| # RecallTrace — Pitch Package |
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| ## Submission Title |
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| **RecallTrace: Causal Inference Under Adversarial Self-Play** |
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| ## Three-Minute Pitch Script |
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| > Timed for spoken delivery. ~150 words per minute. |
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| ### [0:00–0:15] Hook |
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| In 2023, a single contaminated ingredient triggered a recall across four countries. Forty million dollars in losses. The root cause took investigators eleven weeks to find — because the contamination had been relabeled, mixed into safe batches, and shipped through six intermediary warehouses before anyone noticed. |
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| RecallTrace asks a simple question: can an RL agent solve that problem in four steps instead of eleven weeks? |
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| ### [0:15–0:40] What RecallTrace Is |
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| RecallTrace is a causal inference benchmark, not a logistics simulator. The agent isn't optimizing delivery routes. It's investigating a contamination event inside a partially observable graph where 30 to 50 percent of the edges are hidden. |
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| Each episode, the environment generates a unique graph — warehouses, distributors, retailers — with one contaminated lot and one hidden intervention. The agent has five tools: inspect a node, trace a lot's lineage, cross-reference origins, quarantine inventory, and finalize. It sees partial information. It has to figure out which hidden causal intervention — a lot relabeling, a mixing event, or a record deletion — produced the contamination pattern it observes. |
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| This is causal reasoning under partial observability with a real-world framing. That's Theme 3.1. |
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| ### [0:40–1:10] The Self-Play Upgrade |
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| Here's where it gets interesting. We added a second agent — an Adversary. |
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| The Adversary's job is to choose *which* intervention to apply and *where* in the graph to apply it, trying to make the Investigator fail. The Investigator gets rewarded for finding contamination. The Adversary gets rewarded when the Investigator misses it. |
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| They train together. Two hundred episodes. The Adversary discovers on its own that mixing events placed at high-degree crossdock nodes are the hardest to detect. The Investigator discovers on its own that cross-referencing shared lot origins before quarantining eliminates false positives. Neither agent was told these strategies. They emerged from competition. |
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| This is recursive skill amplification — Theme 4's exact language — running inside a world-modeling environment. The benchmark doesn't just test the agent. The benchmark teaches itself to be harder. |
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| ### [1:10–1:45] Demo Moment |
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| Let me show you what the learning actually looks like. |
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| *[Show before_after_demo.png]* |
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| Left panel — Episode 5, untrained agent. It visits seven nodes. It quarantines six of them — including four safe nodes. Belief confidence at quarantine: 0.51 average. It's spraying and praying. F1 score: 0.28. It cannot identify the intervention type. |
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| Right panel — Episode 195, trained agent. It visits four nodes. It quarantines exactly two — the two that are actually contaminated. Belief confidence: 0.89 and 0.87. It stops investigating when P-contaminated crosses 0.85. F1 score: 0.81. It correctly identifies the intervention as a mixing event *before* it quarantines. |
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| The agent went from guessing to reasoning. That's not a metric improvement. That's a behavior change. You can see it without reading a single line of code. |
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| ### [1:45–2:15] Results |
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| *[Show selfplay_training.png]* |
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| F1 score goes from 0.24 to 0.79 over 200 episodes. Nodes quarantined drops from 8.3 per episode to 3.1. Steps to finalize drops from 25 to 11. The adversary's reward flips from positive — it was winning — to negative — the investigator caught up. |
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| Both agents are improving simultaneously. The adversary gets better at hiding. The investigator gets better at finding. The F1 never hits 1.0 because the adversary keeps the problem hard. This is what co-evolutionary training looks like in practice. |
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| The entire loop runs in under one second on CPU. No GPU required. A judge can clone the repo, run `python run_selfplay.py`, and see these plots in sixty seconds. |
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| ### [2:15–2:45] Why This Matters |
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| RecallTrace is not just a benchmark environment. It is a benchmark that evolves. |
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| Every domain where a hidden causal intervention creates an observable pattern under partial information — pharmaceutical contamination, financial fraud, biosecurity, network intrusion — can use this framework. You swap the graph topology, you swap the intervention types, and you have a new self-play benchmark for causal reasoning. |
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| We're not submitting an environment. We're submitting an environment design pattern where the curriculum writes itself. |
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| ### [2:45–3:00] Close |
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| We built an agent that learns to reason causally — and an adversary that forces it to keep getting better. The Investigator doesn't just find contamination. It identifies the intervention type, calibrates its confidence, and stops when it's certain. That's not tool use. That's causal inference. And with self-play, it's causal inference that improves recursively. |
