# SYNTHESIS — GitHub research on ARC-AGI-3 / Milestone 2 (Task 2-b)

Date: 2026-10-01 · Method: GitHub REST API (repo search ×13 queries, code search, org/user listings, README+tree fetches).
Raw artifacts in this dir: `searchrepo_*.json`, `searchcode_milestone2.json`, `all_repos.json` (115 unique repos), `tree_*.json`, `gh_*_README.md`, `orgs_arcprize.json`, `commits_arcagi3agents.json`.

## Headline

**All four Milestone-2 Kaggle leaders are the same lineage: Tufa Labs Duck/TAAF harness + Qwen3.8-Flash-Next on one RTX PRO 6000, and the 20→28 gap is dominated by *inference engineering of memory* (FP8 KV cache → ~1.0–1.4M-token KV pools → much longer rolling per-game history), not by new agent ideas.** Confirmed by three independent sources: rellik13's write-up (14.49→22.53 with zero prompt changes), the 1zuki/arg-agi public crawl doc (`gh_1zuki_milestone2_crawl.md` — a full config comparison of the 4 winners), and da-fr's write-up (93.43% prefix-token reuse; "with the increased context, completely turning off the original text-based world model mechanism worked best").

## Repo landscape (115 unique repos found; top entries)

| # | Repo | ★ | Pushed | LB/score | Relevance |
|---|---|---|---|---|---|
| 1 | [da-fr/arc-agi-3-solution](https://github.com/da-fr/arc-agi-3-solution) | 9 | 2026-09-30 | **27.89 (LB #1)** | ⭐⭐⭐⭐⭐ Daniel Franzen's full Milestone-2 solution: patched harness + SGLang builder + patches + WRITEUP + CONFIGURATION (43KB feature reference incl. disabled experiments) |
| 2 | [LohitSiriki/arc-agi-3-milestone2-solution](https://github.com/LohitSiriki/arc-agi-3-milestone2-solution) | 1 | 2026-09-30 | **22.53** | ⭐⭐⭐⭐⭐ rellik13's `duck-18-1gc-submit`: every notebook cell as .py, full run artifacts (prompts, sglang log, benchmark.json), WRITEUP with score history + memory ablation |
| 3 | [Tufalabs/duck-harness](https://github.com/Tufalabs/duck-harness) | 90 | 2026-07-01 | M1 winner (12.58) | ⭐⭐⭐⭐⭐ The Duck + TAAF + example run + viewer; base of every top LB entry |
| 4 | — (Kaggle only) lordhansolo `arc-agi-3-milestone-2` | — | — | **23.84** | ⭐⭐⭐⭐⭐ vLLM-nightly 0.29.1rc1 + NVFP4/FP8-mixed + FP8 KV **1.417M tokens**, 14 streams, 147k ctx. No GitHub repo found. **This is the closest config to OUR stack** |
| 5 | — (Kaggle only) richardcsaky `arc-agi-3-milestone-2-submission` | — | — | **20.00** | ⭐⭐⭐ vLLM, expert-overlay checkpoint, 262k ctx, 35 seqs, int4 per-token-head KV, Marlin, expert pruning. No GitHub repo |
| 6 | [Alexyskoutnev/TWIN-ARC-AGI-3](https://github.com/Alexyskoutnev/TWIN-ARC-AGI-3) | 9 | 2026-09-17 | **93.3/100** (API agent) | ⭐⭐⭐⭐⭐ Test-time executable digital twin (Python world model, replay-validated, plan-in-model). Best-documented offline world-model method |
