All papers

The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose.

The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches.

The paper shows that LLM review pipelines rarely outperform the stronger model alone due to confirmation bias rather than true verification.

The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation.

The paper presents a phase-annotated synthetic conversational corpus generated using a retrieval-augmented pipeline to study online grooming of minors.

The paper presents an unsupervised pretraining method that uses the multiscale structure of jet formation for high-energy physics data.

The paper presents a LightGBM architecture for multi-horizon queue forecasting and resource-aware decision support in border control systems.

The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces.

The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops.

The paper combines static software scanning with hardware-level microprocessor monitoring and neural architecture search to detect ransomware in real time.

The paper introduces a navigation framework and benchmark using depth and spatial audio for obstacle avoidance in unlit industrial environments.

The paper presents an evolutionary search method that generates small, high-quality test suites to verify code rewrites in self-evolving agents.

The paper uses language models to generate semantically diverse agent policies in code to train reinforcement learning cooperators for zero-shot collaboration.

The paper evaluates model handoff points in multi-turn routing workflows, analyzing trade-offs between initial model selection, mid-task switching, and operational cost.

The paper evaluates whether language models can generate accurate behavioral association patterns for epidemiological modeling using external survey and agent-based benchmarks.

The paper provides theoretical mean-square error guarantees and bounds for layer-wise quantization error compensation across linear and non-linear networks.

The paper introduces an optimization method that separates error identification from credit assignment to improve reinforcement learning for tool-using agents.

The paper presents an optimal transport flow-matching framework to model concurrent, multi-interest user behavior in multimodal sequential recommendation systems.

The paper models autoregressive decoding errors as a martingale process to analyze the stochastic dynamics of language model generation.

The paper proposes a framework to transfer personality assessment across distinct asynchronous video interview protocols using construct alignment and language model raters.

This paper evaluates methods for forecasting total hospital respiratory burden by comparing direct combined prediction against summing individual pathogen models.

This paper proposes a coevolution framework where language model agents and domain foundation models iteratively train and improve each other.

This paper models tacit versus explicit knowledge in scientific discovery as distinct operational regimes in content-addressable memory hardware.

This paper develops SatPV-Field to forecast distributed solar generation by converting fleet measurements into spatial fields with satellite guidance.

This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models.

This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs.

This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems.

This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene.

This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data.

This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them.

This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery.

This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification.

This paper introduces Choice2Seq, a benchmark measuring how often language models retain correct multiple-choice answers when generating them step by step.

This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics.

Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms.

Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator.

Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation.

Presents a framework for developing language model facilitators that balance group dynamics, psychological safety, and task goals across multi-party conversations.

Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models.

Proposes reflected proximal dynamics to combine exact target preservation with momentum-based acceleration in log-concave sampling.

Measures teacher entity familiarity to filter out uninformative search trajectories when distilling deep-search capabilities into smaller agents.

Introduces a tiered benchmark to assess how explicit and implicit visual evidence helps speech recognition models resolve ambiguous words on screens.

Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models.

Surveys the use of flow-matching generative modeling approaches within reinforcement learning across policy parameterization and foundation model alignment.

Extracts reusable local control signals from frontier model traces to guide smaller language model agents without extra target-model training.

Expands a benchmark to evaluate whether language model agents can actively select experiments to discover reliable model scaling laws under compute budgets.

Introduces an INT8 integer-only training algorithm using composable building blocks that removes floating-point arithmetic across all training operations.

Proposes an attention-free language model architecture separating drafting from multi-scale prefix revision to bypass sequence-level quadratic attention costs.

Presents a multi-agent framework that allocates test-time computation only to unresolved steps for video spatial reasoning tasks.

Introduces a 35B model, trajectory pipeline, and benchmark unifying search and browser interaction within a single web agent.

Combines next-token prediction transformers with deep clustering to discover interpretable behavioral patterns in open-ended game trajectories.

Presents a breast cancer risk prediction framework that explicitly aligns longitudinal examinations across multiple mammography views.

Introduces a training-free Bayesian state-update framework to reduce error accumulation and representational drift in streaming 3D reconstruction models.

Proposes a world-centric rotary positional embedding using 3D Gaussian primitives to improve cross-view attention in multi-view vision tasks.

Introduces an adaptive multi-agent debate framework that uses a leader-supporter structure to trigger debate only when simple aggregation is insufficient.

Presents an architecture separating natural language interpretation from numerical time series foundation models for instruction-driven forecasting.

Introduces a margin-based analysis metric to evaluate class boundary representations in forward-forward learning algorithms.

Applies a supervised variational autoencoder to brain connectome data for sex classification in young adults.

Introduces internal predictive registry slots into diffusion transformers to explicitly track physical dynamics in world models.

Presents a loop-aware post-training quantization method to compress looped language models without severe accuracy loss.