Understanding models' reasons: a research agenda on AI behavior
brwilder · x · 2026-08-21
Bryan Wilder proposes a research agenda focused on explaining why models behave in certain ways, distinguishing between developmental explanations (training data, reward functions) and reason-based explanations (beliefs, goals). The article argues that understanding the interaction between these layers is critical for AI alignment and calls for precise experimental designs to disentangle different motivations for action.
More from Research
- Trending HF dataset: Qwen/GLM/Kimi multi-model distillation mix — lhoestq · 2026-08-21
- Legal model training shifts from SFT to LLM-judge RL — ivan_bezdomny · 2026-08-21
- Interdisciplinary project launching on multi-agent alignment — sethlazar · 2026-08-21
- ZAI may have mastered RL environment generation with GLM — scaling01 · 2026-08-21
- Cisco Open Sources Antares Security Model, 3B Matches GPT-5.5 — aminkarbasi · 2026-08-21
- Measuring research progress by the ability to ask increasingly good questions — RichardMCNgo · 2026-08-21