mirror of
https://github.com/wassname/evil_MoE.git
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d111db25f7
probe_distill.py is one script with three modes (default, --teacher-only, --replay-dir) so vanilla and projected arms can replay the same teacher rollouts apples-to-apples. Per-sample delta_S.grad snapshot diff gives cos(grad, v_hack) per sample without breaking accumulation semantics. rh-s65 was trained with simple_overwrite_tests hint applied to the user prompt; train.py's REF_PASS_TEST_SYSTEM_PROMPT override took us off that distribution (0/8 hacks). load_problems_rh restores the no-intervention setup -> 8/8 hacks at step 0. probe_uat.py defines four UATs and reports PASS/FAIL: T1 teacher hack >=0.30, T2 vanilla cos coverage >=90%, T3 projected cos_out<cos_in on >=80% steps, T4 cos | hacked > cos | not (one-sided t, p<0.05). Journal entry flags methodological caveat: v_hack from NLL contrastive gradient is not the GRPO policy gradient; if T4 fails, fallback is to re-extract v_hack with GRPO-contrastive loss (same pairs, adv=+/-1). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
184 lines
8.6 KiB
Makefile
184 lines
8.6 KiB
Makefile
set shell := ["bash", "-cu"]
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# Three seeds for headline arms; one seed for ablations.
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SEEDS_3 := "41 43 44"
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# spec.md §H4 substrate (reference DEFAULT_MODEL_ID).
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# At G=6, max_new=1024: peaks ~90GB on 96GB card after `logits_to_keep` fix
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# (see RESEARCH_JOURNAL 2026-05-24 (b)).
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MODEL := "Qwen/Qwen3-4B"
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TINY_MODEL := "llamafactory/tiny-random-qwen3" # qwen3 arch, ~6M params, smoke only
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BASE := "uv run python -m projected_grpo.run" # tiny-model smoke harness (fast-dev-run)
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TRAIN := "uv run python -m projected_grpo.train" # real LeetCode GRPO entry point
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default:
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@just --list
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# fast-dev-run: tiny-random model, full smoke pipeline end-to-end, ~1-2 min, beartype on.
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fast-dev-run *ARGS:
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BEARTYPE=1 {{ BASE }} --fast-dev-run --model={{ TINY_MODEL }} {{ ARGS }}
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# Real-pipeline presets (train.py = AntiPaSTO + Dr.GRPO + LeetCode rewards).
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# smoke = Qwen3.5-0.8B 10 steps, fits 24GB. Mechanism verification only.
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# full = Qwen3-4B 200 steps G=6, peaks ~90GB on 96GB. spec.md §H4 substrate.
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smoke *ARGS:
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{{ TRAIN }} --preset=smoke --arm=projected --v-hack-path=out/v_hack_smoke.safetensors {{ ARGS }}
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smoke-vanilla *ARGS:
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{{ TRAIN }} --preset=smoke --arm=vanilla {{ ARGS }}
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smoke-both:
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{{ TRAIN }} --preset=smoke --arm=vanilla
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{{ TRAIN }} --preset=smoke --arm=projected --v-hack-path=out/v_hack_smoke.safetensors
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# H4 baseline at spec substrate. No v_hack needed for vanilla.
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full-vanilla *ARGS:
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{{ TRAIN }} --preset=full --arm=vanilla {{ ARGS }}
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full *ARGS:
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{{ TRAIN }} --preset=full --arm=projected --v-hack-path=out/v_hack_full.safetensors {{ ARGS }}
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# Sync the rl-rewardhacking external repo (Nanda's verl wrapper).
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sync-external:
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cd external/rl-rewardhacking && git pull --ff-only
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# Warm HF cache before real runs (avoids re-download on first pueue job).
