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FlowBudget: Training-Free Sampling-Budget Estimation for Flow Matching Models
arXiv:2610.20636v1 Announce Type: new Abstract: Sampling from continuous flow-based generators such as flow matching models requires numerical ODE or SDE integration, making inference cost depend on the number of function evaluations (NFEs). In practice, the sampling budget is inherited from prior work or selected through expensive FID-versus-NFE sweeps, using FID to assess image-generation quality, requiring sample generation and evaluation at multiple budgets. This makes sampling-budget selection dependent on repeated empirical evaluation. We therefore ask a complementary question to solver design and timestep allocation: for a fixed pretrained model and solver, what numerical resolution is sufficient? We introduce FLOWBUDGET, a training-free estimator of solver-relative sampling complexity. Using simulation-free interpolation probes of the learned dynamics, FLOWBUDGET normalizes the leading one-step discretization error by the motion, estimates its coefficient from interpolation pro
Multi-Dimensional Featurizers Yield Fine-Grained Steering and Data Shaping in Robot Foundation Models
arXiv:2610.20767v1 Announce Type: new Abstract: Can better feature discovery improve control over robot behavior through internal interventions and training data? Recent work in vision and language models reveals multidimensional features, motivating block-sparse featurizers (BSFs), which represent each feature in a low-dimensional subspace. We investigate whether recovering this structure provides an interpretable interface for steering vision-language-action models and shaping the data from which they learn. We show that BSFs recover several multi-dimensional task-relevant features underlying the model's computation (e.g., perception, language, proprioceptive state, episode phase, and control variables). We causally validate these features through fine-grained steering: targeted changes in visual perception, semantic representation, and spatial localization produce predictable changes in downstream action prediction. Finally, we use feature-based data attribution to isolate where a s
Evaluating Model Capabilities After Benchmark Saturation
arXiv:2610.20667v1 Announce Type: new Abstract: Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and with it its utility for informing AI safety cases and frontier policies. Yet benchmark items produce far more information than the scores we typically extract from them: a full transcript of how each model solved the task. We investigate whether these transcripts can be used to assess model capabilities even after benchmark scores have saturated. We simulate saturation retrospectively using historical benchmark and model releases, and test whether protocols that access only correctly solved transcripts from saturated benchmarks can predict capabilities measured on held-out, unsaturated items. We evaluate representative contemporary approaches for extracting information from model transcripts: pairwise judging, single-transcript absolute scoring, and predictions fr
Causal Analysis of Robot Foundation Models Enables Efficiency Gains
arXiv:2610.20750v1 Announce Type: new Abstract: Vision-language-action (VLA) models are a promising approach to general purpose robot manipulation. While the internal computation of vision-language models, which compose the backbone of VLAs, has been studied extensively, less is known about how their perceptual and semantic representations are transformed into action in VLA models. We study this question mechanistically in three frontier VLA models ($\pi_{0.5}$, MolmoAct2, and MolmoBot) by analyzing the causal pathways through which the input image and the instruction shape actions. Across the three models, activation patching shows that causally relevant information influences the action expert (AE) through a narrow band of mid-to-late cross-attention layers, which we term the action-conditioning interface. The content of this information changes with token and layer within the model. Early-to-mid layer representations of the target noun token determine which object is selected, where
The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with 180 tasks across six game types, to evaluate agents while games continue running during inference. Paired evaluations show that GPT-6.1-sol outperforms the faster Jev when games are paused, but falls behind when they keep running. We propose Two-Timescale Offline Recursive Self-Improvement (TORSI): a slow teacher studies failures and tests corrections in paused games between attempts, then writes reusable instructions for a fast student. The student acts without teacher calls during gameplay, and validation scores determine which updates are adopted. Two rounds raise Jev's average test score from 13.51 to 24.19 on a 0--100 progress scale, improving all six game types. The instructions also improve average score
I Let AI Write My Code for a Week. The Coding Was Faster, but Something Felt Wrong.
I started using AI to save time while coding. Somewhere along the way, I started wondering whether I was saving time or skipping the part Continue reading on Medium »
The Kling 4.0 Prompt That Directs All 30 Seconds
Kling 4.0 is now fully available. Here is a copy-paste template built on Kling s own prompt structure, plus the mistakes that waste your Continue reading on Medium »
# DISPATCHES FROM A FRACTURED WORLD
### Artificial Intelligence, Digital Immortality and the End of Goodbye*Continue reading on Medium »
Can Wi-Fi Recognize Your Gesture in a Room It Has Never Seen?
What AirFi reveals about radio waves, gestures, and the problem of changing roomsContinue reading on Medium »
The Tesla Roadster flew, drove upside down, and hit 60mph in under a second
Elon Musk promised a flying car. And the Tesla Roadster did exactly that. Tesla demonstrated its next-generation Roadster at an event in Waco, Texas, where the electric supercar drove upside down, briefly flew, and accelerated to 60mph in under a second. It was an impressive demonstration of a car that likely will come with some heavy caveats if it ever goes into production. The event was less than 30 minutes and felt more like a stunt show than a product reveal. Indeed, there was no mention of price, trim levels, or other such mundane details. Who cares about that boring crap anyway? The people were here to see a car fly, and that's wh … Read the full story at The Verge.
When AI Takes the Blame, Hackers Get the Advantage
Why cybersecurity investigations must look beyond AI and find the real attack path.Continue reading on Medium »
AI is already inside most asset managers’ investment process.
At RBC Capital Markets, a post-earnings research note that used to take an analyst 45 minutes is now drafted in seconds (RBC Capital Continue reading on Medium »