Lossfunk Letters

LLMs are blind. But somehow they can see.

How text-only models develop human-like maps of color, pitch, emotion, and taste, without ever experiencing any of them.

Jun 30 • Lossfunk

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The Metaprogramming Reflex: How the Best Coding Agents Survive Languages They've Never Seen

The agentic follow-up to EsoLang-Bench, now with tools, a workspace, and one strategy that separates the strong agents from the rest.

Jun 18 • Aman Sharma and Lossfunk

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May 2026

How to make progress on metaphysical puzzles in AI?

New paper from Lossfunk

May 21 • Paras Chopra

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Beyond Reward Design: Discovering RL Interfaces with LLMs

Jointly evolving RL observations and rewards with evolutionary LLM-guided search

May 11 • Akshat Singh Jaswal

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April 2026

Attributes of a great research question

Sharing our learnings from iterating on what good science is

Apr 29 • Paras Chopra

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Can AI models be conscious?

How can we tell?

Apr 21 • Paras Chopra

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Does spatial context make VLMs better game-playing agents?

And why noisy perception can make them worse.

Apr 2 • Ashish Baghel

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March 2026

The Reasoning Illusion: Why LLMs Fail When the Training Data Runs Out

EsoLang-Bench — accepted to the Logical Reasoning and ICBINB workshops at ICLR 2026

Mar 19 • Aman Sharma

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Making Large Language Models Speak Tulu: Structured Prompting for an Extremely Low-Resource Language

We use a structured 5-layer prompt to get GPT, Gemini and Llama to generate grammatically correct Tulu, a low-resource Dravidian language, with no…

Mar 10 • Prathamesh Devadiga

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Can AI Actually Find Security Vulnerabilities?

We measured AI’s ability to discover new security flaws in the wild

Mar 3 • Ashish Baghel, Akshat Singh Jaswal, and Paras Chopra

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February 2026

Are You Getting The Best Version of Your LLM?

We investigate how language and culture are entangled in LLMs

Feb 18 • Shourya

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Teaching morality to transformers

We train a custom transformers architecture on MIT Moral Machine data and run interpretability experiments on it

Feb 5 • Mayank Goel

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