All papers

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

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.

Introduces Hybrid Scalar-Product Compression to optimize vector retrieval quantization by separating hot and cold vector data.

Presents AIMA, an autonomous multi-agent system using retrieval-augmented generation to manage Instagram content strategy, generation, and scheduling.

Presents an IEEE publication page stub regarding methods to maximize web service retrieval.

Surveys AI-based phishing detection methods, focusing on hybrid architectures that combine reinforcement learning with retrieval-augmented generation.

The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages.

The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction.

The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think.

The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates.

The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment.

The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it.

The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports.

The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks.

The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks.

The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context.

The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute.

The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks.

The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges.

The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent.

The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data.

Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective.

Introduces an instruction-following benchmark and training recipe to improve goal adherence in full-duplex conversational models.

Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining.

Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames.

Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows.

Designs an inference serving system optimized for dynamic, token-level routing across multiple language models.

Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide.

Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments.

Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference.

Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes.

Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation.

Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations.

Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models.

Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions.

Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards.

Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models.

Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams.

Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other.

Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding.

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.

Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories.

Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing.

Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation.

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

Generates synthetic multi-agent dialogues to benchmark conversational agent robustness against non-cooperative and adversarial user behaviors.

Introduces an evaluation benchmark measuring how effectively language models provide supportive responses during self-harm and crisis disclosures.

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.

Introduces a benchmark evaluating how misleading visualizations and charts affect the multi-step execution and decisions of web agents.

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.

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

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.