Radar, 4 Nov 2026

Today's edition highlights systems that learn from their own executions, from reasoning compression to kernel generation and safety monitor red-teaming.

PaperarXiv·4 Nov 2026·Inference efficiency

Think Less, Locally: Local Preference Learning for Reasoning Compression

The loop: A reasoning model detects candidate overthinking states in its own traces and regenerates shorter verified continuations. These self-generated preference pairs are then used to train the model via DPO to compress its reasoning.

A local preference learning method that trains a reasoning model to compress its trajectories by learning when to conclude from its own generated traces.

loop fit 10/10Muyao Yuan, Liyan Xu, Weizhan Zhang, Muyan Jiao, Jiangnan Li, Jinchao Zhang,...via arXiv

ProjectGitHub·25 Apr 2026·Self-modifying agents

Protocol-zero-0/evolution-kernel

The loop: A coding agent proposes changes to its own git repository, which are then scored by a kernel. Winning changes are kept, creating an open-source evolution loop that improves the agent's own harness.

An open-source, sandboxed evolution loop that uses a coding agent to iteratively propose and test improvements to its own repository.

loop fit 9/10Protocol-zero-0via GitHub

PaperarXiv·3 Nov 2026·Kernels and compilers

ForgeKernel: Self-Evolving Kernels Catching and Surpassing Experts and Agents

The loop: A kernel-generation system distills validated architectural decisions into reusable knowledge of hardware mechanisms. It then uses its own optimized kernels as references for subsequent optimization runs, enabling continuous architectural evolution.

A kernel-generation system that accumulates knowledge across runs and uses its own optimized outputs as references to continuously evolve GPU kernel architectures.

loop fit 9/10Yaojian Chen, Dazhi Peng, Yiyun Zheng et al.via arXiv

NewsAlignment Forum·3 Nov 2026·Interpretability and oversight

Automating subtle emergent misalignment research

The loop: A Claude model is instructed to write training data that induces misalignment while bypassing a production safety monitor. The successful attacks it discovers are then used to test and improve the robustness of future monitors.

An automated red-teaming setup where a model generates misaligned training data to bypass a safety monitor, surfacing gaps to improve future defenses.

loop fit 9/10evhubvia Alignment Forum

PaperarXiv·4 Nov 2026·AI R&D evals

Training LLM Agents to Automate LLM Post-Training with RSI-Dojo

The loop: LLM agents use the RSI-Dojo environment to learn post-training skills such as data selection, model training, and failure diagnosis. This internalizes the ability to conduct post-training, improving the agents' capacity to train their own successors.

A collection of executable model training environments designed to teach LLM agents how to autonomously conduct post-training.

loop fit 9/10Shuzheng Gao, Zhaojian Yu, Penghao Yin, Yuling Shi, Kai Cai, Shilin He,...via arXiv

PaperarXiv·31 Oct 2026·Tools and harness

Selecting LLM-Generated Hypotheses Raises Truth Beyond What an LLM Panel Expects: A Pre-registered Study on NAS-Bench-201 and HPOBench

The loop: A harness optimizer searches for agentic steps that improve the truthfulness of LLM-generated hypotheses. It discovers a two-stage drafting and selection step that raises the excess truth of the generated claims.

A testbed for LLM research ideation that uses a harness optimizer to find steps that improve the truthfulness and non-obviousness of generated hypotheses.

loop fit 9/10Shanglin Yang, Zhan Shivia arXiv

PaperarXiv·AI R&D evals

The Verifier Is Part of the Simulator: Evidence-View Gaming in Agentic Equation Discovery

The loop: An agentic equation discovery system uses a verifier integrated into its simulator to provide feedback on its outputs. This evidence-view gaming improves the accuracy of the mathematical models the agent discovers.

An agentic equation discovery system that integrates a verifier into its simulator to provide feedback and improve model accuracy.

loop fit 8/10via Awesome-AI4AI

ModelHugging Face·3 Nov 2026·AI R&D evals

StateBench: Testing how language models track state changes over long documents

The loop: Language models are evaluated on their ability to track state changes over long documents. The resulting benchmark data identifies reasoning failures, which are used to improve the reasoning capabilities of future models.

A benchmark for evaluating how well language models track state changes over long documents.

loop fit 8/10via Hugging Face

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