| 7 | [alexisfox7/PRO-LONG](https://github.com/alexisfox7/PRO-LONG) | 458 | 2026-08-21 | **97.4% best@2** (Fable 5) | ⭐⭐⭐⭐ Programmatic memory: one append-only log + agent greps it; +18pts over coding-agent baselines; arXiv 2607.20064; reproduction harness in `research/arc-agi-3/` |
| 8 | [pbshgthm/arc-skill](https://github.com/pbshgthm/arc-skill) | 89 | 2026-08-19 | **100.00 RHAE, 25/25** (Opus 5) | ⭐⭐⭐⭐ Prediction-gated actions (harness refuses unplanned presses), claim grammar, one-page NOTES.md, escalation ladder |
| 9 | [DriesSmit/ARC3-solution](https://github.com/DriesSmit/ARC3-solution) | 96 | 2026-01-27 | preview winner (12.58 StochasticGoose) | ⭐⭐⭐ CNN action-change predictor for exploration; Tufa Labs origin |
| 10 | [arcprize/arc-agi-3-benchmarking](https://github.com/arcprize/arc-agi-3-benchmarking) | 52 | 2026-09-30 | official baselines | ⭐⭐⭐ Official M2-era benchmarking harness (gpt-5.4/5.6-sol-max, opus-5, gemini-3.8, grok-4.7 provider adapters); `ARC-AGI-3-Agents` is stale since Aug 3 |
| 11 | [U4AR/qwen38-arc3-rl](https://github.com/U4AR/qwen38-arc3-rl) | 0 | 2026-09-24 | baselines ~1.6 | ⭐⭐⭐⭐ LoRA RL of Qwen3.8-27B on Duck (2×H100): reward = level-solve + token-efficiency bonus, Dr.GRPO, verbatim history for prefix-cache/packing |
| 12 | [agno-agi/arc-agi-arcade](https://github.com/agno-agi/arc-agi-arcade) | 14 | 2026-08-26 | 100.00 warm GPT-5.6 | ⭐⭐⭐ Cold/warm/seeded modes; "manuals" as transferable knowledge; contamination flagging |
| 13 | [theredbluepill/arc-interactive](https://github.com/theredbluepill/arc-interactive) | 57 | 2026-10-01 | — | ⭐⭐⭐ 249 community game environments (practice distribution!) |
| 14 | [samrishtt/arc-agi-3-kaggle-competition](https://github.com/samrishtt/arc-agi-3-kaggle-competition) | 3 | 2026-08-31 | 0.33 | ⭐⭐ Go-Explore graph explorer (deterministic, no ML): volatility-masked state hash, win-path caching, tiered click proposals |
| 15 | [sonpham-org/arc-3](https://github.com/sonpham-org/arc-3) | 3 | 2026-09-28 | — | ⭐⭐ instrumented Tufa fork, GCP spot kit, reproduction matrix |
| 16 | [dp-web4/ARC-SAGE](https://github.com/dp-web4/ARC-SAGE) | 4 | 2026-09-09 | 94.85 (frozen, engine-reading) | ⭐⭐ honest historical snapshot; world-model cartridges |
| 17 | [1zuki/arg-agi](https://github.com/1zuki/arg-agi) | — | 2026-10-01 | their LB 3.40 | ⭐⭐⭐⭐ `docs/arc-agi-3/milestone-2-public-crawl-2026-10-01.md` = best third-party M2 analysis (saved: `gh_1zuki_milestone2_crawl.md`) |
| 18 | [Shih-Yu-Yeh/arc3-dev-agi-journal](https://github.com/Shih-Yu-Yeh/arc3-dev-agi-journal) | 0 | 2026-09-26 | LB 0.87→2.56 | ⭐⭐ 33-day dev journal, same resource bracket as us |
| 19 | [54xkeee/duck-harness-glm53-flash-experiments](https://github.com/54xkeee/duck-harness-glm53-flash-experiments) | 0 | 2026-09-06 | — | ⭐⭐ GLM-5.3-Flash on Duck, evidence-prediction loop |
| 20 | [pinion05/arc-agi3-duck-harness-vllm](https://github.com/pinion05/arc-agi3-duck-harness-vllm) | 0 | 2026-08-08 | — | ⭐⭐ Duck vLLM repro on A6000: conv1d CUDAGraph patch |