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| RecallTrace. Thank you. |
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| ## Five Judge Q&A Answers |
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| ### "How is this different from graph traversal?" |
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| Graph traversal finds *connected* nodes. RecallTrace requires finding *causally responsible* nodes — the difference is that edges are hidden and interventions change the evidence. The agent sees a contamination pattern and has to infer which hidden causal mechanism produced it. A BFS will find all reachable nodes. Our agent has to figure out that a mixing event at crossdock 3 is why Lot A shows partial contamination at five locations — and quarantine only the two locations with actual unsafe inventory. That's abductive reasoning, not traversal. |
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| ### "Can the agent game the reward?" |
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| We designed against this specifically. The reward has three opposing components: +2.0 per correct quarantine, -1.5 per false quarantine, and -0.05 per step. An agent that quarantines everything gets punished by the precision penalty. An agent that quarantines nothing gets zero reward. The calibration bonus — +0.3 if belief exceeds 0.8 before quarantine — means you can't game it by just quarantining high-degree nodes. You have to actually build a belief state and act on it. Our early agent tried the spray-and-pray strategy. F1: 0.28. It learned to stop doing that. |
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| ### "What does the adversary actually do that a static curriculum can't?" |
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| A static curriculum presents interventions in a fixed order — easy, then hard. The adversary *discovers* what's hard. In our runs, the adversary independently converges on record deletion at downstream nodes as the hardest placement — because it removes evidence at the exact nodes the investigator checks first. No human designed that curriculum. The adversary found it by tracking which placements caused the lowest investigator F1 and shifting its sampling distribution toward those cells. A static curriculum would need a human to pre-rank difficulty. The adversary automates that ranking and updates it as the investigator adapts. |
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| ### "Why is this Theme 3.1 and not just Theme 4?" |
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| Theme 3.1 is about building and using world models for decision-making. Our Investigator maintains an explicit belief state — P(contaminated) per node, updated after every tool call. It reasons about hidden edges in the contamination propagation graph. It performs causal inference: given this observation pattern, what hidden intervention is most likely? That's world modeling. |
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| Theme 4 — self-play and recursive skill amplification — is the *training method*. The adversary makes the world model problems harder. The investigator improves its world model to solve them. Both themes are load-bearing. Remove the world model and you have a toy game. Remove the self-play and you have a static benchmark. Together, the benchmark evolves with the agent. |
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| ### "How quickly does this train and can a judge reproduce it?" |
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| Two hundred episodes in under one second on CPU. No GPU. No external RL libraries — we use numpy for the score table and matplotlib for plots. Clone the repo, `pip install` the requirements, run `python run_selfplay.py`. You'll see the training log in your terminal and three publication-quality plots in the `plots/` directory within sixty seconds. We verified this cold-start on a clean environment. It works. |
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| ## HuggingFace Mini-Blog Opening |
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| **When a contaminated lot enters a propagation network, investigators face a causal inference problem: which hidden intervention — a relabeling, a mixing event, or a record deletion — produced the contamination pattern they observe?** RecallTrace is an OpenEnv-compliant benchmark where an RL agent investigates procedurally generated contamination graphs under partial observability, using tool calls to inspect nodes, trace lot lineages, and quarantine inventory. The core upgrade: we added adversarial self-play. An Adversary agent chooses where to hide contamination; an Investigator agent learns to find it. Over 200 episodes of co-evolution, the Investigator's F1 rises from 0.24 to 0.79, quarantine precision improves 3x, and the agent shifts from spray-and-pray quarantining to belief-calibrated causal reasoning — correctly identifying intervention types before acting. RecallTrace demonstrates that any domain with hidden causal interventions under partial observability can benefit from self-play benchmarks where the curriculum writes itself. |
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| ## Theme Alignment Summary |
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| | Theme | How RecallTrace Hits It | Strength | |
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| | **3.1 — World Modeling** | Belief state tracking, causal graph inference, hidden-edge reasoning | **Primary** | |
| | **4 — Self-Play / Recursive Skill Amplification** | Adversary discovers hard placements, Investigator adapts, both improve | **Primary** | |
| | **1 — Multi-Agent Competition** | Two-agent competitive co-evolution in shared environment | **Bonus** | |
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| ## One-Pager Positioning |
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| > RecallTrace is the only submission that implements **recursive skill amplification** (Theme 4) **inside a world-modeling environment** (Theme 3.1) with a working self-play loop that produces visible, measurable behavior change in under sixty seconds on CPU. |
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| The benchmark doesn't just test agents. It teaches itself to be harder. The adversary finds what's difficult. The investigator learns to overcome it. The environment evolves. That's what makes this submission legendary. |
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