11865 papers
This paper introduces a gradient-based optimization method for generating time series counterfactual explanations that ensures plausibility by integrating soft-DTW alignment with k-nearest neighbors, resulting in valid and temporally realistic outputs that outperform existing approaches in distributional alignment.
This chapter reviews recent computational models demonstrating that self-supervised and visually grounded learning principles can effectively explain early language acquisition from acoustic and audiovisual speech without relying on strong linguistic priors.
The paper proposes M3-ACE, a multi-agentic context engineering framework that rectifies inaccurate visual perception in multimodal math reasoning by decoupling perception from reasoning and employing collaborative agents with specialized tools to dynamically refine visual evidence, thereby achieving state-of-the-art performance on benchmarks like MathVision.
This paper proposes the Hierarchical Error-Corrective Graph Framework (HECG) for autonomous agents, which integrates Multi-Dimensional Transferable Strategy (MDTS) for precise candidate selection, Error Matrix Classification (EMC) for structured failure attribution, and Causal-Context Graph Retrieval (CCGR) for enhanced contextual reasoning to improve execution reliability in complex, multi-step tasks.
This paper introduces Token-Conditioned Reinforcement Learning (ToCoRL), a framework that leverages the intrinsic behavioral plasticity of Large Language Models to internalize and stabilize inference-time adaptations, enabling precise control over behavioral modes like switching from reasoning to direct answering without degrading overall capabilities.
This paper identifies value-scale amplification as the primary cause of instability in non-linear value decomposition for offline multi-agent reinforcement learning and proposes a scale-invariant value normalization technique to stabilize training, ultimately providing a practical recipe to unlock the full potential of offline MARL.
This paper challenges the stability of human and AI preferences in Reinforcement Learning from Human Feedback (RLHF) by demonstrating that both are susceptible to "choice blindness," where preferences are easily manipulated by context and shallow cues, leading to undetected reward signal corruption and downstream policy degradation.
This paper introduces Geometrically Constrained Outlier Synthesis (GCOS), a training-time framework that generates virtual outliers in the feature space by respecting in-distribution manifold structures and using conformal shells to improve out-of-distribution detection robustness and provide formal error guarantees.
This paper proposes an AI-driven perception method with hierarchical human intention prediction to enable mobile robot chemists in self-driving laboratories to proactively distinguish between human preparatory actions and transient interactions, thereby overcoming the inefficiencies of passive obstruction detection and streamlining human-robot coordination in shared-access scenarios.
The paper introduces SYNAPSE, a systematic, training-free framework that analyzes and stress-tests Transformer models by extracting layer representations and applying forward-hook interventions to reveal domain-independent internal organization, functional stability through redundant neuron subsets, and specific vulnerabilities to small manipulations.
This paper introduces IronEngine, a general AI assistant platform featuring a unified orchestration core and a three-phase pipeline that integrates diverse backends, adaptive memory, and extensive tooling to achieve high task completion rates while separating planning quality from execution capability.
This paper proposes a method to equip LLM agents with native retrieval capabilities by projecting their hidden states directly into the embedding space via a lightweight head, thereby eliminating the need for a separate embedding model while retaining 97% of baseline retrieval quality.
This paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) framework that dynamically switches between exact and approximate evaluation modes within an Online Scheduling Algorithm to efficiently solve the Uncertain Agile Earth Observation Satellite Scheduling Problem, achieving significant computational cost reductions while maintaining superior scheduling performance compared to existing methods.
This prospective feasibility study demonstrates that a conversational AI system (AMIE) can safely and effectively conduct clinical history-taking and generate diagnostic suggestions in a real-world urgent care setting, achieving high patient satisfaction and diagnostic accuracy comparable to primary care providers while requiring no real-time human intervention.
LycheeCluster is a novel KV cache management method that employs structure-aware chunking and hierarchical indexing to transform cache retrieval into a logarithmic-time process, achieving up to a 3.6x inference speedup with minimal performance degradation for long-context LLMs.
This paper investigates world model-based anomaly detection under gradual observation drift, revealing a universal sharp detection threshold that depends on the interaction between detector sensitivity, noise floor, and environment-specific dynamics, while identifying critical failure modes such as the undetectability of sinusoidal drift and agent collapse prior to detection.
The paper proposes R2F, an LLM-free framework for zero-shot open-vocabulary object navigation that repurposes ray frontiers as direction-conditioned semantic hypotheses to achieve competitive performance with real-time execution, eliminating the latency and computational overhead of iterative large-model queries.
This paper proposes X-AVDT, a robust deepfake detector that leverages internal audio-visual cross-attention cues accessed via DDIM inversion to achieve superior generalization across diverse and evolving synthesis paradigms, supported by the introduction of the new MMDF dataset.
This paper proposes Visual Self-Fulfilling Alignment (VSFA), a label-free fine-tuning method that shapes safety-oriented personas in multimodal large language models by exposing them to threat-related images during neutral VQA tasks, thereby reducing attack success rates and mitigating over-refusal without compromising general capabilities.
This paper proposes an operator-theoretic framework that encodes structural constraints via self-adjoint operators to unify gradient projection, spectral compression, and multi-objective feasibility under a single geometric structure, revealing how constraints distort ascent geometry and concentrate effective dynamics along dominant spectral modes.