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Research2026-04-21 · source-backed
Microsoft Research showed Qwen3-4B with a "Skeptical-Agent" outperforms 32B models and approaches 235B single-attempt performance. A 50x+ model size compression through inference-time self-refinement. Practical evidence that you can trade model size for inference-time compute on specific tasks.
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ARC-AGI-2 Reasoning Race. Gemini 3.1 Pro hit 77.1% and Claude Opus 4.6 hit 68.8% — both more than doubling their predecessors' scores in a single generation. The entire field moved ~10 points over the prior two years; each model jumped 30-40 points in one release. The top thre...
Farid Zakaria's Self-Executing Linux Format uses binfmt_misc to hand the file to an interpreter that maps rows from a segments table and jumps to the entry point, with the program reading its own file via argv[0]. Symbols, relocations and application data all live in tables in...
He set the 4-byte SQLite application ID at offset 68 to "SELF", decomposed an ELF binary's components into rows across a custom schema, and registered a binfmt_misc handler that hands the file to a self-exec interpreter which queries the tables and runs the program. One file,...
microsoft/flint-chart hit 3,012 stars and 264 HN points on August 1. The thesis: asking an agent to configure scales, axes, spacing and layout directly is where agent-generated charts break, so Flint has agents emit a compact semantic spec and its compiler derives the rest fro...
Test-time training for long-context LLMs is highly sensitive to which spans you train on. Random spans degrade accuracy because most are irrelevant (arXiv:2607.09415). S-TTT has the model first identify relevant evidence passages, then run adaptation only on those, for up to 1...
Agent Lightning v1.0 (arXiv 2608.17528) inverts the standard agentic RL architecture, and the inversion is the whole point. Normally the training engine owns the environment loop. It drives the agent, collects trajectories, computes rewards. Which means your training setup and...
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