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5,991 papers
Improvements are consistent across realized risk, risk-adjusted performance, and drawdown control, remain after the modeled execution frictions, and are supported by a 99.9\% Model Confidence Set that retains only the neural estimator.
This work introduces a method that induces sparse neural activity in heavily quantized linear-attention models with minimal performance loss, and positions sparse, quantized linear-attention models as a natural fit for deploying LLMs on event-driven multi-core platforms.
This work provides a system-theoretic interpretation of generalization in learning-enabled dynamical systems arising in data-driven optimization and feedback control approximation, and establishes a matrix inequality-based certificate and a uniform stability bound that separates the one-sample sensitivity of the learned operator, and an algorithm-dependent dynamical gain.
MineAmongUs is introduced, a 3D multimodal Among Us sandbox where imposter agents must deceive crewmates through joint verbal and non-verbal action, and ARIA is proposed, a configurable VLM-agent harness that exposes five cognitive-component ablation axes and opens a new path for embodied VLM-agent alignment research.
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DBloom speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, preserving the target's output distribution and recommending the histogram as a preflight check before spending training compute.
This work proposes a decompose-before-reconstruct approach, which significantly improves mesh fidelity and novel-view synthesis and novel-view synthesis, while supporting object-wise modifiability and interactivity.
A reproducible age-prediction benchmark across six rs-fMRI datasets is introduced and provides common inputs, model settings, data splits, and analysis scripts so that future SPD matrix learning methods can be evaluated under the same external-validation protocol.
This work adds to the Kathleen trunk a second memory layer -- a"notebook": a fixed-key holographic (HRR) associative store with a learned local write gate, a self-gating raw read, and write-triggered forgetting -- 25K parameters that attach to the logits of any trunk.
We study null-space estimation from a noisy matrix. For a simple left null space, we first derive an exact compact expression for the error of the smallest left singular vector. We then give an all-order series for the SVD vector and projector, followed by compact and consistently truncated series forms for the fixed-realization empirical risk and conditional population generalization risk. The recursion extends to a multiple-dimensional null space by following the complete invariant subspace. The convergence radius is not inferred from an error plot: it is computed independently from the nearest complex exceptional point that joins a retained eigenvalue branch to its complement. A reduced-nullity experiment shows that moving this spectral boundary can increase the radius, although the improvement is not monotone in the retained nullity. For individually ordered null directions under Gaussian training with \(\tau\geq m\), we prove that the Wishart splitting matrix \(W\) gives a strict second-order empirical ranking. Gaussian averaging equalizes the leading generalization risks at both small and very large noise, while a column-swap theorem proves strict expected generalization ranking for an isotropic signal subspace. For unequal spikes, an exact population-overlap criterion and a simultaneous \(99\%\) Monte Carlo confidence certificate explain the observed intermediate ranking. A sixth-order risk correction improves the lower-crossover estimate in the reported experiment. This equal--ranked--equal phenomenon is a finite-sample diagnostic related to spectral mixing, but its tolerance crossings, the exceptional-point radius, and the asymptotic BBP threshold are three distinct quantities.
CREST is proposed, an inference-time alignment method that steers base model hidden representations using safety directions extracted from a guidance model of any family, avoiding token-level structural limitations entirely and outperforming baselines by up to 22.2\% on safety benchmarks.
The results illustrate a general principle for recursive zero-error constructions: intermediate structures with the same dimension and current code size can have different downstream value depending on where and how they are used in the recursion.
This work proposes Decentralized Barrier Follow-the-Regularized-Leader (Dec-BFTRL), and evaluates each agent's played action against the average of all local objectives, with applications to online continuous diminishing-return (DR) submodular maximization.
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Machine Intelligence
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General Science
GPT-Lab
· gpt-lab.eu
Sep 3, 2026
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
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