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download-model:
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uv run python -c "from huggingface_hub import snapshot_download; \
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snapshot_download('{{ MODEL }}', allow_patterns=['*.json','*.txt','tokenizer*','*.safetensors'])"
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extract-vhack-smoke:
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uv run python -m projected_grpo.extract_vhack_grad \
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--model=Qwen/Qwen3.5-0.8B \
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--dtype=bf16 \
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--out-path=out/v_hack_smoke.safetensors \
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--train-grads-path=out/vhack_grads_train_smoke.safetensors
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extract-vhack-full:
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uv run python -m projected_grpo.extract_vhack_grad \
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--model=Qwen/Qwen3-4B \
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--dtype=bf16 \
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--out-path=out/v_hack_full.safetensors \
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--train-grads-path=out/vhack_grads_train_full.safetensors
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verify-vhack-smoke:
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uv run python -m projected_grpo.verify_vhack_heldout \
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--model=Qwen/Qwen3.5-0.8B \
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--dtype=bf16 \
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--v-hack-path=out/v_hack_smoke.safetensors \
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--out-path=out/vhack_heldout_cos_smoke.safetensors
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verify-vhack-full:
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uv run python -m projected_grpo.verify_vhack_heldout \
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--model=Qwen/Qwen3-4B \
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--dtype=bf16 \
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--v-hack-path=out/v_hack_full.safetensors \
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--out-path=out/vhack_heldout_cos_full.safetensors
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# =============================================================================
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# SWEEPS — what to run, in order
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# =============================================================================
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#
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# 1. `just probe-full-seed 41` — single-seed gate (~6-9h sequential).
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# extract -> verify-heldout -> vanilla -> projected. Inspect before sweep.
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# 2. `just queue-full` — 3-seed headline sweep (~36-54h).
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# Queues 1 extract + 3 vanilla + 3 projected. Only run after probe passes.
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#
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# Helpers (used by queue-full, can also run standalone):
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# just queue-vanilla / just queue-projected — 3 seeds of one arm.
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# just probe-h4 41 — vanilla only on a single seed (H4 substrate sanity).
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# =============================================================================
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# Single-seed gate as 4 DEPENDENT pueue tasks: extract -> verify -> vanilla -> projected.
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# Each stage is its own inspectable task; -a chains them so a stage only starts if
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# the prior succeeded (nonzero exit blocks the chain). Gates A/B are enforced by exit
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# code (verify exits nonzero if frac>0<=0.50). Gate C (vanilla actually hacks) is NOT
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# an exit-code gate -- vanilla exits 0 regardless -- so inspect its HACK_RATE around
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# step ~100 and `pueue kill` the queued projected task if it didn't hack.
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# Use BEFORE `queue-full` to avoid burning 5/6 of the sweep compute on a dead substrate.
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probe-full-seed seed="41":
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#!/usr/bin/env bash
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set -euxo pipefail
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EX=$(pueue add -p -w "$PWD" -o 9 -l "why: extract v_hack full; resolve: Gate A zero-norm=0, ~252 modules" -- just extract-vhack-full)
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VF=$(pueue add -p -a "$EX" -w "$PWD" -o 9 -l "why: verify heldout cos; resolve: Gate B frac>0>0.50, mean>0.20" -- just verify-vhack-full)
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VA=$(pueue add -p -a "$VF" -w "$PWD" -o 9 -l "why: vanilla seed{{ seed }} @ matched batch; resolve: Gate C H4 HACK_RATE>0.30 by ~step100" -- {{ TRAIN }} --preset=full --arm=vanilla --seed={{ seed }} --out-tag=_full_vanilla_seed{{ seed }}_probe)
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pueue add -a "$VA" -w "$PWD" -o 8 -l "why: projected seed{{ seed }} @ matched batch, v_hack NOT post-hoc; resolve: Gate D H1 HACK_RATE<vanilla at matched PASS" -- {{ TRAIN }} --preset=full --arm=projected --seed={{ seed }} --v-hack-path=out/v_hack_full.safetensors --out-tag=_full_projected_seed{{ seed }}_probe
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pueue status
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# Vanilla-only single-seed probe. Cheapest way to answer "does this substrate
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# actually hack with our reward function" (spec.md §H4).
