gemma-4-31b-it-qat-w4a16-ct is the only allowed model — every agent and subagent must use it. You get optional per-agent PEFT LoRA adapters, prompts, sub-agents, and sandboxed skills. Scoring is SWE-Bench-style PASS/FAIL (apply patch, run validation tests, % passing), with a 12-hour total budget including sandbox setup. Here's what I keep coming back to: the harness hands you a code graph and embeddings. The provided tools include get_code_neighbors, search_similar_code (cosine over pre-computed embeddings), and get_code_subgraph. The paper track explicitly calls out "Graph Reasoning" and "Code Comprehension", and says a graph+embedding dataset was released. So the organizers are strongly hinting the intended edge is graph-based repo comprehension. Which makes the debate concrete: Everyone gets the same graph tools. So is the differentiator the model actually using them well (→ post-training / LoRA), or the agent's orchestration of them (→ harness)? Is SWE-Bench PASS/FAIL a fair proxy for a reliable dev agent, or does it reward test-gaming over real comprehension? With a single base model and a 12h budget, what's the realistic VRAM/latency budget for a repo-sized context? About 4000+ entrants and 600+ submissions already in at the time of this writing, closes Dec 2. Anyone here actually competing — are you betting on the weights or the harness, and do you think the graph tools are the intended path or a red herring?   submitted by   /u/politefella0 [link]   [comments]