All papers

The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets.

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

The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions.

The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries.

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

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.

Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity.

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.

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

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.

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.

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.

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

Proposes a reinforcement learning framework that tracks external versus internal knowledge sources to reduce search agent fabrication.

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.

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.

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.

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.

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

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.