All papers

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

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

The authors develop a reinforcement learning method to calibrate an agent's per-step confidence against the final outcome of an episode.

This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files.

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

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

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

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 progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models.

The authors introduce a benchmark to evaluate whether personal agents can distinguish accepted user arrangements from unacknowledged proposals.

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

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

The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs.

The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history.

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

The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks.

The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights.

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.

The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback.

The paper develops a framework explaining why large rollout batches in reinforcement learning reduce sample efficiency despite maintaining drift thresholds.

The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks.

The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

This paper introduces a dual-branch hybrid deep learning model combining spatial features and facial embeddings for deepfake detection.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

Summary pending (next check).

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

Argues conceptually that unsupervised machine learning serves four distinct goals rather than a single unifying objective.

Evaluates whether temporal illumination inconsistency priors improve face-swap deepfake detection performance across diverse video datasets.

Proposes a multimodal reasoning framework to avoid biased role attributions in underspecified or ambiguous social memes.

Presents a correlation-based multiplicative weight update method for training morphological neural network layers without standard gradient backpropagation.

Derives mathematical dimension thresholds required to compute Boolean gates using neural representations stored in superposition.

Proposes label-free test-time adaptation using spatial redundancy to improve respiration sensing on massive MIMO base stations against interference.

Reviews literature on adaptive combinatorial optimization combining machine learning policies with quantum computing workflows.

Presents a retrieval-augmented genomic language model scaling pretraining context length to capture long-range biological sequence interactions.

Analyzes optimal random quantisers for high-dimensional spherically symmetric distributions to reduce distortion with moderate sample sizes.

Develops an onboard inertial and throttle sensor detector with false-alarm controls to identify alcohol-impaired e-scooter riders during trips.

Analyzes the infinite-prompt limit of softmax attention on Gaussian mixtures to explain when attention behaves linearly or selectively.

Models self-improving systems with symbolic archives as dynamical processes, establishing theoretical optimization bounds and empirical stability.

Evaluates a pipeline combining speaker diarization and self-supervised speech representations to detect dysarthria during awake craniotomy procedures.

Establishes theoretical sample complexity bounds for posterior sampling in noisy one-bit compressed sensing under prior distribution mismatch.

Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation.

Develops a kernel Stein discrepancy framework using operator-valued kernels to assess conditional goodness-of-fit for unnormalized models.

Analyzes how latent-dimension scaling impacts dynamical-system learning and rollout error using Koopman operator spectral reliability metrics.

Establishes non-asymptotic statistical error bounds and optimization convergence for learning structured linear dynamical systems from highly subsampled observations.