 MiB of KV cache; Qwen3-8B needed 4,608 MiB for 32K. We calculated the cache from config.json for six models with four kinds of cache (full attention, sliding window, MLA, linear-attention hybrid), measured it in llama.cpp and vLLM, and list what to plan with for each.
llama.cpp vs vLLM: Deployment Methods and Strategies, Part 1: Memory
Deploying the same models on llama.cpp and vLLM, part 1: memory. What each runtime reserves for the KV cache and why (Gemma 3 4B at 32K: 814 MiB against 2,682 MiB), the settings that move it (-ub, --swa-full, max_num_batched_tokens, prefix caching), and four other differences: lengths past training, --parallel, GGUF conversion and vLLM's startup kernel build.