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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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Local AI Coding Agent, Part 3: When the Context Runs Out, the Agent Says It's Done
We ran a local coding agent six times with a 4,096-token context. Four runs ended with pytest passing on a repo nobody had changed: the model's answer was cut off at the end of the window, and agent.py read an answer with no tool call as 'done'. Adding two things, clearing old tool output and dropping cut-off answers, took the agent to the end in 3 of 3 runs at 8K with all 6 hidden tests passing (the Part 2 code stopped at the server in all 3). The price: 24 of the 26 requests right after a clearing reprocessed more than half their prompt, and at 4K two of three runs ran out of things to clear while the third hit the step limit.
Local Coding Agent Part 2: Which Commands Run Without Asking, and Tests the Agent Cannot Rewrite
Three rules for a local coding agent's harness, each tested on a todo app. A first allowlist let 0 of 23 shell commands through, because the model chains them; dropping redirects, checking each piece and adding four words to the list let 10 of 16 run without asking, and every command that asked had rm, cd or an outside path. The existing tests became read-only (put back once), and pytest runs when the model stops. Code, six runs, and the limits.
How Big a Model Can You Fine-Tune on an 8, 12, 16 or 24 GB GPU? We Ran Every Answer Under the Card's Limit
Qwen3 0.6B to 14B, fine-tuned with LoRA, LoRA-FA and VeRA at batch 1 with gradient checkpointing, every answer checked under the card's memory limit. With a bf16 base at 1,024 tokens, an 8 GB card trains 1.7B and a 24 GB card 8B; with a 4-bit base, 4B and 14B. The variant never changed the answer: the largest gap between them was 0.023 GiB allocated, 0.043 GiB reserved.
How to Build a Local AI Coding Agent from Scratch, Decision Model Included (Zero Cloud Calls)
Build a coding agent from three local parts: Flint on llama-server writes the code, a 135-line standard-library Python harness runs its tool calls, and Jeff, a 0.8B decision model on the CPU, notes every shell command before you answer y/N. Five steps, the full code, one real run on a todo app (6 of 6 hidden tests passed), and what it still got wrong.
Four LoRA Variants in a Few Lines Each: VeRA Trained 93% Fewer Numbers and Needed Slightly More Memory
Turn a 19-line LoRA layer into LoRA-FA, LoRA+, VeRA and Delta-LoRA, check three of them against PEFT on Qwen2.5-0.5B, and measure what each keeps on the GPU. VeRA trains 36,864 numbers against LoRA's 540,672 and still needs more activation memory: +0.227 GiB at 2,048 tokens, about 1% of the peak.
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.