All papers

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.

Introduces a benchmark and evaluation to test whether language models can successfully generate coherent nonlinear narratives.

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

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

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.

Introduces an optimization framework that localizes functional subnetworks in language models to enable continual learning without catastrophic forgetting.

Reviews and analyzes reinforcement learning methods used to guide and improve diffusion models across vision, language, and robotics.

The paper investigates the mathematical conditions under which the Muon optimizer's polar update acts as an optimal step.

The authors formulate classification and uncertainty quantification methods for pairwise difference learning frameworks.

The paper compares the Muon optimizer against AdamW across controlled data, wall-clock time, and tuning budgets.

The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines.

The paper proposes a search framework to recover executable editing programs between before-and-after sewing pattern pairs.

The authors introduce a benchmark to audit mathematical claims and repair errors using an automated witness verification checker.

The authors propose an indoor navigation framework that uses user-provided floor plans and topological memory without prior robot site maps.

The paper introduces an open-source dataset and standardized benchmark suite for machine learning methods in analog integrated circuit design.

The paper introduces a speculative decoding framework that uses early recurrent states to draft tokens in looped transformers.

The authors present an interactive environment to evaluate how language models integrate exploration, hypothesis generation, and reasoning.

The paper studies whether predicting actions from consecutive latent states helps world models retain essential environmental dynamics.

The authors introduce an audio model with a non-autoregressive decision head for selection, judgment, and scoring tasks.

The paper evaluates feature encoders and pair selection strategies for pairwise difference learning in low-data classification regimes.

The authors present an engine to synthesize environments, tasks, and rubrics for training and evaluating data analysis agents.

The paper proposes filtering high-drift responses to mitigate off-policy degradation in mixture-of-experts reinforcement learning.

Introduces an in-context learning approach to map specified user preferences directly to Pareto-optimal solutions using offline data.

Proposes a structured knowledge admission and verification system to prevent continual learning agents from storing harmful past experience entries.

Models repeated games with endogenous delegation to analyze trade-offs between economies of scale and institutional accountability.

Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments.

Introduces a benchmark evaluating whether language model agents can independently direct and validate goal-specified physics research investigations.

Introduces a two-stage coarse-to-fine speech separation architecture leveraging visual streams to isolate target speaker audio in noisy environments.

Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations.

Develops a recursive self-improvement framework where an evolving meta-agent adapts agent workflows based on execution trajectories and feedback.

Introduces a causal-beam transfer framework that extracts relevant subtask representations from human videos to guide robotic manipulation.

Proposes a bi-level working and long-term memory framework to balance knowledge retention and acquisition in incremental learning.

Learns instruction-conditioned multimodal representations to dynamically cluster objects based on user-specified partition criteria without fixed cluster counts.

Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively.

Develops pessimistic value-iteration algorithms with theoretical suboptimality bounds for offline risk-sensitive reinforcement learning using optimized certainty equivalents.

Presents a unified framework with synthetic data generation and benchmarks to train models on embodied spatial reasoning and actions.

Develops a solvable topic model theory explaining when sequence models adopt in-context learning rather than specialised memorisation during training.

Equips multimodal vision-language models with video world model operations as tools to support dynamic spatiotemporal visual reasoning.

Analyzes strategic identifiability in multi-agent games, showing when inverse reinforcement learning fails to recover true underlying agent rewards.

Examines whether source code quality metrics predict the token costs of multi-agent pull request review pipelines.

Introduces a framework that aligns interaction regions across objects to improve 3D affordance grounding on unseen objects.

Uses large language models to construct prior trade-off preferences to address cold-start challenges in choice-learning systems.

Presents a theoretical orthogonal polynomial framework explaining why shallow neural networks learn low-frequency sinusoidal components before high frequencies.

The paper pairs language models with symbolic verifiers to test and update reusable hypotheses for environment diagnostics and repair.

The paper aggregates irregular electronic health records into temporal graphs to predict risks of gastrointestinal cancers.

The paper guides knowledge-graph retrieval agents by evaluating the causal impact of evidence on answering questions rather than text similarity.

The paper outlines a long-context midtraining stage using software engineering data to prepare base language models for agentic tasks.

The paper optimizes batched task and motion planning to generate high-quality autonomous demonstrations for robotic manipulation training.

The paper aligns functional brain scans with multimodal representations to decode and retrieve perceived natural speech segments.

The paper uses language model subtask decomposition and dense progress tracking to enhance reinforcement learning on long-horizon tasks.

The paper compresses video history into evolving lightweight model weights instead of competing for limited input context length.

The paper develops a self-distillation approach enabling models to learn from their own unverified alternative reasoning paths without ground-truth answers.

The paper introduces a commercially permissive multilingual instruction dataset covering five European languages to train open language models.

The paper presents a tiny learned policy controller to optimize token unmasking and remasking schedules in diffusion language models.