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Paper of the Week
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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How to Run Multimodal Models on an 8 GB GPU: All Four LFM3 Models Peak Under 5 GiB
A model-by-model guide to running LiquidAI's LFM3 text, vision, audio and omni models with llama.cpp. At full context, with an image or audio clip going in (a long prompt for the text model), the heaviest moment for each model took 3.7 to 4.8 GiB, inside an 8 GB card's usable memory (measured on an A100). Also: how to size a model for your card from two short test runs, and why the VL model can hand back an empty answer.
autoharness: "Summarize the Chat for Next Time" Is One Line. Why Is the Code 3,104 Lines?
We traced autoharness (3,104 lines of Python) from one conversation to a skill a later session reads, then ran it in Claude Code connected to Qwen3.8 Flash Next on Strata. The model only proposes; Python checks and writes. In our run, 5 of 11 proposals were sent back at submit time, the promoter wrote the other 6, and one reflection prompt used 64,655 of 65,536 tokens.
Anthropic Says Fold Your Subagents Into One Loop. We Ran Its Kit: 2.5x Faster on Average, 18 of 19 Tasks vs 16
We ran Anthropic's single-agent-loop-kit on a local model (Qwen3.8 Flash Next). Over 19 tasks the router app completed 16 and the single loop 18, at 19.7 s vs 8.0 s mean per task. The router looped between three team rules into an empty reply; the single loop failed one return question (t05) because of tool design. One model, deterministic decoding, so no significance claim.
Why LLMs Forget, and How Google's 'Sleep' Moves Memories Into a New Slot
Teach a model a second set of facts and it loses the first. Google's Sleep paper copies the fast memory into a new small slot before it is overwritten, then practices on examples it writes itself. In our 100-fact toy, a sleep step kept 73.2% of the old facts against 50.1% without it, better in all 20 runs, although our first attempt made forgetting worse. The paper reports AIME 2024 at 79.2 vs 73.8 for the base Qwen3-8B and has not released code.
Attention Sink: Why Qwen3-8B Gives Up to 77% of Its Attention to the First Word, and Why 'Banana' Works Just as Well
We measured where Qwen3-8B looks: from layer 7 on, 26-77% of attention goes to the first token, and swapping that token for ' banana' changes almost nothing (0.4588 vs 0.4577). Five papers explain why: softmax must spend attention somewhere, the first token is the one spot every token can see, the spot works as a no-op, streaming breaks without it, and a gate on the attention output cuts it from 46.7% to 4.8% in the model the paper analyzes. Notebook included.
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.