Technology
Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion
Pause-and-think-T dataset and benchmark enable a 4B-parameter model to match GPT-5.2’s scene understanding with 59x fewer parameters than Qwen3-VL, proving focused reasoning supervision allows smaller models to generalize effectively.
Linguistics-Aware Non-Distortionary LLM Watermarking
LUNA is a linguistically adaptive watermarking framework achieving high detection accuracy (AUROC 0.9959) with minimal text distortion. It outperforms baselines across six languages by maintaining low perplexity shifts and preserving content quality.
LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM
Samsung’s LP5X-PIM Sim is a high-fidelity HW/SW integrated simulator for LPDDR5X-PIM. It enables precise assessment of energy efficiency and performance optimization.
LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification
LinguIUTics ranked 4th in PsyDefDetect 2026 by fine-tuning Qwen3-8B with QLoRA and imbalance-aware strategies. This approach significantly boosted minority-class recall, improving macro F1 by 24.4% over the baseline.
Demystifying the Optimal Fair Classifier in Multi-Class Classification
This study introduces a probabilistic framework and two algorithms to achieve the optimal accuracy-fairness trade-off in multi-class classification. Empirical results validate their effectiveness in balancing predictive performance with equitable treatment across demographic groups.
MESA: Improving MoE Safety Alignment via Decentralized Expertise
MESA decentralizes safety in MoE LLMs via Optimal Transport, reallocating expertise to mitigate "Safety Sparsity." This approach robustly defends against attacks while preserving model helpfulness.
Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
This study optimizes Wan2.2 video models via joint few-step distillation and low-bit quantization. The approach balances quality and efficiency, outperforming baselines at 8 and 20 steps.
Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty
Upper-face emotional cues enhance audiovisual sentence recognition robustness and model calibration under acoustic noise, despite minimal direct accuracy gains.
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
This study reveals that LLM-based multi-agent systems face diminishing returns due to coordination overhead, not context limits. Optimal performance requires robust base models and task-specific agent counts, challenging the assumption that more agents automatically yield better results.
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
COPF is an online framework ensuring stable counterfactual fairness in dynamic link recommendation systems. It mitigates exposure disparities via graph-aware estimators and multicalibration with minimal utility loss.
The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs
The paper proves outcome-optimized LLMs collapse reasoning via spurious correlations. It shows process supervision acts as a topological filter, forcing models to learn robust causal mechanisms instead of shortcuts.
Shape Your Body: Value Gradients for Multi-Embodiment Robot Design
This study uses frozen, multi-embodiment value functions as differentiable surrogates to optimize robotic designs via value gradients. This approach efficiently refines morphology and identifies performance constraints across diverse robot configurations.
Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation
This paper derives information-theoretic lower bounds for bit-constrained stochastic optimization by reducing it to compressed Gaussian mean estimation. It establishes unconditional communication and statistical limits, validated by a near-matching achievability result.
Multi-Agent Conformal Prediction with Personalized Statistical Validity
PFWCP ensures personalized statistical validity in multi-agent conformal prediction by combining local density weighting with privacy-preserving quantile aggregation. It outperforms existing federated baselines in calibration quality while maintaining strict privacy and asymptotic validity guarantees.
WaveFilter: Enhancing the Long-Context Capability of Diffusion LLMs via Wavelet-Guided KV Cache Filtering
WaveFilter enhances Diffusion LLMs’ long-context performance via training-free wavelet-guided KV cache filtering. It identifies pivotal tokens to build sparse caches, improving efficacy without retraining.
EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models
EPIC accelerates CFG-constrained diffusion language model inference by 67.5% via parallel token commitment and optimized parsing, restoring the efficiency lost in prior methods.
Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment
QTAML uses physics-derived noise models to correct quantum tunneling errors in AI. Its TAC algorithm boosts accuracy with significantly less ECC overhead than baselines.
SORA: Free Second-Order Attacks in Fast Adversarial Training
SORA prevents catastrophic overfitting in fast adversarial training via PertAlign and adaptive step-sizes. It achieves state-of-the-art robustness and clean accuracy with minimal computational overhead.
Causal Density Functions
Causal density functions use Radon-Nikodym derivatives to contrast interventional and observational distributions, enabling pointwise causal effect estimation. This framework allows for empirical testing and scoring of directed influence via reweighted observational expectations.
Logit Distillation on Manifolds: Mapping by Learning
This paper proposes a manifold-based logit distillation method using layer-wise projection and LoRA, reducing student parameters to under 1% while significantly improving word error rates compared to existing techniques.