All papers

The authors formulate a stochastic model to analyze the timing distributions and detection limits of potential recursive artificial intelligence explosions.

The authors present an energy-based prior for cellular perturbation models to better distinguish specific therapeutic signals from general biological background noise.

The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors.

The authors analyze task-evoked eye movements in adolescents to identify objective behavioral markers corresponding to depression severity scores.

The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations.

The authors introduce a benchmark evaluating egocentric video understanding structured around an interactive loop of user perception, action, and world feedback.

The authors introduce a challenge benchmark to develop foundation models for building HVAC energy optimization that transfer across different physical structures.

The paper evaluates how commercial language models value human time by measuring recommended delay compensation amounts across 193 different countries.

The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining.

The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication.

The paper defines an algebraic symmetry framework for characterizing structural agent heterogeneity and policy representations in multi-agent systems.

The paper presents a test-time search method that perturbs input conditions to generate diverse reasoning paths in recursive reasoning models.

The paper proposes a query router that balances answer utility, security risks, and token budgets when selecting language models for mixed enterprise workloads.

The paper presents an empirical study of 654 real-world configuration bugs across ten retrieval-augmented generation systems, categorizing causes and fixes.

The paper analyzes pathwise distance consistency in visual world models to avoid candidate order reversals during latent short-horizon planning.

The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution.

The paper theoretically analyzes multi-objective best-arm identification to determine when multiple criteria reduce exploration requirements in multi-armed bandit settings.

The paper introduces a logit adjustment method based on pairwise comparisons between sampled reasoning paths to improve test-time answer selection in language models.

The paper presents a multimodal emotion recognition framework that integrates Euclidean and hyperbolic geometry to capture hierarchical emotion structures from text and speech.

The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks.

The paper presents a generative audio method that controls Foley sound synthesis using physical interaction, material, and geometry parameters.

The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data.

The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass.

The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning.

The paper introduces an imperceptible image perturbation attack that manipulates alignment in vision-language models during preference optimization without altering text annotations.

The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models.

The paper presents an automated framework to evaluate how adversarial contexts can redirect program execution through valid structured judgments in language-model applications.

The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks.

The authors compare information precision and preference reweighting mechanisms in active-inference agents managing competing survival needs.

The paper characterizes optimal falsification-proof non-market allocation mechanisms when individuals can strategically manipulate their observable characteristics.

This paper presents a post-hoc method that refines fine-tuned models by selectively reverting parameter updates along principal directions of base weights.

The authors present an approach using local language models to automatically generate conceptual architectural models and reports for digital twins.

This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging.

The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling.

This paper applies recursive training and discrete deduction procedures to message-passing neural networks to solve constraint-reasoning tasks with few parameters.

The authors introduce a benchmark to evaluate how audio models update, retain, or reject recurring acoustic sources in long streams.

This technical report presents an updated summary-token attention architecture to optimize cache compression and computation across model layers.

This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character.

The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings.

This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations.

The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics.

This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors.

The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model.

This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models.

The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics.

This paper presents a training-free, gradient-based method to extract token-level reward signals directly from process reward models.

The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning.

This paper presents a theoretical rate-transfer method using stochastic calculus to establish finite-time convergence bounds for stochastic gradient algorithms.

The paper introduces a benchmark using fuzzing and transaction replays to evaluate the behavioral correctness of generated smart contracts.

The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution.

The paper introduces a benchmark and model for evaluating program correctness and generating diagnostic counterexamples for competitive programming code.

The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively.

The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups.

The paper presents a byte-level transformer model designed to detect malware directly from raw executable byte sequences.

The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback.

The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models.

The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios.

The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics.

The paper analyzes sample complexity rates for offline Q-learning algorithms using Kullback-Leibler regularization.

The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods.