5013 papers
The paper proposes SFIBA, a spatial-based full-target invisible backdoor attack that ensures trigger specificity and stealthiness in black-box settings by restricting triggers to local spatial regions and employing a frequency-domain injection method, thereby achieving high attack performance while evading existing defenses.
This paper introduces Task 5 of the DCASE 2025 Challenge, a multi-domain Audio Question Answering benchmark designed to evaluate and advance the acoustic reasoning capabilities of audio-language models across diverse scenarios including bioacoustics, temporal soundscapes, and complex real-world clips.
This study of financial professionals using AI reveals that while AI-generated content improves task quality, unsolicited proactive interventions significantly increase extraneous cognitive load—particularly for less experienced users—thereby degrading performance more severely than intrinsic task complexity.
This paper introduces Ready2Unlearn, a proactive, model-agnostic training-time optimization approach that leverages meta-learning principles to prepare machine learning models for efficient and principled future unlearning, shifting the focus from reactive post-deployment algorithms to forward-looking readiness.
FreeKV is a training-free framework that combines speculative retrieval, fine-grained correction, and hybrid CPU-GPU memory management to significantly accelerate KV cache retrieval for large language models, achieving up to a 13× speedup over state-of-the-art methods while maintaining near-lossless accuracy.
This paper proposes a neuro-symbolic framework that enhances the reliability of LLM-generated mathematical proofs in Euclidean geometry by combining analogous problem retrieval with formal verification feedback, resulting in significant accuracy improvements for models like OpenAI's o1.
MAS-ZERO is a novel, self-evolved inference-time framework that automatically designs, critiques, and refines multi-agent system configurations for specific tasks without requiring a validation set, achieving significant performance improvements over manual and existing automatic baselines across reasoning, coding, and agentic benchmarks.
This paper introduces AgarCL, a research platform based on the non-episodic game Agar.io designed to advance continual reinforcement learning by providing a complex, dynamic environment where standard algorithms and existing continual learning methods face significant challenges beyond the traditional stability-plasticity dilemma.
This paper establishes a maximum principle and the Hamilton-Jacobi-Bellman equation for optimal control on infinite-dimensional probability distribution spaces, and leverages these theoretical results to develop a scalable deep learning algorithm for solving high-dimensional multi-agent control problems.
This paper proposes a novel defense against prompt injection attacks in large language models by augmenting intermediate token representations with layer-specific trainable embeddings to enforce instruction hierarchy, achieving a 1.6x to 9.2x reduction in attack success rates compared to state-of-the-art methods without compromising model utility.
This paper proposes Orthogonal Common Neighbor (OCN), a novel link prediction method that addresses redundancy and over-smoothing in higher-order common neighbors through orthogonalization and normalization, achieving significant performance improvements over state-of-the-art baselines.
This paper introduces a novel representation of local protein environments derived from atomistic foundation models that effectively captures structural and chemical features, enabling the construction of data-driven priors and achieving state-of-the-art accuracy in physics-informed NMR chemical shift prediction.
This paper introduces MMTU, a large-scale benchmark comprising over 28,000 questions across 25 real-world expert-level table tasks, designed to comprehensively evaluate and reveal the significant limitations of current frontier models in understanding, reasoning, and manipulating structured tabular data.
BemaGANv2 is an advanced GAN-based vocoder that enhances long-term audio generation for Text-to-Music and Text-to-Audio applications by integrating Anti-aliased Multi-Periodicity composition modules in the generator and systematically evaluating novel discriminator combination strategies, including the Multi-Envelope Discriminator, to achieve high-fidelity and temporally coherent results.
This paper introduces Co-LoRA, a collaborative personalization framework that addresses both data and model heterogeneity through a task-relevance-aware aggregation strategy and a dimension-invariant module, validated by a new multi-modal benchmark and superior performance over state-of-the-art methods.
The paper introduces ContextMatters, a framework that combines Large Language Models with classical planning to dynamically relax goals based on environmental context, significantly improving 3D scene planning success rates and enabling successful real-world robot deployment.
This paper proposes a semi-self-supervised learning approach featuring a novel GLMask representation and a semantic-to-instance pipeline that achieves state-of-the-art instance segmentation performance with minimal manual annotation, demonstrating superior results on both dense agricultural wheat head images and the general-purpose COCO dataset.
This paper introduces SamS, an efficient algorithm that adaptively schedules training samples in Direct Preference Optimization based on the model's evolving batch-wise states, significantly improving LLM alignment performance without modifying the core DPO algorithm or incurring substantial computational overhead.
This paper introduces MeRF, a method that enhances reinforcement finetuning of large reasoning models by injecting reward specifications directly into prompts as "motivation," thereby leveraging in-context learning to align generation with optimization objectives and achieve substantial performance gains over standard RLVR baselines.
The paper proposes SUBARU, a power-efficient framework for hearables that intentionally employs sub-Nyquist sampling and low bit-resolution ADCs to achieve a 3.31x reduction in power consumption while maintaining high-quality multimodal speech enhancement through a novel wideband reconstruction methodology.