All papers

The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature.

The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks.

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

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 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 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 investigates an agent framework that selectively queries expert judgments for borderline emotion-cause pairs to improve extraction accuracy.

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

This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading.

This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits.

This paper demonstrates that sentence-level relevance retrieval can match full conversation history accuracy in personalized language models using only five percent context.

This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions.

This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms.

This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines.

This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts.

The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents.

The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models.

The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference.

The paper introduces a benchmark evaluating how multi-agent teams develop communication protocols under asymmetric information and restricted communication channels.

The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora.

The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity.

The paper presents a repair method that re-encodes causal suffixes to enable safe non-prefix KV-cache reuse in retrieval-augmented generation.

The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits.

The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas.

The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions.

The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality.

The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities.

The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays.

The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression.

The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models.

The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities.

The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models.

The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility.

The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment.

The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts.

This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains.

This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states.

TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora.

This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi.

The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi.

RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience.

Heterogeneous Structured Pruning selects sublayer-specific sparsity patterns guided by latency and memory models for hardware-efficient inference.

The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs.

This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy.

The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications.

The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations.

RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps.

The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance.

This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data.

The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities.

PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference.

This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering.

This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving.