The Fifth Elephant 2026 Winter Edition

Call for Data Problems and Proposals

Alosh Denny

Alosh Denny

@aoxo

Offering Help On Evaluation Design, Model Compression And Killing Your Own Results

Submitted Sep 29, 2026

How I want to help

  • rewrite vague problem statements
  • suggest matches between people and problems
  • provide subject-matter critique

What I can help with

  • ideas
  • experience
  • technique
  • critique

Where I am useful

Three areas where I have enough scar tissue to be worth talking to.

Evaluation design. Specifically the failure where a metric rewards a system that has stopped working, and the more general question of what liveness number belongs beside a capability number. I found this the hard way on a benchmark that scored a broken model at 100 percent.

Model compression and numerical precision. What it costs, when it is free, why it sometimes fails at sixteen bits when it worked at four, and how to check your own checkpoint before committing to anything.

Designing a sweep that can falsify your own claim. I have retired two of my own published results this year, and I am useful for the specific job of turning “I noticed X” into an experiment that could actually show X is false.

I am also happy to do the unglamorous version of this: taking someone’s rough problem card and asking the four questions that make it answerable.

Topic tags

evaluation, model compression, reproducibility

Availability before the event

Yes, available for light-touch matching and facilitation before the conference, over email or a call. Bangalore is a short flight from Kochi, so in person on both days is fine too.

Bio

Alosh Denny, creator of Superfloat, an open source number format, accelerator and compiler stack for running models without floating point. Head of AI at Aleddo Technologies and LLMOps lead at Intensors, Kerala based, seven peer reviewed publications across IEEE and Springer, IndiaFOSS speaker. He has run 878 training runs to answer one question, and the most useful thing he learned was how to design the experiment that proves himself wrong.

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