All papers

Investigates cost-aware policies for adaptively switching between language models of varying fidelity in stochastic optimization pipelines.

Presents a framework that decouples speech generation and synthesis token spaces to speed up spoken dialogue model inference.

Introduces a reinforcement learning framework that evaluates memory operations in agents using a shared state and process rewards.

Develops a structured explainable reasoning architecture to extract sensitive self-disclosures from cybersecurity interviews using local small language models.

Proposes a graph-guided synthetic data generation framework with controlled difficulty levels for training multi-hop research agents.

Introduces a localized code repair framework that evolves language model memory architectures using fine-grained execution feedback.

Analyzes working memory error patterns in language models to show that failures stem from readout interference rather than target retrieval.

Presents a test-time method that extracts structured schemas from corpora to guide generative models toward more diverse outputs.

Proposes a lossy compression framework for federated learning that balances user privacy against model utility on resource-constrained devices.

Introduces a condition-contrastive distillation approach to prevent student language models from learning erroneous teacher continuations.

Studies whether conversational agents correctly enforce authorization rules when users attempt to cancel prior agent commitments to others.

Presents an autonomous supervisor agent that iteratively tunes an agent's code, harness, and prompt on few examples.

Introduces a two-stage model merging method to enable dynamic multi-objective recommendation trade-offs at inference time without retraining.

Analyzes how prompt variations redistribute internal feature activations in sparse autoencoders and traces their impact on downstream model decisions.

Proposes a decentralized coordination mechanism using commitment tracking to preserve clear accountability across multi-agent delegation chains.

Proposes a fractional power-of-two quantization grid to enable efficient multiplier-free integer inference for large language models.

Introduces a metric to measure how non-linearly features learned by sparse autoencoders interact during downstream model behaviors.

Introduces a multimodal harness and benchmark pairing continuous video with clinical records to assist patient monitoring in hospitals.

Proposes a trajectory-aware retrieval method that indexes tool sequences in a trie to cut token costs and improve selection accuracy.

Presents an agentic system with human-governed adaptation to automate root-cause analysis across complex, large-scale infrastructure environments.

The paper presents an agent system that generates, edits, and renders interactive virtual worlds from text using executable code rules.

The paper explores whether measuring information density across reasoning steps can predict and guide answer correctness in large language models.

The paper proposes a test-time aggregation method using bias control and injection to resolve disagreements among reasoning language model agents.

The paper applies heterogeneous graph neural networks directly to bank relational schemas to detect money-mule accounts prior to flag dates.

The paper examines how increasing visual token budgets and cropping key evidence improves document retrieval-augmented generation in vision-language models.

The paper evaluates a constrained multi-agent reinforcement learning method that learns policies inside world models to reduce environment interaction costs.

The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents.

The paper introduces a method that accelerates on-policy knowledge distillation by truncating student rollouts and applying inverse-probability weighting.

The paper compares quantum and classical machine learning models on multi-cohort fMRI datasets to test their utility for neuropsychiatric classification.

The paper proposes a federated learning framework that uses version control operations instead of full document aggregation to evolve agent skills.

The paper introduces a hierarchical graph convolutional network and dataset to detect internal inconsistencies and headline mismatches in news documents.

The paper introduces an in-context memory method storing latent transitions between visual observations to assist robot manipulation without extra annotation.

The paper introduces a system that retrieves document information into a fixed set of prompt embeddings for frozen language models.

The paper proposes an error-correction method to prioritize and transmit key-value cache tokens across edge-deployed multi-agent systems under latency deadlines.

The paper introduces a reinforcement learning method that uses internal certainty profiles and selective filtering to train models without external verifiers.

The paper presents a self-guided reasoning framework where language models generate code to detect and diagnose errors in vehicle routing problem instances.

The paper introduces an authorization system that reuses validated consequence evidence across agent execution branches while maintaining separate live authority.

The paper studies combining classical and quantum representations to determine when multi-view fusion improves machine learning predictions over single representations.

The paper analyzes multi-reward reinforcement learning failures where auxiliary objectives degrade primary performance and introduces a trust-anchored gating method.

The paper proposes a framework that replaces generative model calls with lightweight decision models for routing and reviewing in multi-agent workflows.

Presents methods for query-guided video grounding across object localization, relationship detection, and temporal moment identification.

Creates a structured schema that converts research papers into connected scientific entities to improve retrieval-augmented generation for autonomous agents.

Introduces a distillation method that identifies critical sub-spans in student agent trajectories for teacher revision and continual learning.

Provides theoretical convergence guarantees and finite-sample bounds for advantage-based value sharing in reinforcement learning algorithms.

Integrates reinforcement learning and tabular model guidance to help language models select features and predict emergency department clinical risk.

Proposes a training framework enabling aerial vision-language agents to navigate in full action chunks and stop reliably at destinations.

Presents a full-duplex conversational model that generates real-time speech alongside synchronized facial expressions and body gestures.

Investigates whether training multimodal models on visual generation tasks transfers accessible factual knowledge into text question answering.

Introduces a framework where a small model drafts tool-use trajectories and a larger model verifies them in single prefill passes.

Combines domain embeddings with language models to detect and repair incorrect subsumption axioms in industrial knowledge ontologies.

Uses tool-augmented language models to extract clinical variables from patient notes and reliably execute deterministic medical calculation formulas.

Surveys the application of artificial intelligence methods to humanitarian disaster response and displacement monitoring.

Extracts attention statistics directly during fast attention computation to build lightweight hallucination detectors for language models.

Presents a training framework that teaches language model agents to transfer reusable problem-solving experience across structured task families.

Combines language model dialogue with workflow automation to process satellite imagery for internal solitary wave detection and forecasting.

Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations.

Improves deep search agents by refining search queries and filtering evidence to align retrieval with multi-step reasoning goals.

Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension.

Studies a hierarchical conditioning architecture that predicts acoustic scenes directly from intermediate audio event probabilities.

Presents an automated framework using language models to manage storytelling, rules, and combat in tabletop role-playing games.