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All Issues →Paper of the Week #8 — Agents Beat All 20 Science Benchmarks, and None of 135 Follow-up Papers Passed
This week's roundup. Agent4Sci reports that frontier agents beat human-verified baselines on all 20 scientific ML benchmarks it collected, mostly through engineering; Into the Unknown reports that none of 135 agent-written follow-up papers met every condition set by the scientists who posed the problems. Also: BizAgentBench, which grades goals and procedure separately on payment-company workflows, and CarryOn, which rebuilds an agent's context from what it has produced instead of its chat history.
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LoRA From Scratch in 19 Lines: It Matches PEFT Exactly, and Saved Less Memory Than We Predicted
Build a LoRA layer in PyTorch, put it into Qwen2.5-0.5B and check it against PEFT: identical outputs and identical training for 30 steps. Then measure what it saves on the GPU: 34% of peak memory at 512 tokens in fp32, not the half we predicted.
Claude Code on a Local Qwen3.8-Flash-Next on a 12 GB GPU Budget: llama.cpp or Strata
Point Claude Code at a local Qwen3.8-Flash-Next held to 12 GB of VRAM by changing a few environment variables, with llama.cpp or Strata. Reading the roughly 28K-token first request took llama.cpp 139-161 s and Strata about 12 s. Strata refuses every request once the conversation passes about 33,500 tokens because of Claude Code's 32,000-token output cap, so set CLAUDE_CODE_MAX_OUTPUT_TOKENS=8192 with it.
The DeepSeek-V2-Lite GGUF We Downloaded Ran Without Its Small KV Cache: Check Two Tensor Names First
DeepSeek-V2-Lite's attention should keep a 32K-token KV cache at 972 MiB in llama.cpp. The 2024 Q4_K_M GGUF we downloaded used 8,640 MiB because it lacks the attn_k_b and attn_v_b tensors. Converting the original weights ourselves brought the cache back to 972 MiB and the process from 20,162 to 11,330 MiB. Here is how to check a file in one command and convert it if needed.
Five LoRA Variants on One Table, and the Setting or Condition Each Abstract Leaves Out
LoRA, LoRA-FA, VeRA, Delta-LoRA and LoRA+ on one table: which of W, A and B each trains, where each paper says the saving is, and the setting or condition you only find past the abstract.
Pick llama-server's -c From Two Short Runs, Not a VRAM Estimate: Load Memory Within 1 MiB at Seven Checked Lengths, and the Estimate Overshot Gemma 3 at 128K by 13.7 GiB
We started llama-server at 4K and 8K on Qwen3-8B, Qwen3.5-9B and Gemma 3 4B, drew a line through the two memory readings, and picked the longest -c under a memory budget. At the seven lengths we then started, the line predicted load memory within 1 MiB. Prompt processing added 12 to 14 MiB and pushed two picks over budget, so we subtract 16 MiB more. A simple all-layers estimate overshot Gemma 3 at 128K by 13.7 GiB.
Paper of the Week #8 — Agents Beat All 20 Science Benchmarks, and None of 135 Follow-up Papers Passed
This week's roundup. Agent4Sci reports that frontier agents beat human-verified baselines on all 20 scientific ML benchmarks it collected, mostly through engineering; Into the Unknown reports that none of 135 agent-written follow-up papers met every condition set by the scientists who posed the problems. Also: BizAgentBench, which grades goals and procedure separately on payment-company workflows, and CarryOn, which rebuilds an agent's context from what it has produced instead of its chat history.