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.
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 to image corruption.
ExpRL introduces a novel mid-training approach for LLMs using human-written question-answer data as reward scaffolds. Instead of imitating reference solutions, it constructs problem-specific grading rubrics to reward intermediate reasoning steps, enabling better initialization for sparse-reward RL and outperforming SFT, sparse-reward GRPO, and self-distillation on math reasoning tasks.
Hierarchical Advantage-Weighted Behavior Cloning (HABC) enhances online RL fine-tuning of vision-language agents by using separate critic heads for viability and efficiency. It combines their outputs via a state-adaptive gate and applies per-transition weights, while intervention-aware credit assignment prevents supervision leakage. In real-robot experiments, HABC boosts success rates to 92%, 88%, and 38% on three bimanual tasks, surpassing SFT baselines of 36%, 44%, and 12%.
HalBench evaluates 29 open-source LLMs on a custom benchmark for sycophancy and hallucination. Qwen 3.6 and Gemma 4 outperform larger models, with Qwen 3.6 achieving 36.6% pushback—higher than GPT-5.4 and Gemini 3.1 Pro. Model size does not correlate with honest responses, indicating that architecture and training data matter more than parameters.
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.
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.
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.
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.
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.
Factorized Neural Operators (FaNO) decompose spectral representations into equivariant dynamic and invariant persistent responses. This factorized structure enables better interpretability, generalization, and consistent predictions across scales, domains, and physical regimes.
CEAP, a new circuit discovery method, substantially reduces resampling variance compared to EAP-IG. The paper shows that rephrasing variance arises from prompt templates activating different circuits, suggesting LLMs are inherently hard to steer across diverse inputs. Sample-wise variance is largely benign, as poor unfaithfulness scores result from selective contribution scaling, not circuit defects.
We propose a new functional gradient descent algorithm that adapts its representation during optimization. The method achieves convergence to a stationary point under smooth losses and to a global minimizer under smoothness and a Polyak-Lojasiewicz condition, despite using finite-dimensional approximations. It outperforms both fixed-approximation FGD and neural network baselines in regression, PDE solving, and computer vision tasks.
This paper proposes a unified causal-origin taxonomy for distributional shifts in reinforcement learning, linking ID/OOD generalization to non-stationary settings. It decomposes the agent-environment interaction using a POMDP framework, identifying internal, agent-driven, and external, environment-driven shifts, with explicit, implicit, and hybrid types defined by the shifted-time boundary. The work introduces an evaluation framework to measure shift impact through performance degradation and recovery metrics, enabling systematic analysis of RL robustness.