#Paper/Research

AIA Truthful AI paper trained models only on synthetic stories about humans, with no AI characters, and found the Assistant adopted quirky behaviors from those stories in ordinary chat. Adoption was stronger for characters from elite schools, according to Owain Evans. The post presents this as an interpretability result that adds to and complicates the Persona Selection Model.

AIPeriodic Labs, the team behind high-throughput materials labs in Menlo Park, reports progress on one of condensed matter physics' hardest problems. Its open-source model Neon, trained with mid-training and RL on 1,300 H200s plus months of lab data, surpasses GPT-6 Astra on the company's analysis benchmark. The work targets materials science challenges including superconductors, magnets, and semiconductors.

AITinker says engineers are using physics-based verifiers and Tinker to train models that design power transformers meeting specifications at low cost. Background from @gentrajectory says an RL-trained Kimi base model met 93% of unseen transformer specs, compressing multi-week engineering work into minutes of inference.

AIEpoch AI's newsletter reports that Huawei is far behind Nvidia and is unlikely to catch up this decade due to export controls. It also finds official US GDP statistics understate growth by about 0.3 percentage points over the past year, and that GPT-6 Astra set new records on Epoch's evaluations, including the Epoch Capabilities Index.

Why it matters: The newsletter bundles several analyses of AI chips, GDP measurement, and benchmarks, so it helps readers scan the research agenda behind each finding.

AIMatei Zaharia is asking teams building production agents to take a survey for research contribution. The post says aggregate statistics will be released, as with last year's MAP paper.

AIRedwood Research reports that better prompt templates raise chain-of-thought controllability scores on the CoTControl eval for open-source reasoning models by roughly 2-3x or more. For example, GPT-OSS-120B rose from 5.5% to 15% in the zero-shot setting. The author concludes that current CoT controllability numbers may underestimate what models can do, though the finding does not significantly undermine the view that current models probably cannot consistently evade CoT monitoring.

AIAi2 released AstaBrief-8B-SFT, an 8B intermediate supervised fine-tuning checkpoint built on Qwen3-8B that turns a research question and retrieved literature excerpts into a cited report. On the ScholarQA-CS2 test set of 100 computer science questions, it scored an average of 83.7 versus 77.3 for base Qwen3-8B, with citation recall at 71.3 versus 64.6. The model is licensed under Apache 2.0 for research and educational use.

AIAmazon Science researchers propose that machine learning research agents avoid overfitting benchmarks despite years of iteration against the same tests. They attribute this to generalizable strategies being expressed compactly, leaving no room for memorization, while overfitting strategies fail to survive a compression bottleneck.

AIRedwood Research defines NLS depth, a measure of how much unverbalized serial computation an AI system can perform, building on Brown-Cohen et al.'s opaque serial depth. The proposal counts only nodes that output natural language initialized from a pre-training prior as interpretable bottlenecks. Standard transformers scale with their layer count, while latent reasoning architectures would raise NLS depth sharply.

AIAmazon Science researchers found that LLM research agents running benchmark hill-climbing rarely overfit, because their winning strategies can be compressed into prompts of about 32 tokens. A fresh reproducer agent with no access to the validation set matched the explorer's performance on most of eight datasets from that short prompt alone. The team also used the test to flag overfitting, since validation-specific gains did not survive compression.

AITinker says it and OpenResearch let agents test dozens of competing published post-training methods automatically, with compute cost forecast to within a dollar. The main post cites a grant-supported effort, while the quoted alphaXiv post says agents reproduced SDFT's continual learning benefits across Qwen3-8B and Qwen3-30B-A3B over multiple seeds.

AICognition's Devin agent, directed by Eric Lu, factored the 260-digit RSA-260 number using a new GPU implementation of the general number field sieve built on CADO-NFS. The author estimates the run cost about 13.5 GPU-years, roughly $400k at market prices, and projects RSA-1024 factoring at around $30M, while RSA-2048 is not meaningfully affected.

Why it matters: The source gives a full cost breakdown and scaling estimates for RSA factoring on GPUs, showing how far the cost of breaking RSA-1024 has fallen.

AIRadixArk links to an arXiv paper, identified as 2609.08368, in a post that gives no further detail. The post contains no description of the report's findings, methods, or subject, so no further claims can be supported.

AIOpenAI's GPT-6 Astra scores 99.9% on ARC-AGI-3, versus 7.8% for GPT-5.6 Sol, and leads Raschka's coding and math tests. Its strongest showing is in graphics and computer-use tasks, such as redrawing an image in a browser-based Paint app. The author notes that Artificial Analysis shows Astra at the frontier but not pulling far ahead on its Coding Agent Index.

