All papers

Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text.

Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute.

Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization.

Introduces a benchmark of 5,525 QA pairs from intelligence reports to evaluate RAG document- and chunk-level retrieval.

Describes an ontology-constrained ensemble system of encoders and fine-tuned language models for directed Arabic relation extraction.

Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics.

Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations.

Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels.

Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively.

Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs.

Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents.

Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding.

Introduces a preview-guided admission framework allowing search agents to selectively incorporate retrieved candidates into the ongoing reasoning context.

Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views.

Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population.

Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models.

Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models.

Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience.

Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference.

Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans.

Develops an open-source testbed to evaluate segment-wise key-value cache reuse beyond exact prompt prefixes during language model serving.

Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons.

Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection.

Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian.

Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables.

Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents.

Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning.

Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning.

Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings.

Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content.

Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data.

Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers.

Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models.

Studies host-level budget allocation and crawling policies to collect pretraining web data for language models.

Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving.

Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers.

Presents a framework that defers memory construction to test time to answer queries across long interaction histories.

Proposes category-conditioned expert routing biases to mitigate data imbalance in mixture-of-experts language models.

Analyzes rubric construction mechanisms and introduces a tree-search method to refine rubrics for evaluating language model outputs.

Presents a modular prompt optimization framework and benchmark targeting stance detection in text.

Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models.

Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls.

Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers.

Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators.

Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs.

Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric.

Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution.

The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations.

The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench.

The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference.

The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages.

The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback.

The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics.

The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions.

The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications.

The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation.

The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models.

The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues.

The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations.

The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality.