AI news

The complete reasoning-from-scratch video series is now up (and linked) here: magazine.sebastianraschka.com/p… (Taking a short break but will be back with LLM model and research updates in November!)

CoEvo-RSI: Aligning evolving harnesses with model capabilities A recursive self-improving framework that uses bidirectional historical inheritance to solve brain-body mismatch, letting a 9B model outperform much larger frontier models across diverse agent tasks.

When should the cloud execute for the edge agent, and when should it just advise? We present a systematic study of edge-cloud collaboration in long-horizon agent execution. Using EC²-Bench, we evaluate four collaboration strategies across 128 executable tasks, jointly measuring task completion, resource cost, and privacy exposure. Here are three key findings: 1️⃣ Small cloud budget, big gains.(cost-efficiency). With Qwen3.5-9B on-device and GLM-5.3 in the cloud, edge-only scores 39.12; the best collaboration reaches 60.46 — about 73% of cloud-only (82.70) — at only $8.73, roughly 15% of cloud-only's $56.42. 2️⃣ Performance converges, privacy doesn't. On Qwen3.7-Plus, best collaboration hits 76.84 vs 82.70 cloud-only; strategy gaps shrink to just 3.67 completion points, yet privacy exposure spans 66.23 percentage points — and the best-performing strategy leaks the most. 3️⃣ The mode flips with edge capability. Weak edges do best with an external router that lets the cloud execute (2B…

JEPA learns what to keep from what it sees, and uses that to predict what comes next. But what about the details it can’t predict? Wouldn’t it be easier for the model to drop them? Look at this example Imagine a traffic light that changes color randomly. Its next color is unpredictable, but its current color helps predict whether a cyclist will stop or move. If we drop the light, the cyclist becomes harder to predict. ▪️ A recent research call this set-level view "predictive closure:" A feature’s value depends on its relationships with the other features the model represents. Keeping the light makes the cyclist easier to predict, but adds an unpredictable color to the model’s targets. Dropping it removes that target, but loses a useful clue. The researchers separate these two costs: 1. Aleatoric uncertainty: what remains uncertain even with the full current observation, like the light’s next random color. 2. Predictive leakage: useful information available in the observation bu…

Apple and UCLA present OpenVLHarness A training-free harness that gives multimodal LLMs a capability layer and persistent multimodal memory, boosting average scores by 9.7-12.6 points across 22 benchmarks.

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Can LLMs improve their own harness? Self-improving harnesses are a hot topic. And one interesting question is whether LLMs can generate and improve them on their own. They find that two full rewrites of the harness raise held-out scores by only 2.80 points, and the 95% interval crosses zero. Each frontier model writes a complete harness from a fixed coding bootstrap, plays five development games through it, and then rewrites its whole codebase twice. Every version is scored on 20 games that never influence the rewrites. Across 27 lineages, first harnesses range from zero to near-ceiling. The revisions add very little. Gains on the development games overstate the held-out gains, and revisions that cut inference cost often lower the score. Human-steered systems still score higher on the same games, so better designs exist. The models just do not find them reliably on their own yet. arxiv.org/abs/2610.30499 Chat with Paper: academy.dair.ai/papers/can-llms…

Nice work on using skills more effectively for coding agents. (bookmark it) In this work, researchers treat skill granularity as a runtime decision. For each task, the agent can use primitive tool calls, a macro skill, or a macro skill that falls back to primitive steps when it fails. Fixed macro execution is efficient when the skill fits and brittle when it does not. The controller picks the level from task structure, execution state, and earlier macro failures, and a finite-state executor handles the fallback as an explicit transition instead of an informal retry. In a 12-task routing study, a learned router reaches 100% success where a hand-written heuristic fails. With macro-to-micro fallback, the agent recovers controlled macro failures that fixed granularity leaves unresolved. arxiv.org/abs/2610.31461 Chat with Paper: academy.dair.ai/papers/adaptive…

LEV: think deeper without a single extra token A decision model that loops its layers, scoring options after each pass. One checkpoint works at any depth. On JevBench, Lev-2.6B goes from 63.2% to 76.2% accuracy as depth increases from 1 to 4.

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DeepSeek raised the bar, and SGLang pushed it even further 🧡 lmsys.org/blog/2026-10-2… Read our blog to learn more about the performance results, engineering optimizations, and how SGLang brings it all together in a production-ready serving stack! x.com/sgl_project/st…