All papers

The paper introduces a system layer that separates control flow from untrusted data to protect language model agents against prompt injection attacks.

The paper proposes an interpretable framework using brain imaging signals to monitor emotional states in children during robot-assisted therapy.

The paper introduces a multimodal dataset combining physiological sensor signals and video recordings to study human social stress.

The paper presents a multimodal deep learning method using PET and CT scans to predict brain metastasis in non-small cell lung cancer.

The summary text is too thin to judge, containing mostly page navigation text instead of the research abstract.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

The publication compiles the proceedings of the first international workshop on harness engineering for generative user interfaces.

The paper compares traditional cloud encryption methods with artificial intelligence based encryption techniques across several performance metrics.

The paper outlines a vision for trace-guided automated program repair to address context bloat and reasoning limitations.

The paper outlines a vision for trace-guided automated program repair to address context bloat and reasoning limitations.

The paper proposes a persistent homology metric to evaluate the topological and structural stability of time-varying social networks.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper introduces an open-source library to construct validation suites that stress test the reliability of LLM judges.

The paper provides a conceptual taxonomy and review distinguishing task-specific AI agents from collaborative agentic AI systems.

The paper provides a conceptual taxonomy and review distinguishing task-specific AI agents from collaborative agentic AI systems.

Examines scanning infrared depolarization measurements for defect monitoring and quality control across semiconductor wafer processing stages.

Reviews forty years of automated driving system research, focusing on safety assurance and verification challenges.

Summary contains IEEE Xplore webpage boilerplate rather than paper content on command and control for unmanned vehicles.

Presents an empirical comparison evaluating usability, reliability, and CI/CD integration challenges of AI-based unit testing tools.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Presents a web platform using GPT-4 and Whisper to deliver interactive interview practice and automated feedback.

Profiles power oscillations and voltage droops in cloud GPUs caused by LLM inference workloads and introduces a stressmark framework.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.