All papers

The paper introduces an adaptive network-slicing orchestration framework for medical IoT applications operating across edge-computing infrastructure.

The paper demonstrates that internal activation states can forecast and block unauthorized actions and deceptive reports by language model agents.

The paper evaluates small language models for text-to-SQL tasks and tests lightweight iterative retry workflows to improve query accuracy.

The paper introduces decision models that balance fast single-pass predictions with adaptive reasoning for uncertain cases using reinforcement learning.

The paper proposes clustering observations by feature importance profiles to incorporate latent factor representations into deep learning models.

The paper presents a minimal coding agent baseline for Lean theorem proving using standard bash tooling instead of specialized harnesses.

The paper proposes a budgeted KV-cache repair technique to restore model performance efficiently after deleting obsolete context tokens.

The paper presents a statistical mechanics framework using modern Hopfield models to analyze inference reliability and phase transitions in Transformer attention.

The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks.

The paper examines how shared KV caches cause language model agents to leak goals across competing clients, offering internal intervention techniques.

The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations.

The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align.

The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics.

The paper introduces a test-time wrapper that distorts exposed output probabilities to defend black-box machine learning models from query-based adversarial attacks.

The authors introduce a topology-aware transformer meta-trained across multiple electricity markets to forecast day-ahead electricity prices for unseen grids.

The authors provide a comprehensive textbook covering reinforcement learning methods, game-playing algorithms, and key historical milestones.

The paper develops a sequential betting approach to reliably detect data distribution shifts in unstructured text and image streams over time.

The authors propose a conditional flow-matching generative model to extend the field of view in diffusion brain MRI scans across scanners.

The paper presents a multi-station transformer model that jointly detects phase arrivals, locations, magnitudes, and associations for earthquake monitoring.

The authors introduce a time-frequency diffusion framework that improves the generation of rare extreme events in time-series data.

The paper argues that software agency should be treated as a core organizing abstraction in computer science rather than merely an AI application.

The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals.

The paper presents a taxonomy that locates agent workflow failures across specific workflow stages and agent functional capabilities.

The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments.

The paper develops a plug-and-play module for convolutional neural networks that provides optimal rotation and translation invariance based on invariant theory.

The authors formulate colon cancer treatment planning as an interactive reinforcement learning task in settings where actions are only partially verifiable.

The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models.

The authors present a parallel decoding method that speeds up on-device tool calling for language model agents without requiring model retraining.

The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices.

The authors design an FPGA implementation of spiking neural networks that performs both inference and training on-chip without backpropagation through time.

The paper evaluates seven machine learning and graph models on detecting misinformation within social media noise from conflict-related tweets.

The authors introduce a framework to track safety model judgments through output parsing to prevent malformed responses from causing bad decisions.

The paper presents a neurosymbolic framework combining query plan checks and candidate selection to improve text-to-SQL translation for financial governance.

The paper presents an in-context foundation model architecture for next-event prediction across diverse operational event logs.

The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras.

The paper introduces a dual-stage robust aggregation defense against data and model poisoning attacks in federated financial fraud detection.

The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models.

The paper evaluates the energy efficiency, carbon intensity, and accuracy of ten post-training quantization methods across various language models and GPUs.

The paper proposes a vision-language-action model where action execution avoids inference latency by decoupling from explicit reasoning output at runtime.

The paper presents a convex-analytic framework to select query directions for safety verification in Markov decision processes.

The paper analyzes the failure modes of agent memory reuse and evaluates certified memory update strategies when facts change.

The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks.

The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation.

The paper analyzes why dynamic graph neural networks fail on evolving nodes and introduces a change-aware classification framework.

The paper introduces an action-conditioned world model that separates observation representation learning from intervention learning for mobile network energy control.

The paper applies Hilbert geometry to constrain activation shifts in softmax layers for stable optimizer updates in language model heads.

The paper re-evaluates the pretraining compute scaling laws between diffusion and autoregressive language models using modern diffusion training designs.

The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images.

The paper proposes a training-free framework that co-evolves an executable verifier program and harness state to self-improve agents on open-ended tasks.

The paper presents a monocular depth estimation method that uses image editing priors to improve geometric consistency under environmental perturbations.

The paper presents a unified node-centric framework to quantify and manage multi-agent system lifecycles across design, runtime, and post-hoc attribution.

The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization.

The paper introduces a 4D latent cache to reuse static and dynamic visual representations for efficient conditioning in video generation models.

Presents SADDER, an inference-time text diffusion safety framework using directional embedding signals to steer unsafe tokens toward benign distributions.

Evaluates enterprise policy enforcement across structured JSON versus single-token outputs, measuring the impact of explicit reasoning on decision stability.

Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning.

Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics.

Introduces EncounterMob, an LLM-based framework that generates human mobility trajectories while preserving both individual routines and multi-person encounter patterns.

Proposes Dynamic K-Head, a question-conditioned budget selector that sets continuous latent reasoning steps based on input difficulty.

Proposes Token Window Pacing, a serverless scheduling framework that regulates request admissions at the token level to reduce inter-token latency.