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probe-h4 seed="41":
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{{ TRAIN }} --preset=full --arm=vanilla --seed={{ seed }} --out-tag=_full_vanilla_seed{{ seed }}_h4
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# Headline 3-seed sweep: extract + 3 vanilla + 3 projected via pueue.
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# Only run after probe-full-seed shows vanilla hacks and projected fires.
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queue-full:
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#!/usr/bin/env bash
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set -x
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pueue add -w "$PWD" -o 6 \
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-l "why: extract full v_hack for exact checkpoint; resolve: out/v_hack_full.safetensors exists and train.py key/rank check passes" \
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-- just extract-vhack-full
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just queue-vanilla full out/v_hack_full.safetensors
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just queue-projected full out/v_hack_full.safetensors
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# 3-seed vanilla baseline (H4: baseline hack rate >30% at step 200).
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queue-vanilla preset="full" vhack="out/v_hack_full.safetensors":
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#!/usr/bin/env bash
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set -x
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for seed in {{ SEEDS_3 }}; do
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pueue add -w "$PWD" -o 5 \
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-l "why: H4 sanity {{ preset }}, does exact train.py substrate reward-hack; resolve: if <30% hack at final window, escalate model/prompt before H1" \
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-- {{ TRAIN }} --preset={{ preset }} --arm=vanilla --seed=$seed --out-tag=_{{ preset }}_vanilla_seed$seed
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done
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# 3-seed projected (H1: -30pp hack vs vanilla at matched pass).
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queue-projected preset="full" vhack="out/v_hack_full.safetensors":
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#!/usr/bin/env bash
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set -x
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for seed in {{ SEEDS_3 }}; do
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pueue add -w "$PWD" -o 4 \
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-l "why: H1 {{ preset }}, projected delta_S grad reduces hack rate >=30pp at matched pass; resolve: compare to same-seed vanilla logs" \
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-- {{ TRAIN }} --preset={{ preset }} --arm=projected --seed=$seed --v-hack-path={{ vhack }} --out-tag=_{{ preset }}_projected_seed$seed
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done
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# Diagnostic: print v_hack steering check (CAA-style) on base model.
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# H: adding v_hack at inference should shift completions toward hack-flavored text.
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vhack-check *ARGS:
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{{ BASE }} --vhack-check --model={{ MODEL }} {{ ARGS }}
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# Distillation probe: hacky teacher (ariahw rh-s65) samples, student trains
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# with per-sample v_hack cosine logging. step_NNN.jsonl.gz per step is replayable.
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probe-distill *ARGS:
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uv run python -m projected_grpo.probe_distill --v-hack-path=out/v_hack_full.safetensors {{ ARGS }}
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# UAT pipeline: 1) teacher pool 2) vanilla replay 3) projected replay 4) analyze.
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# T1 teacher hack >= 0.30 T2 vanilla cos coverage >= 90%
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# T3 projected cos_out<cos_in on >= 80% of steps T4 cos | hacked > cos | not (p<0.05)
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probe-teacher-pool steps="20":
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uv run python -m projected_grpo.probe_distill --teacher-only --steps={{ steps }}
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probe-vanilla-replay steps="20":
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uv run python -m projected_grpo.probe_distill --arm=vanilla --steps={{ steps }} \
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--replay-dir=out/probe_distill/teacher_pool \
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--v-hack-path=out/v_hack_full.safetensors
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probe-projected-replay steps="20":
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uv run python -m projected_grpo.probe_distill --arm=projected --steps={{ steps }} \
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--replay-dir=out/probe_distill/teacher_pool \
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--v-hack-path=out/v_hack_full.safetensors
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probe-uat:
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uv run python -m projected_grpo.probe_uat
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# Print the results table prototype.
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table-proto:
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@cat docs/table_proto.md
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# Show recent pueue logs.
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log:
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pueue log -l 40
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# Append a new research journal entry (interactive).
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journal:
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@echo "Edit RESEARCH_JOURNAL.md and prepend a dated entry."
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@${EDITOR:-vi} RESEARCH_JOURNAL.md
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