All papers

The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts.

The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution.

The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens.

The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems.

The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents.

The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness.

The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability.

The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes.

The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning.

The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models.

This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns.

This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models.

This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models.

This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination.

This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks.

This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales.

The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning.

The paper investigates whether machine translation systems preserve or neutralize media bias across languages.

The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory.

The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

The paper proposes a synthetic data pipeline that extracts atomic facts from source texts to guide factual pretraining for language models.

The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems.

The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations.

The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints.

The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions.

The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings.

The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks.

The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles.

The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports.

The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback.

Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding.

Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks.

Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts.

Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models.

Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing.

Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes.

This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints.

This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications.

This paper introduces an open-source multi-agent system designed to generate, verify, and debug hardware description code.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency.

This paper surveys synthetic data generation and data augmentation techniques for machine learning with an emphasis on healthcare.

The paper analyzes training instability cascades in large recommender systems and proposes targeted gradient, weight, and loss interventions.

The paper introduces a multimodal video retrieval system that optimizes temporal paths to locate ordered events across video, text, and audio.

The paper introduces an infrastructure that organizes scientific works as persistent, stateful research objects to maintain continuity across tools.

The paper introduces a framework that uses language models to extract task-scheduling priority functions from optimal solver solutions on directed acyclic graphs.

The paper reviews explainability methods and evaluation metrics in multimodal recommender systems, categorizing approaches into modality and post-hoc explanations.

The paper proposes a federated recommendation adapter that adds bounded semantic residual scores to on-device collaborative filtering models.

The paper uses conditional flow matching during training to regularize matrix factorization embeddings across sparse user interactions in collaborative filtering.

The paper presents a context-coupled graph learning method that jointly models collaborative and multimodal item relations for recommendation systems.

The paper proposes a generative recommendation framework that reconstructs continuous target-side features during autoregressive decoding to improve ranking accuracy.

The paper introduces a wavelet-based integration method to resolve performance degradation caused by feature redundancy in trimodal recommender systems.

The paper distills an LLM-based search relevance judge into smaller multimodal student models for efficient e-commerce search evaluation.

The paper develops a transaction-aware graph method to trace multi-token fund flows and asset transformations across cryptocurrency networks.

The paper studies how pipeline selection and scored populations introduce evaluation blind spots in open-web event admission systems.

The paper proposes a sparse prior-regularized objective that makes popularity bias correction context-dependent during sequential recommender model training.

The paper introduces an optical music recognition dataset and pipeline linking medieval chant manuscript images to musical target tokens.

This paper introduces a tail-aware evaluation method that estimates web-scale image retrieval performance using small random distractor samples.

The paper evaluates the cross-lingual transfer, calibration, and translation sensitivity of fast typed-decision models on regional Indonesian languages.