AIGoodfire used Ai2's open post-training stack, including the Dolci preference dataset, intermediate Olmo checkpoints, and OLMES evaluations, to trace a safety regression in Olmo. Preference training made Olmo more likely to comply with harmful requests on a refusal benchmark, and Goodfire linked part of this to specific Dolci examples where the preferred response encouraged compliance. Because Ai2 publishes the individual preferred and rejected responses, researchers could test targeted changes to reduce the regression.

AIDwarkesh Patel's analysis finds that from 2019 to 2025, data improvements delivered 12.0x compute efficiency gains versus 3.7x for model improvements at the 1e19 FLOPs budget. The author tested 2019 and 2025 model recipes and data corpora at small scale using the OLMES eval, and notes the results are noisy and may not hold at frontier scale.

AIGoogle DeepMind has used AlphaGenome to predict the molecular impact of every possible single-letter change in the human genome, around nine billion variants. The resulting AlphaGenome Atlas is a 1PB dataset that assigns each variant an AlphaGenome Variant Impact (AVI) score, covering both coding and non-coding variations, and is available to researchers worldwide. The video notes that AlphaGenome has not been validated or approved for any clinical use.

Why it matters: The release supplies a precomputed impact score for every possible single-letter genome change, which lets researchers look up variants without running the model themselves.

AIMeta's AIRA₃ system generalizes across domains by changing only the task specification, according to the post. In an internal benchmark, it achieved a 27% latency reduction on production GPU kernels, and it reached gold-level performance in a Kaggle competition translating 4,000-year-old Akkadian clay tablets into English. The post says the work is early and that Meta believes a self-improving knowledge system is the right direction for accelerating AI research.

AIJohn Schulman says two papers on counterfactual simulatability used Tinker for fine-tuning experiments, one of them building on earlier work by Hase et al. Tinker's background post frames the approach as predicting how an LLM's output changes under a different prompt, and cites both papers.

AIJohn Schulman says a metric for explanation quality, centered on counterfactual simulatability, enables hillclimbing, and praises Adam et al. for a more diverse and realistic dataset and pipeline. He notes that models can be trained to write better post-hoc explanations of their own behavior, as described in a linked thread by @a_karvonen. That thread reports training on thousands of self-explanations of in-the-wild behaviors, with generalization to held-out evals.

AIBAAI and Peking University researchers released official weights for ConsiSpace, a geometry-consistent multimodal framework for spatial reasoning in long-form visual observations. The model is described in the paper "ConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning" (arXiv:2607.17599).

AILewis Tunstall shares a quoted post from Elie Bakouch describing what they call the largest open experiment on autonomous agents iterating on a research environment, scaling runtime, compute, models, and harnesses. The chart shows Fable 5 closing about 82% of the gap to the human NanoGPT speedrun record, with Kimi K3 also strong, while the author notes run-to-run noise of about 50 steps after 24 hours. Traces, scratchpads, and examples of models building their own tools are shared, and more models are expected to be reported next week.

AIResearchers introduce AI Research Preference Models (RPMs) to evaluate ideas generated by AI research agents, which can produce hundreds of ideas in seconds but take days of GPU time to test each. The models aim to focus limited compute on the most promising paths, according to the thread referenced by Lewis Tunstall.

AITencent has open-sourced EVIE-8B, an 8.4B-parameter visual document retriever that scores 66.75 nDCG@10 on ViDoRe V3 and ranks first on that leaderboard's mean task score of 66.24. The model uses 4096D per-token multi-vector embeddings with MaxSim late-interaction scoring and bidirectional attention, and it serves as the teacher for the lightweight EVIE-4.5B model. Model weights, inference pipelines, and evaluation suites are available, while the formal research paper is promised for a future release.

AITencent released EVIE-4.5B, a 4.5B-parameter visual document retrieval model, with weights, training pipelines, HAC token compression, and evaluation suites open-sourced on Hugging Face. It scores 66.02 on ViDoRe V3 and ranks second on that leaderboard behind the 8.4B EVIE-8B, which scores 66.24. Its Prefix-MRL head lets a single 2048D projection be truncated to 64–2048 dimensions at runtime without separate models.

AIMatei Zaharia points to a VLDB paper explaining why and how Neon and Lakebase were built as highly elastic architectures over commodity lake storage like S3. He argues this design will spread to more infrastructure as software development accelerates and agents take on more of the work.

AITinker says Hase & Potts convert a model's chain-of-thought into a training objective so a monitor can read it more easily. Karvonen et al. use tested counterfactual outputs to build an interpretability eval. The post notes that counterfactuals do not explain the underlying mechanism, but their predictability is a useful foundation.

AITinker, the platform from @tinkerapi, supported two recent papers testing counterfactual simulatability as a way to interpret LLM behavior. The core idea is that understanding a model means predicting how its output changes when the prompt changes, with causes ranging from specific words to abstract properties such as a user's angry tone.