All papers

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

Proposes a lightweight protocol for AI agents to report runtime status and liveness to central servers and monitoring dashboards.

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 demonstrates vulnerabilities in unilateral weight obfuscation schemes used for on-device secure language model inference.

The authors present an agent loop that closes functional test coverage in hardware design using independent simulation scoreboards.

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

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 study evaluates using language models to directly reconstruct mobile network schemas versus generating validation code tools.

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 paper formalizes the theoretical limits of automated gates evaluating self-improvement updates in autonomous agent code.

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

The paper uses human interventions to localize and steer computations behind agent failure to decide between acting and clarifying.

The authors evaluate how easily item identities and user profiles can be recovered from language embeddings in recommender systems.

The authors introduce a Fisher-informed adapter merging method to maintain retained task performance during federated language model unlearning.

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.

Investigates how partial interventions in concept-based models can unexpectedly reduce downstream task performance due to information leakage.

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

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.

Investigates how user trust influences delegation and verification choices during human interaction with multimodal video question-answering systems.

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

Analyzes how continual learning methods affect demographic fairness over time across eleven deepfake generation techniques.

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 provides a formal algebraic framework and normal forms to structure and audit enterprise AI agent workflows.

The paper enables multimodal models to proactively ask clarifying questions when ambiguous inputs carry cross-modal safety risks.

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.

The paper extends integrated gradients to trace feature attributions along causal paths for high-dimensional models.

The paper personalizes mixture-of-experts models and analyzes which specialized experts encode distinct user-specific features.

The paper proposes a decoupled per-token scoring method to efficiently manage key-value cache memory during long-context generation.

The paper coordinates projector and language model adaptation using task-specific routing to reduce catastrophic forgetting in multimodal models.

The paper demonstrates that a single adversarial agent can cause benign models in multi-agent discussions to quietly adopt false beliefs.