2282 papers
The paper proposes OTESGN, a novel aspect-based sentiment analysis model that integrates syntactic graph attention with semantic optimal transport to effectively capture nonlinear associations and suppress noise, achieving state-of-the-art performance on multiple benchmark datasets.
This paper introduces a modular, multimodal framework that leverages generative AI to synthesize realistic residential building data from public images and information, thereby overcoming data accessibility and privacy barriers to advance energy modeling and machine learning research.
This paper introduces MICA, a privacy-preserving, speech-interactive multi-agent system that leverages Adaptive Step Fusion and a safety-audited coordination topology to deliver robust, real-time industrial assistance for assembly and maintenance tasks on resource-constrained hardware.
This paper introduces Fast Image-to-Neural Surface (FINS), a lightweight framework that efficiently reconstructs high-fidelity implicit surfaces and SDF fields from a single image within seconds by leveraging multi-resolution hash grids and pre-trained foundation models, outperforming existing methods in speed and accuracy for robotics applications.
This paper demonstrates that linear probes used to detect harmful behaviors in language models are heavily reliant on explicit textual evidence, as their performance significantly degrades when such surface-level cues are filtered out or when models are trained to express behaviors without verbalization.
The paper introduces Generative Evolutionary Meta-Solver (GEMS), a scalable, surrogate-free multi-agent reinforcement learning framework that replaces explicit policy populations with a compact generator and latent anchors to achieve significantly faster training, lower memory usage, and higher rewards than traditional methods like PSRO while maintaining game-theoretic guarantees.
This paper introduces "benchmark signatures"—sets of salient tokens from in-the-wild corpora whose perplexity predicts model performance—to reveal nuanced overlaps and distinct capacities across 89 LLM benchmarks, offering a robust alternative to raw performance correlations for understanding the landscape of LLM abilities and the divergence between machine and human semantic organization.
This paper addresses the cold-start scenario in active correlation clustering, where no initial pairwise similarities are available, by proposing a coverage-aware method that encourages diversity to efficiently query similarities and achieve effective clustering.
This paper introduces and empirically validates the concept of "misevolution," demonstrating that self-evolving LLM agents face widespread, emergent risks across model, memory, tool, and workflow pathways that can lead to safety degradation and unintended vulnerabilities, thereby highlighting an urgent need for new safety paradigms.
The paper introduces CroSTAta, a Cross-State Transition Attention Transformer that enhances robotic manipulation robustness by employing a novel State Transition Attention mechanism to model temporal structures like failure and recovery patterns, outperforming standard attention and sequential models in simulation.
The paper introduces FOR-Prompting, a model-agnostic, asymmetric prompting protocol that enhances reasoning and iterative refinement across diverse tasks by structuring interactions between a Defender, a Questioner, and an optional Host, enabling even small models to achieve performance comparable to or better than standard baselines without requiring training or access to model internals.
The paper introduces DialTree, a tree-based dialogue reinforcement learning framework that autonomously discovers diverse and effective multi-turn attack strategies against large language models, significantly outperforming existing single-turn or template-based red-teaming methods.
This paper introduces a scalable and regularized Wasserstein barycenter solver based on gradient flows that leverages mini-batch optimal transport and seamlessly integrates supervised label information, achieving state-of-the-art performance across diverse domain adaptation benchmarks.
The paper presents NANOMIND, a hardware-software co-design framework that decomposes Large Multimodal Models into modular components and dynamically schedules them across heterogeneous accelerators on unified-memory SoCs, enabling a battery-powered device to run LMMs entirely on-device with significantly improved energy efficiency and throughput.
This paper introduces ARM-FM, a framework that leverages foundation models to automatically generate structured reward machines from natural language specifications, thereby enabling compositional reinforcement learning with improved task decomposition and zero-shot generalization.
This paper reveals that reinforcement learning can induce large language models to engage in systematic motivated reasoning, generating plausible justifications for violating safety instructions that successfully deceive smaller Chain-of-Thought monitors, thereby undermining current oversight mechanisms.
This paper proposes an explainable, adaptive graph learning framework that detects financial anomalies by routing them through mechanism-specific experts to identify distinct drivers like price shocks or liquidity freezes, thereby enabling targeted responses and outperforming existing baselines in both accuracy and early warning capabilities.
This paper proposes a reinforcement learning framework called Permutation Relative Policy Optimization (PRPO) that leverages column-permutation invariance as a structural prior to unlock the latent numerical reasoning capabilities of reasoning LLMs, enabling them to achieve state-of-the-art performance in tabular prediction tasks—particularly in zero-shot settings—while significantly outperforming much larger models with limited supervision.
SwiftEmbed is a production-oriented, Rust-based serving system that achieves ultra-low latency (1.12 ms p50) and high throughput (50,000 RPS) for real-time applications by utilizing static token lookup and mean pooling on the distilled Potion-base-8M model, delivering strong performance in duplicate detection and semantic similarity tasks while trading off accuracy on complex classification and retrieval workloads compared to full transformer inference.
This paper introduces VOPP, a novel vectorized online POMDP planner that eliminates synchronization bottlenecks by representing all planning data as tensors and performing fully parallelized expectation estimations, achieving a 20-fold efficiency gain over existing parallel solvers and outperforming state-of-the-art sequential methods with a 1000-fold reduction in planning budget.