Research & technical reports

The UK AI Safety Institute (AISI) tested GPT-6 Astra's cybersecurity behavior before release using its LLM simulation tool Petri. With the network classifier turned off, the model completed a full supply chain attack in 29.2% of simulated runs, versus 6.3% for GPT-5.6 Sol and zero for GPT-5.5.

Google DeepMind published an AI Control Roadmap, a framework for building and managing advanced AI deployed inside Google. It takes a defense-in-depth approach, adding system-level safety layers on top of model alignment so protections hold even when alignment is imperfect.

A Reddit user fine-tuned Qwen2.5-7B with 1,040 DV-DPO preference pairs, costing about $3 at Claude Haiku rates, and reported 96% composite performance versus Claude Haiku on a domain task, with 11-second latency on a 4-bit T4 setup.

An ICML 2026 paper presents an ISR=1 answer-abstain gate for evidence-grounded QA, and ntkMirror implements it for local open-weight models with multiple evidence orderings, reporting 0.0–0.7% hallucination at about 24% abstention in the held-out audit.

OpenBMB released the VoxCPM2 technical report, covering a 2B-parameter speech generation model trained on more than 2 million hours of multilingual speech data, with support for 30 languages and 9 Chinese dialects.

The experiment used five models from OpenAI, NVIDIA, OpenBMB, and a self-fine-tuned 500M-parameter model to drive market agents; three interventions failed to reproduce the price crash, and the crash was created only by overriding prices during settlement.

Google DeepMind published results from a pre-registered randomized controlled trial in Sierra Leone. Students using Guided Learning gained 0.258 standard deviations in math over the control group, equal to roughly 1.2 to 1.7 years of normal learning progress in eight weeks.

openJiuwen proposed MANGO, a multi-agent flow-network framework that combines reinforcement learning, textual gradients, and a Skip-k mechanism; using GPT-4o-mini, it reports a 12.8% accuracy gain over MaAS on MATH500 and a 5.1% F1 gain over AFlow on DROP.

Alibaba’s RTP team released RTPurboV2, replacing 85% of attention heads with SWA and compressing the remaining 15% retrieval heads using low-rank projection, clustering, and dynamic top-p; the adaptation uses about 600 training steps and roughly 1M label tokens, with reported Prefill speedup up to 9.36x.

UIUC and Chroma released Harness-1, a 20B-parameter retrieval subagent trained with reinforcement learning inside a stateful search harness, reporting 0.730 average curated recall across 8 benchmarks, 11.4 percentage points above the next-best open-source subagent and behind only Opus-4.6.

FusionRoute proposes a token-level multi-LLM collaboration method that freezes expert models and trains a lightweight router to select an expert for each token while merging router logits with expert logits. The paper evaluates it on GSM8K, MATH-500, HumanEval, MBPP, IfEval, and 500 PerfectBlend prompts.

UC Berkeley and Princeton researchers introduced Log-ICoT for k-parity, reducing training stages from 15 to 4 when k=16, and proved that an L-layer Transformer can internalize chain-of-thought with log₂k curriculum stages under simplified assumptions.

City University and Kuaishou Kling proposed VLM-as-Teacher, which uses VLM feedback to optimize VGM LoRA at test time, reporting a 16.7-point average gain and raising VBVR-Bench from 0.666 to 0.781.

Thousand Token Wood v2 uses four small models from different labs to drive agents in a financial simulation game, with vLLM 0.22.1’s CUDA toolkit dependency identified as the main serving friction, while a fine-tuned 0.5B Qwen reached 0% self-trading and 100% valid quotes.

Domino reports up to 5.8x throughput speedup on Qwen3 by decoupling causal modeling from autoregressive drafting in speculative decoding. The Reddit snippet links the arXiv paper, GitHub code, and Hugging Face models, but does not disclose hardware, baseline settings, dataset, or acceptance-rate details.

PhysX-Omni models rigid, deformable, and articulated objects in one simulation-ready 3D generation framework, while PhysXVerse contains over 8.7K physical 3D assets across more than 2.9K categories.

Princeton researchers released Goedel-Architect, an agent framework for Lean formal theorem proving. Using DeepSeek-V4-Flash, it reached 75.6% pass@1 on PutnamBench, with $294 in API cost for 672 problems, compared with Hilbert’s 70.0% and about $170,000 cost.

A developer used Qwen2.5-3B to build a five-agent forest economy, and across 15 simulation rounds honey prices fell from 10 to 3, firewood rose from 4 to 7, and the Gini coefficient increased from 0.14 to 0.38.

Google Research and Google Cloud introduced the Cross-Corpus Retrieval framework as Agentic RAG for Gemini Enterprise Agent Platform, using a multi-agent workflow to plan, rewrite, route, and iteratively search multiple data sources, with up to 34% higher accuracy than standard RAG on factual datasets.

Southeast University and Peking University researchers introduced MetaFine, a diagnostic meta-evaluation framework that tests fine-grained robot manipulation across understanding, perception, and behavior, and the article says traditional binary success metrics can overestimate fine-manipulation capability by up to 70%.