DeepSight provides an all-in-one LM safety toolkit, a separate paper detects jailbreaks from internal LLM representations, and Mozilla evaluates multilingual guardrails — three independent efforts to systematize LLM safety evaluation and defense.

DeepGen 1.0 targets image generation and editing below 10B parameters to reduce training/deployment cost; FireRed-Image-Edit from Rednote claims open-source SOTA for image editing — both push multimodal editing into smaller, more deployable models.

Composition-RL addresses uninformative examples in RLVR prompt sets via composable verifiable prompts, a detection method identifies RLVR training data via structural convergence, and Native Reasoning Models propose training on unverifiable data to escape RLVR's reliance on verifiable-only rewards.

Three papers independently identify RLVR's dependence on verifiable-only data as a fundamental constraint — next bottleneck is whether reward signals from unverifiable domains can train reasoning without reward hacking.

Blog post with 810 HN points shows improving 15 LLMs at coding by only changing the harness (not the model), while GPT-5.3-Codex-Spark launches via Cerebras partnership for real-time coding — both highlight that evaluation scaffolding and serving infrastructure matter as much as model weights.

810-point HN post demonstrates coding benchmark gains from harness changes alone — next bottleneck is standardizing evaluation harnesses so benchmark scores reflect model capability rather than scaffolding quality.

One paper directly addresses the reference-policy mismatch in DPO that causes distribution drift; P-GenRM proposes personalized generative reward models with test-time user scaling — both target the core DPO limitation that the reference policy diverges from the learned policy during training.

MiniCPM-SALA hybridizes sparse and linear attention for efficient long-context modeling; a new library implements multiple linear RNN architectures with accelerated kernels — both target the memory/compute wall of full quadratic attention at long sequences.

MiniCPM-SALA and a multi-architecture linear RNN library both target sub-quadratic long-context modeling — next bottleneck is whether hybrid approaches can match full attention quality on retrieval-heavy tasks beyond 128K tokens.

Open-source multi-agent orchestrator coordinates 20+ Claude Code agents for long-running tasks, HN discussion on agent orchestrators for coding, and Moltis offers self-extending AI assistant with memory — all address single-agent failure on sustained complex work.