Others of note: farmountain/aera-arc3-paper (epistemic agent), DenisSud/su-JEPA (latent world model, JAX), pomagrenate/dsg-ewm (executable world model; the EWM 63.8 baseline in TWIN), kanishkpaul/arc-agi3-world-models, PrimeIntellect-ai/arc-agi-3-prime-agent, aws-samples/sample-strands-agents-bedrock-arc-agi-3, LiDeChi/arc3-world-model-agent, Alexyskoutnev TWIN site. **Negative lookups:** Jeroen Cottaar (`jcottaar`) has NO ARC repo (only packing/ariel2/seismic); `NVARC3` GitHub account exists but 0 repos; "taaf arc" query → 0; Tufalabs org has duck-harness + infra forks (vllm/sglang/transformers/flashinfer forks updated 2026-09-09) + `miles` RL framework + `ladder`.

---

## Deep dives — the three public Milestone-2 winners

### 1. da-fr (Daniel Franzen) — 27.89 — github.com/da-fr/arc-agi-3-solution

Same Duck core (LM + Python tool). Changes, by impact:

**Serving/memory (the big lever).** Intel W4A16 AutoRound Flash-Next + Albucino INT4 MTP draft (3 spec steps), BF16 PLE kept in host RAM; SGLang "Pennyroyal" v2.5.3 + custom patches (Low-M BF16 GEMM, spec-state memory-budget correction, Marlin scale-dtype fix, **Mamba-checkpoint retention prefix-cache patch**, **shard prefetch patch**); FP8 E4M3 KV; harness context **128 Ki** (vs Duck's ~37k), server 136 Ki, **drain to 58 Ki on trim** (hysteresis: trim one large block, then let history regrow → **93.43% of prompt tokens served from cache**); image-aware token estimation (base64 no longer counted as text); admission limited to **10 streams** = server limit so returning games find their prefix resident; Mamba cache 60 slots; end-of-prefill checkpoint kept + LRU-refreshed.

**Scheduler.** All 25 games started at once; **priority scheduler** admits highest `P=(A+B)·C` where A = immediate score gain of finishing current level (with action-efficiency penalty, normalized by N levels), B = continuation-value lookup (3+ levels left: 8, 2: 7, 1: 5, 0: 0) faded to 0 in final 20% of runtime, C = Gaussian completion-probability falloff calibrated on 725 observed level attempts: `C = 0.25·2^-(a/115)² + 0.75·2^-(t/62000)²` (a=actions, t=generated tokens on level). Slot handover happens only at context eviction (protects prefix cache).

**Perception.** Intermediate **animation frames + text timelines** exposed as REPL variables (strongest perception win; hint only when "transient pixels" exist; inspired by jakobbrggen's TAAF-Anim notebook); `frame_diff()` object-level structured diff (moved/rotated/appeared/disappeared/recolor) on 4-connected components; images upscaled **10×** (was 4×); **difference images** after actions; **game-over fatal frame** supplied.

**Acting/agent.** `UNDO` (ACTION7) exposed as a real action — "helped substantially, suspected very important on the competition set"; **persistent model-defined Python functions** retained for the whole game (functions only, not variables); guards: batch no-op guard, stale-state guard (blocks *next* action call in same snippet after ineffective one; from level 2), terminal-state guard; `gameplay_changed` vs `board_changed` (4-cell border excluded to ignore budget bar); tool output budget ~3,072 tokens with middle truncation; faithful per-action results + explicit game-over diagnosis prompts (budget-bar rule: only fully-depleted bar means budget death) and level-transfer guidance prompts.

**Explicitly negative (their §6):** structured world-model updates (turned OFF entirely — raw long history beats it), summarization (no clear gain), animation composite images, auto-inserted timelines, stronger verification hints, death ledgers, more elaborate priority rules.

Sampling: temp **0.7**, top-p 0.95, top-k 20.

