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@@ -128,6 +128,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
 |---|---|
+| [Swap the agent harness, not the model: a +1/12 persistence lever](reports/omp-harness-as-variable.md) · [chart](reports/omp-harness-as-variable.png) | How much of an agentic-coding score is the model and how much is the harness wrapped around it? Held the model fixed (Qwen3.6-27B-Q6_K, one local llama-server on a 5090, think-off, temp 0) and swapped only the agent scaffold, graded on 12 SWE-bench Verified bugs with the official harness. The rig-native tool loop (40-step budget) resolves 8/12; omp v16.1.14 (a deps-free CLI agent, 450s budget, same model and `:8090` endpoint) resolves 9/12 — a strict superset, the lone delta being sphinx-8621. The mechanism is persistence, not reasoning: on the 4 hard bugs the native loop committed no patch (gave up) 3 times, omp once; omp lands patches where native quits, and one of those passed. Both harnesses miss the same 3 bugs (seaborn-3187, requests-1921, pylint-7080) — same model, same ceiling, so the scaffold only moves the give-up rate. Empty-patch rate is the give-up tell, here separating two harnesses on a fixed model. Honest limits: n=12 single seed, so the +1 is inside the noise (the signal is the direction plus the mechanism); the budgets differ by construction (steps vs wall-clock), which is the point — a harness is prompt plus tools plus stopping policy, bundled. The inverse of the Ornith-1.0 claim the rig tests next (RL that bakes the scaffold into training). |
 | [Qwen-AgentWorld's zero-fine-tune transfer doesn't reproduce on a 5090](reports/agentworld-lwm-transfer.md) · [chart](reports/agentworld-lwm-transfer.png) | Qwen-AgentWorld (arXiv 2606.24597) trains a language world model to predict environment transitions and claims the warm-up transfers to agentic tasks with zero agent fine-tuning, +3.4-12.8%. Tested the released LWM-warmed 35B-A3B against its own base Qwen3.5-35B-A3B in a think-OFF/temp-0 controlled A/B on one RTX 5090. The synthetic agentic board is flat (97.5 = 97.5, a saturated axis that hides differences); the real SWE-bench Verified anchor (30 bugs, official harness) goes 14/30 vs the base's 16/30 — a reshuffle rather than a collapse (11 solved by both, 3 AgentWorld-only, 5 base-only), net -2 with more give-ups (13 empty patches vs 10, mean 33/40 steps: it explores more and commits fewer fixes). The claimed +3.4-12.8% transfer lands at 0% on synthetic and -12.5% on real coding. A scoped null: think-OFF to match the base's banked number, so it doesn't refute a think-ON gain (that A/B is the queued falsification leg), and it tests the SWE-coding slice of a seven-domain claim. |
 | [FP4 on a consumer 5090: the Blackwell headline loses to plain int4](reports/fp4-consumer-blackwell.md) · [chart](reports/fp4-consumer-blackwell.png) | FP4 is the Blackwell selling point — benchmarked on one RTX 5090 (sm_120) against the quants you'd actually run, Qwen3-14B in vLLM 0.21. Two findings that compound. (1) NVFP4 is the only quant that won't run out of the box: AWQ/FP8 use prebuilt Marlin kernels, but NVFP4 makes FlashInfer JIT-compile native sm_120 FP4 cutlass kernels at load — needing ninja on PATH + a real CUDA toolkit + the correct CUDA_HOME (the default /usr/local/cuda-13.0 doesn't exist on the box) + a flashinfer-cache clear. (2) Once native FP4 is genuinely running (declared modelopt_fp4, not a Marlin dequant fallback), it's still slower than AWQ int4 at every batch: batch-1 decode AWQ 150 vs NVFP4 100 (0.66x) vs FP8 90; batch-32 AWQ 3937 vs NVFP4 3321. The 4-6x FP4 numbers are B200 tensor-core throughput; on consumer sm_120 a mature int4-Marlin kernel wins. bf16-14B doesn't fit 32GB (no KV room). (3) The academic real-FP4 path (QuTLASS MXFP4, arXiv 2509.23202), built from source on sm_120a (CUTLASS submodule, torch 2.8/cu128, forced -ccbin g++-14 past the GCC-15/CUDA-12.8 guard): its 4x is real at the GEMM — the MXFP4 matmul crosses 4x by batch 128 and peaks ~6x over bf16 on Qwen3-8B — but end-to-end decode runs 3-4x slower than bf16 (20.4 vs 78.6 tok/s at batch 1) and uses ~2x the VRAM, because decode is memory-bound and pays a fixed per-layer rotation+quant tax the tiny matmul can't amortise. Verdict: use AWQ int4 for serving, skip NVFP4 on consumer Blackwell; QuTLASS MXFP4 only pays off for compute-bound high-batch/prefill work, not single-stream serving. |
 | [One-Shot EM doesn't reproduce on a 5090: entropy fell, accuracy didn't](reports/one-shot-em.md) · [chart](reports/one-shot-em.png) | One-Shot Entropy Minimization (arXiv 2505.20282) claims +24.7 on Qwen2.5-Math-7B from ONE unlabeled example in ~10 steps, no rewards. The full-param recipe OOMs on 32GB (14GB weights + 14GB bf16 grads), so the consumer-feasible version is LoRA (batch 16). Measured greedy pass@1 with the authors' grader: base reproduces the paper (MATH500 53.4 vs 53.0), but EM adds +2.0 at its peak step then collapses back by step 15, and AMC23 goes −2.5 (claim was +25.8 / +26.2). The keeper is the mechanism: the entropy objective trained fine (mean per-token entropy 0.098→0.035) while accuracy stayed flat — distribution-sharpening, not learning, in the paper's own words. Same base as Spurious Rewards (+21 on MATH500 from random rewards). Honest limits: full-param didn't fit, so this isn't a refutation of the multi-GPU number; the few-shot format control backfired on Qwen-Math's native zero-shot CoT. |