Official research posts and technical reports from labs: architectures, training methods, measurement and safety research. Purely academic papers are not collected.
Google DeepMind published Co-Scientist research in Nature, introducing a Gemini-based multi-agent AI system that iteratively generates, debates and evolves new hypotheses for complex scientific problems.
Why it matters: The post discloses the system's three-stage collaboration mechanism and deployment cases at several labs, showing how AI takes part in scientific hypothesis generation.
Thinking Machines released TML-Interaction-Small, a 276B-parameter MoE model with 12B active parameters, and the post says it advances realtime voice through 200ms time-aligned microturns, encoder-free early fusion for audio and images under 200ms, and benchmark wins over GPT-Realtime-2 and Gemini 3.1-Flash.
Why it matters: HKR-H/K/R all pass: TML-Interaction-Small gives architecture, active parameters, 200ms interaction, and named rivals. Benchmarks still need replication, but a real-time voice SOTA claim is same-day material.
Shanghai AI Lab, Shanghai Jiao Tong University, and USTC tested Long-CoT SFT on Qwen3-14B-Base and found that cross-domain performance recovered and improved after 8 epochs, with generalization conditioned on optimization depth, data quality and structure, and base-model capability.
Why it matters: HKR-H/K/R all pass: the SFT-generalization claim has a clear hook, Qwen3-14B-Base plus an 8-epoch finding, and direct relevance to fine-tuning teams. It lacks deployment impact or full benchmark detail, so it stays in the mid-featured band.
OpenAI’s Parameter Golf brought together over 1,000 participants and more than 2,000 submissions to test AI-assisted machine learning research, coding agents, model quantization, and model design under strict parameter constraints.
Why it matters: OpenAI’s Parameter Golf recap clears HKR-H/K/R with a concrete contest, 1,000+ participants, and 2,000+ submissions. It is research/benchmark signal, not a model or product launch, so 78 fits the lower featured band.
The author proposes prompt caching for RL training. On Qwen3.5-4B, it reports a 7.5x speedup with 16k-token prompts and 64-token outputs, and the G=8 example with 1000-token prompts and 100-token responses reduces 8800 processed tokens to 1800 unique tokens.
Why it matters: HKR-H/K/R all pass: the angle is novel, and the post gives 16k/64 plus G=8 token-dedup numbers. Kept at 78 because this is a single Reddit post without independent replication or a paper/code artifact disclosed.
PRISM raises LLaDA-8B-Instruct on GSM8K from 67.58% to 85.30% by combining hierarchical trajectory search, partial remasking, and self-verified feedback, reducing dLLM test-time scaling cost from O(NT) toward O(N+KT) under a final candidate width K.
Why it matters: HKR-H/K/R all pass: the hook rejects brute-force scaling, the post gives GSM8K and complexity numbers, and it speaks to inference cost. Still an ICML framework paper, not a mainstream product release, so it sits in 78–84.
Qwen-Image-2.0 uses a Qwen3-VL condition encoder and multimodal diffusion transformer for image generation and precise editing, with instruction inputs up to 1K tokens and reported gains in multilingual text rendering, layout quality, and human-rated generation and editing tasks.
Why it matters: HKR-H/K/R all pass: Qwen’s flagship image model report gives concrete architecture, 1K-token instruction input, and editing claims. The domestic flagship-model signal lifts it into the must-write band.
A Science study reports that OpenAI o1 reached a 67% correct or near-correct diagnosis rate on real emergency department data, exceeding doctors at 50-55%, but the study did not cover long-term inpatient data or imaging diagnosis.
Why it matters: HKR-H/K/R all pass: a Science-linked ER benchmark reports o1 at 67% versus doctors at 50-55%. It stays below P1 because it is one diagnostic study and excludes inpatient and imaging settings.
CreativeGame makes an agent write a mechanic contract before four code-generation stages, then evaluates iterations with CreativeProxyReward, two hard gates for runtime and static errors, and lineage-aware memory shared within each game evolution tree.
Why it matters: HKR-H/K/R pass, but this is a game-generation research framework without disclosed open-source status, metrics, or production adoption. It fits the 72–77 band rather than a must-write item.
Richard Sutton and coauthors proposed Intentional Updates, which derive the step size from the desired output change; Intentional AC approached SAC on MuJoCo under batch=1 streaming training without replay, while each update used about 1/140 of SAC’s FLOPs.
Why it matters: HKR-H/K/R all pass: Sutton's name, Intentional Updates, MuJoCo conditions, and 1/140 SAC FLOPs give it substance. Strong research signal, but less market-moving than a major LLM product release, so it stays in the 78–84 band.
