Six tracks, and a gate.

4–8 contributors

SAAF — aquatic floatables

Waste entering the ocean is almost never measured. Clean-ups get planned on impressions — a photograph, a volunteer's estimate, a small area counted by hand and extrapolated to a coastline — and without a baseline nobody can say whether the problem is growing or shrinking, or whether last year's effort did anything. Drone imagery and detection models are now cheap enough that a survey needs a consumer 4K drone rather than a research budget, which is what makes a repeatable count possible at all. This is also the clearest test of the whole IIDA argument — a problem too long for a startup to fund, too applied for a university to prioritise, and worth a great deal to whoever measures it first.

Kill criteria. If the strict score stays where it is and mass estimates cannot be stated with honest error bars, this stops being measurement and becomes a counting exercise with a decimal point, and it closes. It also closes if surveys are flown but nobody — municipality, NGO or donor — changes a decision because of one. A number nobody acts on is not an outcome.

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6–10 contributors

World Models and JEPA

Joint Embedding Predictive Architectures point past purely generative AI toward sample-efficient learning, grounded reasoning and world models for embodied agents. For a research programme with limited compute, an architecture family whose central claim is sample efficiency is not a fashionable choice — it is the practical one.

Kill criteria. If replication does not reach published baselines within two quarters, the track narrows to the Indic low-resource question only, or closes.

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6–10 contributors

Tokenomics — Valuing AI Token Usage

As language models become infrastructure, the unit economics of inference remain poorly understood. Pricing, fair use and per-token value attribution are open problems with genuine policy weight — and they are a live blocker on enterprise adoption, which makes them squarely an SME problem.

Kill criteria. If the attribution toolkit cannot reproduce a finance team's own cost model on a real workload, the framework is wrong and the track closes rather than being defended.

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4–8 contributors

Maharashtra AI Policy

Maharashtra is writing AI policy while the industrial base it applies to — Pune's manufacturing and services SMEs — has almost no representation in the drafting. Policy written without that input tends to fund capability that already exists in large firms and misses the constraints an SME actually faces. The window for evidence to be useful is while a document is being drafted, not after it is published.

Kill criteria. If the policy process closes without our submission being read, or the work becomes advocacy without evidence behind it, this closes rather than continuing as a relationship-building exercise. Influence that cannot be pointed at is not a deliverable.

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