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JEPA: Why Predicting in Pixel Space Was the Wrong Goal All Along

May 29, 2026

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13 min read

JEPA: Why Predicting in Pixel Space Was the Wrong Goal All Along

Self-supervised learning has been dominated by two ideas: reconstruct masked pixels (MAE), or force representations of different views to be similar (DINO, BYOL, SimCLR). JEPA (Joint-Embedding Predictive Architecture) rejects both. It predicts abstract representations of masked regions, not pixels. This single architectural choice produces richer semantic features with 10x less compute than MAE and zero hand-crafted augmentations. Yann LeCun has been arguing for this design for decades. The empirical results are now here.

Mohinish S

SnackOnAI Blog

TurboQuant: The Quantization Algorithm That Actually Proves Its Distortion Rate Is Near-Optimal

May 28, 2026

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13 min read

TurboQuant: The Quantization Algorithm That Actually Proves Its Distortion Rate Is Near-Optimal

Every quantization method claims minimal quality loss. TurboQuant (Google Research, ICLR 2026) is among the first to prove it: the distortion rate is within a constant factor of the information-theoretic lower bound. The proof comes with a two-stage algorithm that works online, requires zero per-vector quantization overhead, and directly addresses the KV cache memory bottleneck that limits long-context LLM inference.

Mohinish S

SnackOnAI Blog

MiniMax M2.7: The Model That Ran Its Own RL Experiments and Got 30% Better Without a Human Touching the Code

May 27, 2026

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13 min read

MiniMax M2.7: The Model That Ran Its Own RL Experiments and Got 30% Better Without a Human Touching the Code

MiniMax M2.7 is not a model that was trained by engineers. It is a model that participated in training itself. An internal version of M2.7 ran over 100 autonomous rounds of scaffold optimization, evaluated its own outputs, decided which changes to keep, and achieved a 30% performance improvement on internal benchmarks. This is not a demo. It is the production pipeline that built the model you can use today.

Mohinish S

SnackOnAI Blog

RelBench v2: Four New Databases and What They Reveal About Where Relational Deep Learning Breaks Down

May 26, 2026

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9 min read

RelBench v2: Four New Databases and What They Reveal About Where Relational Deep Learning Breaks Down

RelBench v2 does not just add more databases. It adds databases specifically chosen to stress-test relational deep learning in domains where the pkey-fkey graph hypothesis is hardest to satisfy: high-cardinality sparse interactions, long-tail distributions, and temporal dynamics that defeat simple neighborhood aggregation. The leaderboard results on the four new databases tell a more honest story than the headline benchmark numbers.

Mohinish S

SnackOnAI Blog

RelBench v1: The Benchmark That Forced Honest Evaluation on Relational Deep Learning

May 25, 2026

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9 min read

RelBench v1: The Benchmark That Forced Honest Evaluation on Relational Deep Learning

Every published result on relational database ML before RelBench was incomparable: different temporal splits, different leakage handling, different metrics. RelBench v1 fixed all three simultaneously by making correct temporal evaluation the default behavior, not the careful choice. The benchmark is the infrastructure. The databases are the test suite. The enforced defaults are the contribution.

Mohinish S

SnackOnAI Blog

FST: The Dual-Engine Training Method That Reaches Peak Performance With Three Times Fewer Steps

May 24, 2026

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12 min read

FST: The Dual-Engine Training Method That Reaches Peak Performance With Three Times Fewer Steps

Reinforcement learning trains LLMs by updating parameters. Prompt optimization adapts LLMs by updating context. Everyone picks one. Fast-Slow Training (FST) runs both simultaneously, treating the prompt as fast weights that absorb task-specific information and the parameters as slow weights that preserve general reasoning, reaching higher performance in fewer steps while maintaining the model's ability to keep learning.

Mohinish S

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Snack On AI

JEPA: Why Predicting in Pixel Space Was the Wrong Goal All Along

TurboQuant: The Quantization Algorithm That Actually Proves Its Distortion Rate Is Near-Optimal

MiniMax M2.7: The Model That Ran Its Own RL Experiments and Got 30% Better Without a Human Touching the Code

RelBench v2: Four New Databases and What They Reveal About Where Relational Deep Learning Breaks Down

RelBench v1: The Benchmark That Forced Honest Evaluation on Relational Deep Learning

FST: The Dual-Engine Training Method That Reaches Peak Performance With Three Times Fewer Steps

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