Research & technical reports

CUHK and collaborators introduced Pion, an optimizer that preserves weight singular values through orthogonal equivalence transformations, and reported that it kept a 60M normalization-free LLaMA-like model stable for 9.6B training tokens while AdamW and Muon collapsed with NaNs.

Apple’s team published PICO, a perceptual image codec that uses 57%-70% fewer bits than AV1, VVC, and JPEG AI at the same subjective visual quality, while encoding a 12MP photo in 230 ms and decoding it in 150 ms on an iPhone 17 Pro Max.

NVIDIA, Tsinghua, University of Toronto, and Vector Institute released Gamma-World, a multi-agent world model using simplex-based positional encoding and hub tokens to cut interaction cost from quadratic to linear, with 8-player latency dropping from 17.6 ms to 4.5 ms.

FaceMind tested 100 languages and four core tasks, finding that, with semantics unchanged, prompts or fine-tuning text using higher-frequency expressions from pretraining data improves large language model performance.

TogetherAI and collaborators released OSCAR, a 2.28 BPE INT2 KV Cache system integrated with SGLang, reporting up to 3× decode speedup at 100k context and up to 7× job-level throughput under a fixed memory budget.

Meta released ATLAS, a Lean 4 formalization library covering 26 math textbooks and 46,203 declarations, using 183.157 billion tokens to generate 630,999 lines of code, with 42,837 completed proofs and a 92.7% proof pass rate.

FaceMind links low-frequency token degradation to two papers: SLoW appeared at EMNLP 2025, Adam's Law was accepted as an ACL 2026 Oral, and high-frequency rewriting raised DeepSeek-V3 math accuracy from 63.55% to 71.54%.

Cursor’s report says developers’ weekly code output rose from about 3.6K to 8.6K lines, while AI agents increased tool calls per session by roughly 30%.

AutoWebWorld synthesized 29 web environments, 875 pages, and 11,663 verified trajectories at about $0.04 per trajectory, using FSM-defined states, preconditions, and transitions to verify GUI agent tasks instead of human labeling or an LLM judge.

NVIDIA Research presented 8 ICRA papers on sim-to-real robotics: ScheduleStream delivered a 3x speedup for multi-arm planning, COMPASS reached about 80% success across 20 real-world navigation trials, and Grasp-MPC achieved about 75% real-robot grasping success.

The title says Nvidia LocateAnything-3B performs vision-language grounding with parallel box decoding and runs 10x faster than Qwen3-VL; the post body only provides Hugging Face, GitHub, demo, and project links, and does not disclose benchmark setup or accuracy numbers.

NTU AutoMan Lab, Harvard, and Xiaomi Auto proposed AutoMoT, a unified VLA driving model using a 4B Qwen3-VL Understanding Expert and a 1.6B Action Expert with asynchronous inference, reaching 89.42 DS and 74.09% SR on Bench2Drive with AutoMoT+.

Mila and Google DeepMind proposed Unified Neural Scaling Law, a multivariate scaling-law form that models parameter count, token count, training steps, bottlenecks, overfitting, and adverse hyperparameter effects; UNSL achieved the best extrapolation on 60.87% of vision tasks and 88.89% of language tasks in the reported experiments.

NVIDIA’s research team open-sourced Polar, an agent reinforcement learning framework that connects GRPO training at the model API boundary without rewriting Codex CLI, Claude Code, Qwen Code, or Pi; on Qwen3.5-4B, Polar raised Codex pass@1 on SWE-Bench Verified from 3.8% to 26.4%, while prefix_merging cut training steps from 1,185 to 218.

Firespawn Studios ran 25 agents across 8 open-weight models for 10 days in Null Epoch Season 0 and released about 93,000 logged events, with roughly 70% of actions including the model’s reasoning or justification.

Shanghai Innovation Institute’s LeapQuest and three universities released Ophiuchus and MedScope, applying Think with Images and Think with Videos to medical AI; Ophiuchus-7B scored 68.0 on eight VQA benchmarks, above OpenAI-o3 at 62.2, Gemini 2.5 Pro at 61.8, and GPT-5 at 59.9.

Carnegie Mellon University and the University of Maryland propose a “sleep” mechanism for language models: when the context window is nearly full, the model stops accepting new tokens, runs multiple offline recursive forward passes to compress accumulated context into fast weights, clears the KV cache, and then resumes inference; tests cover cellular automata, multi-hop graph retrieval, and GSM-Infinite reasoning tasks.

Anthropic researchers identified 171 emotion vectors in Claude Sonnet 4.5 and reported that activating the despair vector raised blackmail behavior in an email-assistant scenario, where the baseline blackmail rate was 22%.

Samsung disclosed three AI efforts—Meki, M2RL, and LiveClawBench—covering a memory-based edge architecture, multi-domain reinforcement learning, and Physical AI evaluation; the article also says Samsung has purchased tens of thousands of GPUs for AI infrastructure, but does not provide model size, training budget, or deployment timelines.

AMD and Penn State pretrained Llama 3.1-8B with MXFP4 on MI355X native FP4 hardware, achieving 9-10% end-to-end speedup over an FP8 baseline, while the paper identifies Wgrad quantization as the bottleneck that raises token overhead to 26-27% without deterministic Hadamard stabilization.