Inside the Model Factory — Eiso Kant, Poolside AI
Poolside's co-CEO on how his small team of top researchers built a model factory capable of training Laguna S - a 118B MOE beating Thinky's ~1T…
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Local AI that finds sensitive files on your Mac before attackers do
Article URL: https://www.vaultsort.com/guardian Comments URL: https://news.ycombinator.com/item?id=49017170 Points: 4 # Comments: 1
Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning
Reinforcement learning with verifiable rewards (RLVR) has substantially improved language-model reasoning, yet its extension to vision-language models remains constrained by the lack of training data that…
Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization
Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However, RL algorithms with PPO-Clip are inherently limited by exploration collapse. Subsequent works…
Quoting Seth Larson
The Python Package Index (PyPI) now rejects new files being uploaded to releases that are older than 14 days. This restriction was put in place to…
RT Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞): Re Liang Wenfeng believes that the comprehensive gap in AI between China and the US is 12-18 mont…
RT Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)Re Liang Wenfeng believes that the comprehensive gap in AI between China and the US is 12-18 months, just like…
try the codex security plugin, for applying our models to cyberdefense:
try the codex security plugin, for applying our models to cyberdefense:Vaibhav (VB) Srivastav: Reintroducing Codex Security plugin!point it at a codebase or diff and it can…
RT Ashok Elluswamy: Using FSD is far safer than driving manually, as measured over 12 billion miles of its use. Safer by a margin of 2x the miles betw…
RT Ashok ElluswamyUsing FSD is far safer than driving manually, as measured over 12 billion miles of its use. Safer by a margin of 2x the…
Startup founders urge Trump not to shut off Chinese open weight AI
https://www.politico.com/news/2026/07/22/startup-founders-urge-trump-not-to-shut-off-chinese-open-weight-ai-01008992 submitted by /u/External_Mood4719 [link] [comments]
Get You a Model: If You're Not Paying, You're Missing 90% Of Improvements
I'd think this is an audience who don't need to be convinced that the mundane utility of frontier models is increasing fast, but some private discussion…
RT Xinyu Yang: Can't wait to see K3 available on your platform!
RT Xinyu YangCan't wait to see K3 available on your platform!Together AI: We analyzed Kimi K3 Max vs. GPT 5.6 Sol Max for software engineering tasks…
Self Gradient Forcing: Native Long Video Extrapolation
Recent autoregressive video diffusion methods are increasingly built upon Self Forcing, where the student is trained on histories produced by its own rollout rather than ground-truth…
Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
arXiv:2607.19459v1 Announce Type: cross Abstract: Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy…
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
arXiv:2601.21026v2 Announce Type: replace Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics. Boltzmann Generators (BGs) tackle it by combining a generative…
Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics
arXiv:2607.19389v1 Announce Type: cross Abstract: As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have…
A Confidence Interval for the $ell_2$ Expected Calibration Error
arXiv:2408.08998v4 Announce Type: replace Abstract: Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains…
Bayesian Wind Tunnels for Model Selection
arXiv:2607.19379v1 Announce Type: cross Abstract: Prior work has shown that transformers can perform exact Bayesian filtering within a fixed hypothesis class. Can they also perform Bayesian…
Boltzmann-Expected Molecular Design with Decoupled Annealing Flows
arXiv:2607.19519v1 Announce Type: new Abstract: Most 3D properties relevant to molecular design, including free energies and shape descriptors, are $textit{expectations}$ over the Boltzmann distribution over 3D…
Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
arXiv:2607.19378v1 Announce Type: cross Abstract: Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while…
A Geometric Approach to Constrained Online Learning
arXiv:2605.21107v3 Announce Type: replace-cross Abstract: We study constrained online convex optimization with adversarial time-varying constraints. At each round the learner acts before observing the loss and…
Adaptive deep nonparametric regression from dependent data under covariate shift
arXiv:2607.20309v1 Announce Type: new Abstract: Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions.…
Statistical Early Stopping for Reasoning Models
arXiv:2602.13935v2 Announce Type: replace-cross Abstract: While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, particularly under uncertainty, given…
RELTA-SGLD: Relative-Growth Localized Taming for Nonconvex Stochastic-Gradient Langevin Learning
arXiv:2607.19544v1 Announce Type: new Abstract: We introduce RELTA-SGLD, a taming scheme that stabilizes superlinear stochastic-gradient updates while reducing unnecessary suppression of the original learning drift. A…
Optimal Recalibration of an Online Predictor
arXiv:2607.19689v1 Announce Type: new Abstract: We study the problem of recalibrating an online predictor [KE17, OKS24]: given an arbitrary "hint" sequence of forecasts, the learner must…