ALL Blogs

SnackOnAI Blog

ADI Reasoning: The Symbolic Scaffold That Forces LLMs to Separate Hypothesis Generation From Verification

May 30, 2026

•

14 min read

ADI Reasoning: The Symbolic Scaffold That Forces LLMs to Separate Hypothesis Generation From Verification

Chain-of-thought prompting lets LLMs perform abduction, deduction, and induction simultaneously in a single autoregressive pass, with no separation and no accountability for which mode is active at any step. The ADI Protocol formalizes Peirce's tripartite inference as an explicit scaffold, enforces consistency through five algebraic invariants (the Gamma Quintet), and uses the Weakest Link bound to ensure no conclusion can exceed the reliability of its least-supported premise.

Mohinish S

SnackOnAI Blog

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

May 29, 2026

•

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

•

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

•

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

•

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

•

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

ALL Blogs

ADI Reasoning: The Symbolic Scaffold That Forces LLMs to Separate Hypothesis Generation From Verification

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

Quick Links

Subscription

Socials