East China Normal University and Fudan University researchers proposed Next-ToBE, a training objective that keeps standard autoregressive inference while adding a soft target over future-token windows, and the article reports the method ranked best in 35 of 36 experiments across Qwen2.5-Math-1.5B, Qwen2.5-Math-7B, and Llama3.1-8B-Instruct.
Why it matters: HKR-H and HKR-K pass: the mechanism and 35/36 result are specific, and next-token training is a real debate. The item stays near the featured floor because no artifact, reproduction detail, or production claim is disclosed.
Zhejiang University and Tencent Youtu proposed AdaMARP for immersive role-playing, using a four-channel message format and a scene manager; its data pipeline includes 81 literary works, 20 synthetic themes, and AdaptiveBench with 100 evaluation seeds.
Why it matters: ACL 2026 role-play agent work brings four-channel messaging, a scene manager, and an 81-book dataset, clearing HKR-H/K. Narrow use cases and missing open-source or production evidence keep it at threshold featured.
NVIDIA AI released Star Elastic, a single checkpoint that can zero-shot slice 30B, 23B, and 12B reasoning models in BF16, FP8, and NVFP4; when the 23B submodel handles thinking and the 30B model handles final answers, reported accuracy rises 16% and latency drops 1.9× on AIME-2025, GPQA, LiveCodeBench v5, and MMLU-Pro.
Why it matters: HKR-H/K/R all pass: Star Elastic has a concrete mechanism and testable numbers for inference deployment. Its reach is still narrower than a frontier-model release, so it sits in the high-quality featured band.
Anthropic’s patent describes the Computer Use training pipeline: it captures user actions, uses a transformer to infer action intent, and applies a stronger model for synthetic expansion, turning raw UI operations into reasoning data.
Why it matters: HKR-H/K/R all pass: the patent angle is clickable, the three-step data pipeline is concrete, and agent builders care. It is analysis, not an official release or reproducible artifact, so 76 fits the featured threshold.
MBZUAI VILA Lab and UCL analyze Claude Code v2.1.88 source code and identify 5 design philosophies, 13 design principles, 7 permission layers, and 5 context-compaction layers behind its production-agent architecture.
Why it matters: All HKR axes pass: the contrarian Claude Code angle is clickable, the v2.1.88 permission/context mechanisms add substance, and agent tradeoffs resonate with builders. It is third-party analysis, not an Anthropic release, so it stays below must-write.
Google DeepMind released AI Co-Mathematician, an asynchronous agent workspace for math research, and answered 23 of 48 private FrontierMath Tier 4 problems, scoring 48% under 48-hour, no-token-limit conditions versus GPT-5.5 Pro at 39.6%.
Why it matters: HKR-H/K/R all pass: the story has a hard benchmark number and a concrete research hook. No disclosed product access or cross-source cluster, so it stays at the top of 78–84 rather than p1.
OpenAI researcher Jiayi Weng proposes Heuristic Learning: codex gpt-5.4 reached a perfect 864 score on Breakout and generated 342 search trajectories across Atari 57, with updates applied to code, tests, replays, and memory rather than neural-network weights.
Why it matters: HKR-H/K/R all pass: an OpenAI researcher proposes Heuristic Learning with concrete hooks like Breakout 864 and 342 Atari 57 trajectories. This is strong research/commentary signal, not an official model or product release, so it stays in the 78–84 band.
AllenAI introduced EMO, a mixture-of-experts model with 14B total parameters and 1B active parameters, trained on 1 trillion tokens and able to use only 12.5% of its experts for specific tasks while retaining near-full-model performance.
Why it matters: HKR-H/K/R all pass, but this is an AllenAI/Hugging Face research release rather than a frontier model launch. The 14B/1B and 12.5% expert-activation claims justify the low featured band.
NVIDIA Cosmos Lab and Purdue University proposed Scenethesis, a language-and-vision agentic framework for text-to-3D scene generation that uses visual grounding, SDF-based physical constraints, and a judge module; experiments report about 72% first-pass success, 91% after self-checking, and collision rate reduction from 6.1% to 0.8%.
Why it matters: HKR-H/K/R all pass: NVIDIA/Purdue plus an agent loop is clickable, and the post gives SDF constraints, a judge module, and 72%→91% results. Strong research signal, but not a product release, so it stays in 78–84.
BAIR’s post describes adaptive parallel reasoning, where ThreadWeaver and Multiverse dynamically control parallel threads for math and code reasoning; the RSS snippet does not disclose benchmark scores, latency reductions, or reproducible settings.
Why it matters: BAIR authority supports the 72+ band, and HKR-H/K/R all pass. The post names mechanisms and dynamic thread control, but lacks scores, latency gains, and reproducible conditions, so it stays below 78.