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Daily bookmarks: relevant news, facts and discoveries.

Engineering

MiMo RL Environment Explorer

Published on September 29, 2026, the MiMo RL Environment Explorer is described as an interactive space to inspect MiMo-V2.6 reinforcement-learning environments and run rollouts.

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Engineering

A 7B model learns continuously from prediction markets

A publication dated September 29, 2026 describes a 7B model that continuously updated its weights using prediction-market data and ran continuous backtests over 390 days. It says the model outperformed several larger models in the same test environment.

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Engineering

OPAL trains linear attention with its own outputs

The approach distills linear attention models in long contexts and, according to the report, fully recovers full-attention retrieval while reaching 83–93% of mathematical reasoning performance with 3 billion tokens.

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Engineering

TIRx Harness explores GPU kernel optimization

Announced on September 29, 2026, TIRx Harness is an open compiler harness for agents to explore, debug, and optimize GPU kernels, with compiler analysis, a catalog, hardware specifications, and remote evaluation.

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Engineering

Vx uses inline MLIR for leading-zero operations

Published on September 29, 2026, the bookmark describes Vx's use of the `mlir!` macro to lower MLIR directly into leading-zero count instructions, avoiding manually maintained routines for different hardware.

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Engineering

Agents optimize kernels for Kimi Delta Attention

Published on September 28, 2026, the post reports that kernel design agents generated and optimized Kimi Delta Attention kernels, reaching up to 2.96× the performance of FlashKDA on B300 with one-tenth the state error.

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Engineering

Matryoshka Attribution ranks components

Published on September 28, 2026, the post describes a technique that ranks neural representations or fine-tuning weight changes by their influence on behavior preserved under soft interventions.

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Engineering

On-policy self-distillation for reasoning in LLMs

Published on September 28, 2026, the material presents Self-Distilled Reasoner, an on-policy self-distillation approach for language models. Its preview describes student-sampled trajectories and dense teacher supervision at token level.

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