### 2. rellik13 / sirikilohit (LohitSiriki repo) — 22.53 — cleanest causal evidence

Score history: 0.99 → 3.81 (model: Qwen3.8-Flash-Next) → 6.42 (SGLang + more games) → 7.14 (49k ctx) → 9.96 (**solved-level rule memory** pinned in prompt, +2.8, the only text addition that helped) → 13.40 (**Wang's harness port**: stricter sandbox, object segmentation, test-ideas-with-code prompts) → 14.49 (harness *fixes*) → **22.53 (FP8 KV + longer history, no prompt change)**.

Final config: W4A16 + RadixArk FP8 PLE swapped in + RadixArk NVFP4 MTP (3 steps/4 draft tokens), SGLang Pennyroyal, **FP8 KV pool 1,004,288 tokens**, 16 concurrent, ctx 69,632, **history trim 57,344→45,056** (was 36,864→26,624), one-pool **UCB scheduler** with 2,400 s slices, hierarchical host tier (48GB) for KV/Mamba, thinking mode, temp 0.6.

Why FP8 KV won: server logs showed KV pool 91–97% full with requests queued for cache space — memory, not throughput, was the bottleneck. Earlier "FP8 failed" test was confounded by MXFP8 re-quant (~25% slower decode) and flags lost to comments. Key lesson recorded in `cells/21_patch_cell_v14_43_reasoning_ledger...` etc: an explicit "ledger for proven/refuted assumptions" was **never used by the agent**; adding advice/summaries consistently *hurt* (each repeated line pushed out the agent's own turns at fixed ctx); restarting stuck levels with fresh context didn't help; temp 1.0 worse; expert pruning worse; higher-res images worse; fine-tune "Swift 1.5" no better per token.

### 3. lordhansolo — 23.84 — the vLLM route (our closest template)

No GitHub repo (notebook only), config per 1zuki crawl + Kaggle discussion 744792: **vLLM nightly 0.29.1rc1** (commit e975732), mixed NVFP4/FP8 target with BF16 PLE, built-in NVFP4 MTP, **FP8 E4M3 KV, 1,417,100-token pool**, **147k server / 127k harness context**, 14 live streams, prefix caching + **FP8 indexer KV + BF16 Mamba cache**, 3,918-second per-game cap, 8-wave admission. Proves NVFP4 on vLLM is fully competitive when the KV pool and history are sized right — exactly our stack.

richardcsaky (20.00, vLLM): expert overlay checkpoint, `--max-model-len 262144`, `--max-num-seqs 35`, int4 per-token-head KV, Marlin backend, async scheduling, expert pruning.

---

## Adjacent breakthrough methods (API-model agents — techniques to port)

- **TWIN (93.3, 23/25 games, 0.61× human actions):** coding agent writes `step(grid,action)->grid` + `goal_reached(grid)` Python twin; harness **refuses scored actions while twin replay fails**; plans inside the twin (depth 8/20k nodes; goal search 14/30k); mismatch = counterexample that repairs code; probes 7.1% of actions; fits EWM baseline (63.8) with 7.7× fewer tokens. Frontier = goal inference (twins fit 99.4% on-log but only 68.6% off-log).
- **PRO-LONG (97.4% best@2, $1,750, Fable 5):** single append-only `log.txt` of all observations/actions/outcomes; agent retrieves with grep/Python; ~30-line system prompt; NO subagents, NO summarization, NO vector DB. Ablations: last-25-sections window degrades a lot; no-log collapses. Over matched baselines +18 pts at 4.2–5.8× fewer tokens.
- **arc-skill (100.00 RHAE, 7,645 actions vs human 17,135):** hard harness gate: every press needs a falsifiable prediction (`cell X,Y=V; move X,Y DX,DY; region; vanish; level+1; win; change; noop`) graded against the returned frame (7,627 predictions, 443 misses); plan = sequence of predicted steps, halts at first miss; one-page NOTES.md per game survives 115 compactions; escalation ladder sentences→Python→simulator→full world model (full model used once, never fitted, game won on rung below); planned sequences missed 2.9% vs single test presses 37.1%.
- **agno-agi arcade:** "manuals" (structured game knowledge) transfer across models — seeding GPT-5.6 manuals lifts Gemini-3.7/Grok-4.6/G by 20–40 RHAE. Suggests writing Duck prompts/knowledge from solved games into reusable per-archetype manuals.
- **U4AR RL:** LoRA RL on Qwen3.8-27B through the Duck; reward = level-solve + fewer-generated-tokens bonus; GRPO over (game,level) segments; verbatim replayed history → prefix-cache hits + 4.7× fewer forward passes when packing. Baselines only ~1.6 ARC score — RL from self-play on 5 games does NOT beat serving/memory engineering yet.
- **samrishtt Go-Explore (0.33 LB):** deterministic state-graph explorer; volatility-masked status-bar hashing (mask rows/cols with change-freq > 0.5); win-path caching + re-entry replay on regression (the "regression memory bug" fix); tiered click proposals (component centroids, small/rare objects first). Good seed for an offline/free exploration controller.

