9630 papers
This paper introduces VIP, a Variance-Informed Predictive allocation strategy that dynamically optimizes rollout distribution across training prompts using Gaussian process-based variance estimation to minimize gradient variance and significantly improve sampling efficiency in online reinforcement learning with verifiable rewards.
This paper introduces the Exploratory and Focused Manipulation (EFM) problem to address visual occlusion in robot manipulation, proposing the EFM-10 benchmark and a Bimanual Active Perception (BAP) strategy that effectively leverages dual-arm coordination for active vision and force sensing.
This paper demonstrates that steering vectors in large language models are fundamentally non-identifiable, as numerous distinct interventions—including orthogonal perturbations—produce behaviorally indistinguishable results, thereby revealing inherent limits in interpreting these vectors as unique internal representations without additional structural constraints.
LatentChem introduces a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning in continuous latent space which spontaneously emerges as a more efficient and accurate alternative to explicit Chain-of-Thought, achieving a 59.88% win rate and 10.84 speedup over baselines.
This paper introduces Topology-Aware PINNs (TAPINN), a novel framework that employs supervised metric regularization and alternating optimization to effectively resolve spectral bias and mode collapse in multi-regime physics-informed neural networks, achieving superior convergence stability and accuracy compared to standard and hypernetwork-based baselines.
This paper empirically demonstrates that while Kolmogorov-Arnold Networks (KANs) can compete with MLPs on simple univariate residuals in hard-constrained recurrent physics-informed architectures, they suffer from severe hyperparameter fragility, instability in deeper configurations, and consistent failure on multiplicative terms, ultimately revealing limitations in their additive inductive bias for modeling state coupling in oscillatory systems.
This paper proposes an explainability-guided active learning framework that improves medical image analysis by strategically selecting samples based on both predictive uncertainty and attention misalignment with expert-defined regions, thereby achieving superior data efficiency and clinical interpretability compared to traditional methods.
Pailitao-VL is a unified multi-modal retrieval system that achieves state-of-the-art, real-time industrial search performance by replacing traditional contrastive embeddings with an absolute ID-recognition paradigm and evolving reranking into a compare-and-calibrate listwise policy, thereby overcoming granularity, noise, and latency challenges in large-scale production environments.
This paper introduces "Zombie Agents," a persistent black-box attack on self-evolving LLM agents that covertly implants payloads into long-term memory during benign sessions to survive across interactions and trigger unauthorized actions in future sessions, demonstrating that current per-session defenses are insufficient against such memory-based compromises.
SubQuad is an end-to-end pipeline that overcomes the computational and data imbalance bottlenecks in large-scale adaptive immune repertoire analysis by integrating near-subquadratic MinHash retrieval, GPU-accelerated affinity kernels, and fairness-constrained clustering to enable scalable, bias-aware discovery of clinically relevant clonotypes.
This paper proposes a three-stage curriculum learning framework that leverages structure-aware masking and Group Relative Policy Optimization (GRPO) to efficiently distill Chain-of-Thought reasoning into compact student models, achieving significant accuracy gains and output length reduction on GSM8K by progressively guiding the model from structural understanding to self-optimized brevity and targeted knowledge internalization.
This paper argues that Schema-Guided Dialogue and the Model Context Protocol converge into a unified paradigm for deterministic LLM-agent interaction, proposing five foundational schema design principles to address critical gaps in failure handling and tool relationships while enabling scalable AI governance.
This paper proposes Decoupled Promptable Sequential Recommendation (DPR), a model-agnostic framework that enables conventional sequential recommenders to dynamically steer retrieval using natural language prompts by modulating latent user representations through a specialized fusion module, Mixture-of-Experts architecture, and a three-stage training strategy, thereby achieving superior performance in intent-driven tasks without sacrificing collaborative filtering efficiency.
This paper introduces a simulation-based clinical red teaming framework that pairs AI psychotherapists with dynamic patient agents to evaluate mental health support systems, revealing critical safety gaps such as the validation of delusions and failure to de-escalate suicide risk in AI agents tested against Alcohol Use Disorder scenarios.
This paper extends kernel density estimation and hierarchical shrinkage to Hoeffding trees for imbalanced regression in data streams, demonstrating that kernel density estimation significantly improves early-stream performance while hierarchical shrinkage offers limited gains.
Zatom-1 is the first open-source, end-to-end foundation model that unifies generative and predictive learning for 3D molecules and materials using a multimodal flow matching objective, achieving state-of-the-art performance across domains while significantly reducing inference time and enabling positive transfer between chemical systems.
This paper proposes an interpretable, multimodal gesture recognition framework that fuses inertial and capacitive sensor data via log-likelihood ratio to enable robust, real-time, hands-free teleoperation of drones and mobile robots, supported by a new dataset and demonstrating performance comparable to vision-based methods with significantly lower computational costs.
This paper proposes a pessimistic auxiliary policy that samples reliable actions by maximizing the lower confidence bound of the Q-function, thereby mitigating out-of-distribution errors and improving the performance of offline reinforcement learning algorithms.
The paper introduces Jailbreak Foundry (JBF), a multi-agent system that automatically translates jailbreak research papers into executable modules within a unified harness, enabling rapid, reproducible, and standardized benchmarking of large language model security against rapidly evolving attack techniques.
DiffusionHarmonizer is an online, single-step generative framework that leverages a custom data curation pipeline to transform imperfect neural reconstruction renderings into temporally consistent, photorealistic simulations, effectively resolving artifacts and harmonizing inserted dynamic objects for autonomous robot development.