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All Issues →Paper of the Week #10 — Reorganize the Agent's Workspace, and Haiku 4.5 Runs at a Quarter of the Cost
This week: EnvOpt (UC Berkeley, UChicago, Bespoke Labs) lets one agent rebuild the workspace another agent works in, without answer keys. On AutomationBench, Claude Haiku 4.5's cost fell 72.7% and its pass rate rose from 2.33% to 16.67%. The same workspace cut Opus 5's cost by 68.1%, with pass rate moving from 33.83% to 35.17%. We checked every number against the paper's tables and note where the shared figures need context. Also: SlackBench, from the same group, where the best of 33 agent setups scored 80.25% on questions about a research team's Slack, and reading the workspace as plain files beat Slack tools by 7.85 points on average.
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Paper of the Week #10 — Reorganize the Agent's Workspace, and Haiku 4.5 Runs at a Quarter of the Cost
This week: EnvOpt (UC Berkeley, UChicago, Bespoke Labs) lets one agent rebuild the workspace another agent works in, without answer keys. On AutomationBench, Claude Haiku 4.5's cost fell 72.7% and its pass rate rose from 2.33% to 16.67%. The same workspace cut Opus 5's cost by 68.1%, with pass rate moving from 33.83% to 35.17%. We checked every number against the paper's tables and note where the shared figures need context. Also: SlackBench, from the same group, where the best of 33 agent setups scored 80.25% on questions about a research team's Slack, and reading the workspace as plain files beat Slack tools by 7.85 points on average.
We Built Five Multi-Agent Architectures and Gave Them the Same Job: One Agent Won, the Handoff Team Never Saved a File
Single agent, skills, subagents, handoffs and router, written in one Python file and run on a local Qwen3.5 27B. On 16 policy questions (x3), skills got 48 of 48 and handoffs 27. On a coding task graded by 60 hidden tests, four patterns scored 60/60 every time; handoffs scored 0 in all three runs. Subagents and router used about twice the tokens of skills on coding.
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.