## Official infra status

- `arcprize/ARC-AGI-3-Agents`: last real commit 2026-08-03 (URL fix). Superseded for benchmarking by `arcprize/arc-agi-3-benchmarking` (pushed 2026-09-30): Standard vs Provider-Adapter harnesses, `manual_rolling` vs `continuous_conversation`, native compaction (Anthropic 175k threshold, xAI encrypted reasoning replay) — i.e., ARC Prize itself is standardizing on *long native context + compaction*, mirroring the LB leaders.
- `arcprize/ARC-AGI-Community-Leaderboard` (pushed 2026-10-01): new showcase; requires scorecard URL for AGI-3.
- Tufalabs org: duck-harness (90★) + forks of vllm/sglang/transformer-engine/flash-attention updated 2026-09-09 (inference-infra experimentation) + `miles` RL framework (fork) + `tufazip`.

---

## What WE can learn (our stack: Duck fork, Qwen3.8-Flash-Next-NVFP4, LB 4.54 → target 27+)

Our 4.54 sits almost exactly where rellik13 was on Sep 18 (6.42 w/ SGLang) and da-fr's early runs; the crawl doc's diagnosis of a similar 3.40 entry applies to us nearly verbatim: *same solver lineage, radically different serving envelope.* Ranked by expected LB impact:

1. **Memory envelope first, prompts last.** Enable FP8 E4M3 KV cache on our vLLM nightly (lordhansolo proves 1.42M-token pool on NVFP4 + vLLM) and raise history retention to ~45–57k with **large-block hysteresis trimming** (trim 57k→45k once, not 2k/turn; each trim invalidates the prefix). Add cache-occupancy telemetry (their smoking gun: pool 91–97% full + queued requests = raise pool, don't add prompts). If vLLM nightly limits us to ~8 resident seqs, cut admission to that (cache-resident useful state ≫ nominal concurrency).
2. **Prefix/Mamba-state retention.** Keep prefix caching ON, retain end-of-prefill Mamba checkpoints (LRU refresh), image-aware token counting (base64-as-text bug wrecks trim decisions), and re-tokenization-safe history (U4AR's verbatim-history trick improves hits + enables packing later).
3. **Harness fixes bundle (all no-context-cost, rellik13's 14.49 step):** label ACTION7 as `UNDO`; replace "The game is over." with "Level restarted." + diagnosis (budget-bar rule); **remove** the "ignore the border bar" advice (it *is* the action budget); expose mid-action animation frames (+ hint only on transient pixels); keep per-action results faithful. da-fr's UNDO + game-over prompts are copied in `gh_da-fr_WRITEUP.md` §4 verbatim — reuse them.
4. **Solved-level rule memory** (pinned rules from cleared levels; +2.8 for rellik13) — the ONLY text addition that paid across all write-ups. Everything else that adds recurring text (advice, per-action summaries, ledgers, archetype playbooks) is measured NEGATIVE. Our governor/schema-lite addenda should be re-audited against this evidence: keep them additive-only-at-reset, never recurring.
5. **Persistent model-defined Python functions** across calls (da-fr) — free token savings, no risk; pairs with our sandbox.
6. **Priority/UCB scheduling** over one pool (da-fr's P=(A+B)·C formulas or rellik13's UCB+2400s slices; also gives us `priority_pace_reference.json` + `priority_scheduler.py` as copyable code from da-fr's tree).
7. **If we get memory right and stall:** port TWIN-style twin-gating or arc-skill-style prediction-graded actions into the Duck's python tool (they're harness changes, model-agnostic); use theredbluepill's 249 community games for offline practice distribution; write "manuals" from our solved runs (agno's seeding evidence) but only as pinned solved-level memory, not recurring advice.
8. **Variance protocol** (everyone's top lesson): single runs resolve nothing (rellik13: same notebook 5.02/5.50/6.42; 37 practice → 9.98 LB; discussion 743723: identical builds 2.60 vs 1.45). Compare at equal generated tokens per game (their metric), read transcripts, keep our full-length practice runs, gate on public score only (1zuki's rules: offline means diagnose, never promote).

