8799 papers
ImageEdit-R1 is a novel multi-agent framework that employs reinforcement learning to coordinate specialized vision-language and generative agents, enabling dynamic, context-aware image editing that outperforms existing monolithic models and baselines in handling complex, multi-step user instructions.
This paper proposes In-Context Reinforcement Learning (ICRL), a novel framework that eliminates the need for supervised fine-tuning by leveraging few-shot prompting during reinforcement learning rollouts to progressively teach large language models how to effectively use external tools, ultimately achieving state-of-the-art performance in a data-efficient, zero-shot manner.
This paper introduces DSH-Bench, a comprehensive benchmark featuring a hierarchical subject taxonomy, granular difficulty and scenario classification, and a novel Subject Identity Consistency Score (SICS) metric to systematically evaluate and diagnose subject-driven text-to-image generation models.
This paper introduces the Dual-Consensus Weak-to-Strong (DC-W2S) framework, which enhances the reliability of Process Reward Models in biological reasoning by strategically filtering noisy weak supervision signals through self- and neighborhood-consensus metrics to enable robust training without exhaustive expert annotation.
This paper identifies the critical limitation of current LLM-based agents in accessing unindexed information, introduces the first dedicated UIS-QA benchmark to quantify this challenge, and proposes UIS-Digger, a multi-agent framework that significantly outperforms state-of-the-art models by effectively combining dual-mode browsing and file parsing to retrieve vital unindexed data.
SaiVLA-0 introduces a neuroscience-inspired, compute-aware Vision-Language-Action framework featuring a tripartite Cerebrum-Pons-Cerebellum architecture that decouples high-level semantics from real-time control to achieve modular scalability, active foveated vision, and significant improvements in training efficiency and task success rates.
FoleyFlow introduces a novel video-to-audio generation framework that achieves superior semantic and rhythmic synchronization by aligning unimodal encoders through masked audio-visual modeling and employing a dynamic conditional flow that utilizes temporally varying video features to guide audio synthesis.
The paper introduces DARC, a retraining-free, inference-time method that mitigates the brittleness of standard preference alignment by framing response selection as a distributionally robust, risk-sensitive decision-making process to explicitly manage annotator disagreement and tail risk without compromising average quality.
This paper proposes a gradual knowledge excavation framework that enables large language models to iteratively acquire external information and perform logical reasoning, achieving state-of-the-art performance on complex open-domain question answering with significantly fewer parameters.
This paper proposes a novel explainable hybrid deep learning framework combining 1D-CNN and GRU architectures with interpretability techniques like IGs and SHAP to enhance fault detection, diagnosis, and root cause analysis in automotive software system validation while overcoming the limitations of traditional black-box models.
This paper introduces the Condition Insight Agent, a deployed decision-support framework that integrates heterogeneous industrial data sources through constrained, rule-verified LLM reasoning to generate evidence-grounded maintenance explanations and actionable advice while ensuring reliability and human oversight.
This paper introduces ESC, an Evolution Strategy-based calibration method that addresses the unique challenges of audio signal quantization by optimizing activation scaling, thereby achieving near-lossless performance for INT4 and full INT8 quantization across multiple speech tasks.
This paper demonstrates that performing reasoning in a continuous latent space via the CODI framework significantly outperforms standard explicit reasoning in multilingual settings, particularly for low-resource and zero-shot scenarios, while achieving substantial compression of reasoning traces.
This paper reveals that hidden states in end-to-end full-duplex speech models like SALM-Duplex and Moshi significantly leak speaker identity, and proposes two streaming anonymization methods using Stream-Voice-Anon that effectively mitigate this privacy risk while maintaining low-latency dialogue performance.
This paper introduces TildeOpen LLM, a 30-billion-parameter open-weight model that achieves superior performance across 34 European languages, particularly for low-resource groups, by employing curriculum learning and dataset upsampling to address data imbalances without requiring increased computational resources.
This paper proposes MM-TS, a novel framework for multi-modal contrastive learning that dynamically adjusts temperature and margin schedules based on local data distribution to address long-tail imbalances, unifying InfoNCE and max-margin objectives to achieve state-of-the-art performance across multiple image- and video-language datasets.
This paper critiques the reliance of current tabular foundation model benchmarks on point-estimate metrics like MSE, advocating instead for the adoption of proper scoring rules such as CRPS to evaluate probabilistic forecasts and the use of finetuning or promptable strategies to align model inductive biases with distributional regression goals.
This paper proposes two novel fusion strategies, Registration-aware Guided Image Fusion (RGIF) and Reliability-Gated Modality-Attention Fusion (RGMAF), which effectively integrate heterogeneous thermal and visual sensor data to significantly enhance unmanned aerial vehicle detection performance across diverse perspectives and resolutions.
This paper extends the adaptive aggregation method of AsyncFedED by exploring alternative distance metrics to better capture gradient staleness in asynchronous federated learning, demonstrating that specific metrics improve convergence, accuracy, and stability under heterogeneous and non-IID conditions.
SplitAgent introduces a novel distributed architecture that enables privacy-preserving collaboration between enterprise and cloud AI agents by utilizing context-aware dynamic sanitization, differential privacy, and zero-knowledge verification to achieve high task accuracy while significantly reducing data leakage compared to static approaches.