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All Issues →Paper of the Week #9 — The Best Agent Passed 46 of 168 Physics Research Runs
This week's roundup. In FrontierPhysics, 42 physicists turned their own research into 56 tasks, and the best agent configuration passed 46 of its 168 runs (27.4%); the failures were mostly loose reasoning, not missing physics. IdeaTaste finds that agent-written research proposals overlap with the work researchers actually published at about 18% of the rate human papers overlap with each other. Also: Route Anything, where a perfect per-question model picker would gain 5.12 points but trained pickers gained 0.30; CoworkOS, a harness that ran five rounds of fine-tuning on another model; and LoCo, which cuts a 7B reasoning model's answers by about 44% on math.
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We Ran Anthropic's Memory 'Dreaming' on Our Own Claude Code Notes
Anthropic describes an offline pass that rewrites an agent's memory. We ran one on our 165 Claude Code memory notes. The index shrank 46%. When we cut the injected index short, questions about the notes at the bottom of the index fell from 92% to 77% correct; after the rewrite they came back to 93%, with 35% fewer input tokens than the uncut original. A fact-by-fact check found details the questions missed, such as a commit hash and exact checkpoint names.
Long-Term Memory for a 32K Local Agent Without More GPU Memory: Testing Mnemonic Proxy
AI models do not remember; apps resend the conversation, and the context window limits that. Mnemonic Proxy keeps the original text in an archive instead of summarizing it. In our nine-turn test with Qwen3.5-9B and opencode, 128K filled up around turn 6, while 32K with the proxy, on the same GPU memory, reached turn 9 with the turn-1 values intact. The model fetched them with recall in one session of three without a hint.
One Sentence Through GPT-2, Qwen3 and a Mamba Hybrid: Where Does the Name Come From?
We wrote the forward pass of GPT-2, Qwen3-0.6B-Base and Granite-4.0-H-350M by hand and followed the last token of one sentence through every layer. All three answer Alice. In the hybrid, switching off the four attention layers changes nothing at 18 tokens, but at 1,410 tokens the model without them falls to 0.002, while the full model stays at 0.990.
28 Attention Designs, Each With a GIF and a Short Function: From Bahdanau to Latent Sparse Attention
Additive attention, MHA, GQA, MLA, sinks, BigBird, DSA, MoBA, Reformer, ClusterKV, LSA, Gated DeltaNet and more, grouped by what each one changes. Each has a GIF, a short PyTorch function and what it improved, why and at what cost, with the papers' own numbers. The functions are teaching versions checked on CPU: we did not train them or measure their speed.
Paper of the Week #9 — The Best Agent Passed 46 of 168 Physics Research Runs
This week's roundup. In FrontierPhysics, 42 physicists turned their own research into 56 tasks, and the best agent configuration passed 46 of its 168 runs (27.4%); the failures were mostly loose reasoning, not missing physics. IdeaTaste finds that agent-written research proposals overlap with the work researchers actually published at about 18% of the rate human papers overlap with each other. Also: Route Anything, where a perfect per-question model picker would gain 5.12 points but trained pickers gained 0.30; CoworkOS, a harness that ran five rounds of fine-tuning on another model; and LoCo, which cuts a 7B reasoning model's answers by about 44% on math.
Latent Sparse Attention: 72x in One Layer, 9x for the Whole Model at 10M Tokens
The paper reports 72.4x prefill in one Qwen3.8-27B layer and 9.21x to first token for the whole model at 10M tokens, with almost no first-token gain at 512K. We follow real queries through a small LSA index on Qwen3-8B to show how it did, what its limits are and what it missed. We did not run the LSA server.