All papers

The paper introduces a compact tabular foundation model combining distribution-aware representations with dataset-conditioned sparse mixture-of-experts computation.

The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives.

Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding.

Implements adaptive action grids in value-based reinforcement learning that selectively refine promising bins while pruning suboptimal actions.

Introduces a counterfactual risk management method to calibrate adaptive decision-making against shifting opponent behaviors within fixed deviation budgets.

Evaluates patient linkage and attribute inference risks present in brain magnetic resonance imaging representations generated by pretrained foundation models.

Derives token-level credit from single rollouts using hindsight future context rather than sampling multiple attempts per prompt.

Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution.

Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks.

Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts.

Develops and tests a lightweight convolutional neural network to classify potato leaf diseases on field-collected farm images.

Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models.

Uses teacher critiques on incorrect student rollouts to improve on-policy distillation and recover supervision during mathematical reasoning tasks.

Replaces discrete attention lookup in transformers with integration over a continuous memory curve to improve validation likelihood.

Presents a retraining-free method to adapt transformer sizes dynamically across diverse computational budgets using joint soft pruning of components.

Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments.

Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer.

This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency.

Designs an auction mechanism that models correlated valuations among advertisers competing in conversational assistant answers.

Presents a stochastic logic programming method to simulate human user behavior for testing decentralized grassroots apps.

Simulates Ethereum blob fee markets with multi-agent reinforcement learning to analyze how independent rollups interact.

Develops representation theorems for actual power structures in two-agent concurrent games using neighborhood frames.

Uses coupled echo state networks to study how interaction and feedback affect joint performance and efficiency.

Presents a distributed semantic exploration method for robot teams to balance map information gain with movement limits.

The paper investigates statistical contracts and hypothesis testing to allocate resources strategically among competing agents.

The paper presents a decentralized multi-agent reinforcement learning controller for cooperative aerial manipulation using multiple drones.

The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions.

The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data.

This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games.

The paper describes a system submission combining rules and language models for multilingual financial named-entity recognition and question answering.

The paper evaluates the cross-lingual transfer, calibration, and translation sensitivity of fast typed-decision models on regional Indonesian languages.

The paper studies opinion change, rhetorical strategies, and sycophancy in spoken multi-turn dialogues between humans and language models.

The paper proposes a linear score correction method to recover retrieval quality in compressed late-interaction document representations.

The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text.

The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations.

The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens.

The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers.

The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection.

The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models.

The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication.

The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models.

This paper audits Chinese language models for bias and error propagation across formal and ironic social registers.

This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed.

This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns.

This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents.

This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward.

This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs.

This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests.

This paper audits request independence in open Jev-style retrieval models across multi-passage query states.

This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images.

This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent.

This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing.

This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu.

This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation.

This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability.

This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction.

This paper evaluates social bias in Chinese language models across social media expressions, internet slang, and sarcastic packaging.

This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees.