964 papers
This review proposes an integrative model of language comprehension that explains how speaker effects arise from the dynamic interplay between bottom-up acoustic perception and top-down social expectations, distinguishing between individual familiarity and demographic biases while highlighting the model's relevance for understanding language development and human-AI interaction.
This paper introduces Llama-Mob, an instruction-tuned Llama-3-8B model that outperforms state-of-the-art methods in long-term, city-scale human mobility prediction and demonstrates strong zero-shot generalization across different urban environments.
The paper introduces OfficeQA Pro, a challenging enterprise benchmark using a massive corpus of U.S. Treasury Bulletins to demonstrate that current frontier AI agents struggle significantly with grounded, multi-document reasoning, achieving low accuracy even with direct document access and benefiting notably from structured document representations.
This paper introduces VET-Bench, a diagnostic benchmark revealing that current Vision-Language Models fail at tracking visually identical objects due to an over-reliance on static features, and proposes Spatiotemporal Grounded Chain-of-Thought (SGCoT) to achieve over 90% accuracy by explicitly generating object trajectories as intermediate reasoning steps.
The paper introduces Sandpiper, a mixed-initiative system that integrates interactive researcher dashboards with agentic LLMs to enable scalable, privacy-preserving, and rigorous qualitative analysis of large-scale educational discourse while mitigating hallucinations and ensuring methodological consistency.
This paper introduces SlowBA, a novel backdoor attack against VLM-based GUI agents that utilizes a two-stage reward-level injection strategy and realistic pop-up triggers to induce excessive reasoning chains, thereby significantly increasing response latency while maintaining task accuracy and evading existing defenses.
This paper proposes a zero-AV-resource framework for audiovisual speech recognition that generates synthetic talking-head videos by lip-syncing static facial images with real audio, successfully enabling high-performance model training for under-resourced languages like Catalan without the need for labeled video corpora.
This paper introduces the Cross-Lingual Transfer Matrix (CLTM) to systematically quantify language-dependent performance variations in paralinguistic tasks like gender identification and speaker verification, revealing that despite their acoustic nature, these tasks exhibit distinct cross-lingual transfer patterns when using multilingual HuBERT-based encoders.
DualTurn is a dual-channel generative speech model that learns natural turn-taking dynamics through unsupervised pretraining on conversational audio and fine-tuning to predict agent actions, outperforming existing methods in both action prediction accuracy and turn-boundary anticipation while enabling tool-calling capabilities.
The paper introduces SynPlanResearch-R1, a framework that synthesizes tool-use trajectories to encourage deeper exploration during supervised fine-tuning, thereby overcoming the limitations of reinforcement learning with verifiable rewards and significantly improving research agent performance across multiple benchmarks.
The paper introduces DistillGuard, a framework that systematically evaluates output-level defenses against LLM knowledge distillation and finds that most current approaches are largely ineffective, with performance degradation being highly task-dependent and insufficient to broadly prevent knowledge theft.
ArcLight is a lightweight LLM inference architecture designed specifically for many-core CPUs that overcomes cross-NUMA memory access bottlenecks through efficient memory management, thread scheduling, and controlled tensor parallelism, achieving up to 46% higher throughput than mainstream frameworks while maintaining broad device compatibility.
To address the "spatial intelligence gap" where Vision-Language Models struggle with elementary 3D tasks despite strong logical reasoning, the paper introduces 3ViewSense, a framework that leverages an engineering-inspired "Simulate-and-Reason" mechanism to ground spatial understanding in orthographic views, significantly improving performance on occlusion-heavy counting and view-consistent reasoning benchmarks.
This paper evaluates the capabilities of various large language models, including Llama-3 and ChatGPT, in solving diverse discrete optimization problems using natural language datasets, revealing that while stronger models generally perform better, Chain-of-Thought reasoning is not universally effective and data augmentation can improve performance on simpler tasks despite introducing instability.
KCoEvo is a knowledge graph-augmented framework that addresses the challenges of API-driven code evolution by decomposing migration into path retrieval and informed generation stages, significantly improving accuracy and execution success over standard LLM baselines through structured reasoning and synthetic supervision.
This paper provides a comprehensive technical survey of the history and evolution of image generation models, detailing the objectives, architectures, and limitations of various approaches from VAEs to diffusion methods, while also addressing recent advancements in video generation and the critical challenges of robustness and responsible deployment.
This paper introduces AQuA, a fine-grained dataset that categorizes ambiguous visual questions into four levels with corresponding optimal response strategies, demonstrating that fine-tuning Vision-Language Models on this dataset enables them to effectively recognize ambiguity and adaptively generate context-appropriate responses such as seeking clarification or listing alternatives, thereby outperforming existing baselines.
This Systematization of Knowledge (SoK) paper establishes the first unified framework for Agentic Retrieval-Augmented Generation (RAG) by formalizing autonomous loops as decision-making processes, proposing a comprehensive taxonomy and architectural decomposition, critiquing current evaluation limitations and systemic risks, and outlining critical research directions for building reliable and scalable agentic systems.
This paper proposes a "third ambition" for artificial intelligence research, advocating for the use of large language models as scientific instruments to study human behavior, culture, and moral reasoning by treating them as computationally accessible condensates of collective discourse while addressing their methodological and epistemic limitations.
This paper introduces DCTR, a table retrieval mechanism that leverages fine-grained typed query decomposition and global connectivity awareness to effectively handle complex, open-domain question answering over relational databases, demonstrating robustness on industry-aligned benchmarks.