LLM

OpenAI released 700+ AI math manuscripts, then withdrew three. We split "AI proved X" into five layers, show what a machine checks, and give an audit checklist.

Same exploration path · Dev tooling

Google released Gemini 4 Argon on 2026-09-30: output limit raised from 64K to 1M tokens, 77.9% on DeepSWE v1.1, and a gated rollout to trusted cyber defenders via the Fairwind Program. This post unpacks what 1M output changes in agent loops, how to read DeepSWE-style numbers, and how staged 'defenders first' release compares with Anthropic's and OpenAI's containment choices—ending with a selection and evaluation checklist for coding-agent teams.

Reading UC Berkeley arXiv:2609.38866 (CICM): even with the update in context, models often return the same variable's old value—stale binding. Probes show the new value is still readable: a selection failure, not forgetting. Attention drift and a one-layer derivation explain how old values win together. GPT-5.6 Sol: 9/40 on 2,048-step logs, 40/40 with a snapshot. Includes an AI memory and agent-state checklist.

Same exploration path · Dev tooling

A deep read of arXiv:2609.18366 CHASE—automatic harness optimization can exploit benchmark-wide shortcuts on a fixed released protocol; a Challenger searches executable protocol transforms with a validity firewall and held-out confirmation, retaining released gains while cutting gain destruction on Syn-Ledger and OfficeQA.

Reading arXiv:2609.29429 RLCDAlignBench: TypeSafe Jev (RLCD) on 10 alignment failures × 44 benchmarks hits median AUROC 0.886 with a generic question, ~63× cheaper than LLM judges; checklist for wiring Just-Ask-Jev into an agent harness monitor.

Reading Anthropic’s guest post on Fable 5.1 inside Claude Science: planar N=4 SYM nine-loop hexagon amplitudes via an explicit science harness and minimal human intervention—harness × agent-loop, not a bigger-model story.

Same exploration path · Dev tooling

A deep read of Accenture arXiv:2609.28919—enterprise coding-agent bills are often blamed on “expensive models,” but the paper moves the blame to harness defaults; cache-safe routing with Jev recovers about 14–21% of model spend in a 10k-seat emulation, with a sovereignty ladder from observe to own.

A deep read of arXiv:2609.29769—typed classifier Jev vs three flash LLM judges on nine panels and 5,003 pairs: accuracy seldom separates, cost/latency gaps hit 29–325× and 30–220×, and correlated errors cap a cheap-first cascade at about +1.5pp.

A deep read of arXiv:2609.23986 Jev-Mem—separating memory organize/retrieve/stop into a System-One control plane—contrasted with this site’s Jev decision line and Growing Harness “sink control into code,” with checked LoCoMo numbers and a reusable design checklist.

Same exploration path · Dev tooling

A source-checked read of Strands harness and the Harness SDK—how they turn context management, sessions, tool gates, and loop control into a product—plus a selection checklist against ECC, Growing Harness, and Claude Code harness.

Same exploration path · Dev tooling

A deep read of arXiv:2609.26760 Growing Harness—turning recurring control into executable code via task feedback—contrasted with “just stuff more context” and with in-site harness / Skills / deterministic-pipeline narratives.

Learn how to build intelligent AI agents with practical skills and tools. Complete beginner-friendly tutorial covering agent skills, tool integration, computer use, file operations, and real-world examples using Claude and OpenAI.

A detailed introduction to Model Context Protocol (MCP), an open protocol that provides standardized context transmission for AI applications