eval:
- Add eval/lib/cci.py: drop-in interactive Claude session shim (pexpect/PTY)
replacing headless `claude -p`. Drives the real TUI on Max-subscription auth
(no ANTHROPIC_API_KEY), scrapes the reply + /context + /cost, and is
argv/stdin/--output-format json compatible with the old call sites.
- Wire every call site to cci.py: gist-recall (run/run2/run3),
needle-haystack (sweep/crux), gsm8k (bench), swe-bench (run_pilot),
swe-bench-pro (rep_nav/run_pro), and eval/lib/anthropic-client.mjs.
- Isolation per call: --setting-sources project --strict-mcp-config
--dangerously-skip-permissions. Recall evals keep --allowedTools Read;
swe-bench keeps the full agentic toolset. Each trial is a fresh process.
demo:
- Resolve the `opus` shorthand to the full claude-opus-4-8[1m] model in
cost-ab and effective-context a.sh/b.sh. setup.sh intentionally keeps the
base id: the proxy scope gate strips [1m] from the request before matching,
so [1m]-on-launch + base-in-scope is the correct pairing.
docs:
- FINDINGS.md: drop the 2026-06-16 Opus 4.8 re-test sections.
Does the model actually read pixelpipe's render, or coast on memorized answers?
- novel random-number problems (un-memorizable): text 100% vs image 93% (-7pp real reading tax)
- GSM8K (in training data): 97% vs 96% -- inflated ~3pp by recall
- verbatim recall from a dense render: 0/15
README 'Benchmarks' section leads with the clean 93% and reports the failure
rows too (no cherry-pick). Harness in eval/gsm8k/: gen_novel.py, render_cfg.mjs
(pixelpipe's real renderTextToPngs), bench.py -- claude -p, exact-match grading.
teamchong