Copyable code right now: `da-fr/arc-agi-3-solution` → `ARC3-Inference/inference/agent/{priority_scheduler.py,noop_repeat_guard.py,runtime_state.py,vision_context.py}`, `inference/utils/{animation.py,frame_diff.py,retained_functions.py,segmentation.py}`, `serving/patches/*`, `ARC3-Inference/CONFIGURATION.md` (43KB, saved). `LohitSiriki/...` → `cells/44_patch_cell_v16_7_m72_one_pool_ucb_scheduler.py`, `cells/42_...solved_level_memory.py`, `cells/33_...context_guard.py`, `cells/29_...mid_action_frames.py`, `cells/19_...action7_fix.py` + `analysis/{sawtooth,tps_by_running}.py` for cache/trim telemetry.

## Files saved in research/milestone2/

Readmes/writeups: `gh_da-fr_arc-agi-3-solution_README.md`, `gh_da-fr_WRITEUP.md`, `gh_da-fr_CONFIGURATION.md`, `gh_LohitSiriki_arc-agi-3-milestone2-solution_README.md`, `gh_LohitSiriki_WRITEUP.md`, `gh_LohitSiriki_analysis_README.md`, `gh_LohitSiriki_tps_by_running.py`, `gh_Tufalabs_duck-harness_README.md`, `gh_Tufalabs_ARC3-Inference_README.md`, `gh_arcprize_arc-agi-3-benchmarking_README.md`, `gh_arcprize_Community-Leaderboard_README.md`, `gh_alexisfox7_PRO-LONG_README.md`, `gh_alexisfox7_PROLONG_arcagi3_README.md`, `gh_pbshgthm_arc-skill_README.md`, `gh_DriesSmit_ARC3-solution_README.md`, `gh_Alexyskoutnev_TWIN-ARC-AGI-3_README.md`, `gh_U4AR_qwen38-arc3-rl_README.md`, `gh_U4AR_RESULTS_REPORT.md`, `gh_samrishtt_arc-agi-3-kaggle-competition_README.md`, `gh_agno-agi_arc-agi-arcade_README.md`, `gh_theredbluepill_arc-interactive_README.md`, `gh_dp-web4_ARC-SAGE_README.md`, `gh_1zuki_milestone2_crawl.md`, `gh_tsitsino_MILESTONE_2.md`.
Structured: `all_repos.json` (115 repos), `searchrepo_*.json` (13 queries), `searchcode_milestone2.json`, `orgs_arcprize.json`, `commits_arcagi3agents.json`, `tree_*.json` (12 trees incl. da-fr + LohitSiriki + Tufalabs 3,607 files), `user_*.json` (jcottaar/dfranzen/Tufalabs/NVARC3 etc).