12214 papers
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.
The paper proposes Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations by aligning them with multi-view echocardiography data to overcome the limitations of single-view alignment, thereby enabling accurate prediction of cardiac morphological phenotypes and retrieval of similar echo studies with a compact model size.
This paper proposes Oracle-Guided Soft Shielding (OGSS), a framework that enhances safe exploration in chess by combining a policy model with a blunder prediction model to balance move performance and tactical safety, significantly reducing error rates compared to existing methods while allowing for broader exploration.
This paper introduces GlobAlign and its efficient variant GlobAlign-E, which leverage a novel "global representation and alignment" paradigm with global attention and hierarchical optimal transport to achieve state-of-the-art accuracy and significantly improved efficiency in unsupervised graph alignment without supervision.
RetroAgent is an online reinforcement learning framework that enables LLM-based agents to evolve through a hindsight self-reflection mechanism generating dual intrinsic feedback—numerical progress tracking and retrievable language lessons via a novel SimUtil-UCB strategy—thereby achieving state-of-the-art performance and superior generalization on complex interactive tasks compared to existing methods.
This paper introduces OSS-CRS, an open-source, locally deployable framework that liberates DARPA's AIxCC cyber reasoning systems from obsolete competition infrastructure, enabling their practical application to discover and patch vulnerabilities in real-world open-source projects, as demonstrated by the successful porting of the first-place Atlantis system to find 10 new bugs.
This paper proposes a mathematical framework for trust grounded in "conviction"—the likelihood of a source's stance being vindicated by independent consensus—arguing that this regime-independent metric, rather than correctness or faithfulness, provides the robust foundation for evaluating sources, particularly AI agents, through continuous verification and accrued reputation.
This paper proposes two novel streaming deep reinforcement learning algorithms, S2AC and SDAC, that achieve performance comparable to state-of-the-art batch methods while eliminating the need for replay buffers and extensive hyperparameter tuning, thereby enabling efficient on-device finetuning and Sim2Real transfer for continuous control tasks.
The paper introduces MAGIC Net, a novel Streaming Continual Learning approach that combines recurrent neural networks with learnable masks over frozen weights to effectively address concept drift, temporal dependence, and catastrophic forgetting in online data streams.