This paper introduces a unified causal-origin taxonomy that categorizes distributional shifts in reinforcement learning into internal, agent-driven, and external, environment-driven sources. It unifies ID/OOD generalization and non-stationary settings by framing shifts as structured changes in the agent-environment interaction process, using a POMDP decomposition and a shifted-time boundary perspective.
CrossMaps is a real-time, confidence-aware semantic mapping pipeline that uses RGB-D data to create language-queryable maps. It integrates multi-scale CLIP embeddings with a dual-memory architecture—Short-Term and Long-Term Memory—to aggregate visual observations and promote coherent, confident cells as persistent semantic landmarks. The system enables natural language queries to guide rover navigation via semantic heatmaps.
CircuitLasso proposes a scalable method for learning sparse circuits in large language models using sparse linear regression. It achieves structural accuracy comparable to state-of-the-art intervention-based methods at significantly lower computational cost, while enabling efficient discovery of semantic feature propagation and improving performance on domain-generalization tasks with reduced cost.
This paper proposes a structural causal model using a directed acyclic graph to define when Theory of Mind engagement is causally warranted in human-machine conflict. The model identifies four exogenous conditions, five mediators, and three causal pathways for ToM activation, with epistemic accuracy as the primary outcome. It offers a resource-rational framework for AI social reasoning, validated through simulation and human-machine studies.
A new empirical auditing framework detects and classifies synthetic data disclosures as either true or phantom. It distinguishes direct reproductions of user data from incidental generation without model access or training, using only synthetic output and a held-out control set. The method provides tighter privacy leakage bounds than prior approaches and requires significantly fewer computational resources.
A quality cliff at 6.25 Hz in neural audio codecs is caused by insufficient training token exposure due to fixed clip duration. Correcting this training configuration enables smooth WER degradation down to 3.1 Hz and 1.6 Hz, indicating low frame rate efficiency is more achievable than previously thought.
A study finds that while textual review signals can be fused with collaborative data, their marginal contribution remains limited compared to collaborative signals in matrix factorization models. Adaptive fusion and cross-attention mechanisms improve representation flexibility, but do not significantly boost performance across datasets.
Analysis of 56,800 AI conference papers shows documentation practices improved from 2014 to 2024. Papers sharing both code and data increased from 11% to 64%, and estimated reproducibility rose from 28% to 64%. These improvements predate formal reproducibility checklists, indicating a broader shift toward open science.
A consensus-based agentic large language model framework is proposed for accurate 10-digit Harmonized Tariff Schedule code classification in Canadian maritime logistics. Evaluated on 3,300 expert-labeled product records, the framework shows that fine-grained HTS classification remains challenging for advanced LLMs, highlighting the need for evidence-grounded, uncertainty-aware, and human-in-the-loop workflows.
Experiments on EC 2025's 'Stable Menus of Public Goods' show that human-intuition prompts improve LLM performance and multi-turn interactions enhance ambitious steps. However, when compared to a first-year PhD student using an unpublished manuscript, the LLM is found to be slightly less effective.
PACT combines a reactive RL policy with a 2B-parameter Small Language Model to generate and validate action plans. The SLM plan is executed directly if verified as safe, feasible, and complete, bypassing the RL policy. PACT outperforms baselines on three increasingly difficult FrozenLake environments.
ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness under image corruption.
Public AI evaluation archives show that a single terminal result can arise from two distinct pre-terminal histories, with estimated times to reach 95% of performance ceilings at 23.03 or 75.13. A candidate selection-aware frontier model fails synthetic recovery and uncertainty calibration, and is rejected by fixed audit gates. An archive-and-adjudication protocol verifies timing boundaries and falsifies unsupported frontier claims.
TuneJury is an open, instance-level pairwise reward model that predicts music preference scores from text prompts and audio clips. It is trained on diverse human-preference data and demonstrates strong generalization, with anchor calibration enabling efficient post-hoc alignment for music generation systems.
TokenPilot reduces inference costs by 61% to 87% in both isolated and continuous modes, outperforming prior systems in cost efficiency while maintaining competitive performance. It uses ingestion-aware compaction and lifecycle-aware eviction to preserve prompt cache continuity and minimize token footprint without introducing prefix mismatches.
FusionRS introduces the first large-scale RGB-infrared-text dataset for remote sensing vision-language modeling. It aligns RGB and infrared images with IR-aware captions, enabling dual-modal vision-language foundation models. Experiments show improved RGB-IR alignment, retrieval, and captioning, with ablation studies confirming the critical role of modality-specific textual supervision.
HAMON uses passive optical components to perform long-horizon time-series forecasting, outperforming top digital models on ETTm2 across all horizons and on ETTh2 at all but the longest horizon. It achieves up to 14% lower MSE and relies on physical optical propagation without trainable digital layers, demonstrating that passive optical mixing can produce competitive forecasts.
Image classifiers like PRISM2D, GFNet, and ViT-B/16 show that phase, not magnitude, drives predictions in hidden layers. ResNet-50 reveals a latent sign code in late blocks, indicating phase/sign identity exists across architectures, though expressed differently due to activation and readout mechanisms.
Microsoft CEO Satya Nadella introduces 'Loopcraft' as a new theory of the firm, emphasizing that the real opportunity in AI lies not in selecting the best model, but in building learning loops that compound human and token capital. He asserts that the priority must be creating frontier ecosystems where every organization can own and grow its institutional knowledge, enabling broad value flow across industries and countries.
Hyperball is a simple optimizer wrapper that sets fixed Frobenius norms for weight matrices and their updates. It improves training speed and learning rate transfer in large models, achieving 20--30% token equivalent speedup over weight decay baselines on up to 1.2B parameter models.