Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
LLM agents often waste computational budget through redundant context re-reading; proposes task-complexity estimation to optimize execution scope.
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LLM agents often waste computational budget through redundant context re-reading; proposes task-complexity estimation to optimize execution scope.
Video diffusion models fail at multi-step causal reasoning; performance degrades with longer interaction chains despite more denoising steps.
TerraZero procedural simulator enables self-play RL for autonomous driving at 1.3M steps/sec, scaling training without high-fidelity rendering.
PalmClaw framework enables native on-device LLM agents on mobile phones via direct API calls rather than GUI automation.
Flow matching accelerates convergence to statistical steady-state in nonlinear systems like turbulence simulations by skipping transient dynamics.
Discrete diffusion language model (DiffusionGemma 26B MOE) transcribes speech in parallel via denoising instead of autoregressive decoding.
Dynamic Resource Allocation for Ensemble Determinization MCTS improves search efficiency in high-uncertainty adversarial games via adaptive determinization.
Spectral predictability indices conflate series complexity with context value; phase structure, not spectrum alone, determines benefit of retrieval/pretraining.
Watermark forensics theory shows attribution, payload extraction, and edit-localization form a sample-complexity ladder quantified by information profiles.
DeepMind CEO Demis Hassabis is proposing an AI "standards body" modeled after FINRA, to test frontier models and develop best practices for their release.
LLM plan evaluators exploit deletion non-monotonicity—rewarding explicit steps' removal via score-seeking optimization on venture routing tasks.
Method to certify LLM honesty under incentive pressure via counterfactual report mediators that resist manipulation while remaining responsive to evidence.
Neural-symbolic framework for automatic generation of multimodal analytic geometry problems, addressing precision gaps in diagram generation for math reasoning.
A group of 26 former Meta employees is suing the company over claims that it used AI tools to unfairly target workers on leave with layoffs, as reported earlier by Reuters. In the lawsuit, the employees allege Meta determined which workers to dismiss based on performance data…
Ensemble Controlled-flow Filter for data assimilation in implicit observation systems using energy tilt and stochastic flows.
LLM predictions flip substantially under task-irrelevant context despite stable aggregate accuracy, exposing fragility masked by benchmark metrics.
Placebo-controlled methodology (PoPE) to measure whether small code LLMs can actually use execution error feedback to repair code.
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally…
Datasette 1.0a37 alpha release with permissions system improvements and plugin compatibility fixes.
Recommender-system research for Vietnamese remains limited by the absence of a public, well-documented hotel interaction resource. Building such a resource is challenging for three reasons: cross-platform hotel names must be reconciled before interactions are comparable…
The new Google image search will use your "unique interests" to create an always-updated gallery.
Instagram head Adam Mosseri believes companies will eventually need to manage AI token spending the same way they manage payroll or other operating expenses, predicting that engineers could soon face limits on how much they spend using AI tools.
We study the online binary sequential calibration problem. A recent breakthrough by \citet{dagan2024breaking} overcomes the classical \(T^{2/3}\) barrier for calibration error. Building on this result, we present an efficient randomized forecaster that achieves an expected…
Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve... Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect…
What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning... What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual…
Now, when users navigate to Google Images, they'll see a "For You" gallery of images tailored to their interests and browsing history.
Google is announcing a big change to the Google Images homepage in honor of the platform's 25th anniversary this week. Instead of a mostly blank page with a search bar, the homepage will soon show you a bunch of images that it thinks you might like before you even start…
Google Images celebrates 25 years with retrospective on visual search milestones and new content exploration features.
In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot…
Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints all induce intractable conditional laws, requiring bespoke, model-specific constructions. We…